Gartner Data & Analytics Summit 2019 10 - 11 June / Mumbai, India

This session will provide a high-level introduction to data science and machine learning and their proper function in a data-driven organization. Content will include hype vs. reality, key trends, proven use cases and an overview of leading technologies. How do data science and machine learning fit within both the organization’s analytics and AI strategies? What are the early steps data and analytics leaders should take to invest in data science and machine learning? What do the first two years of a data science and machine learning initiative look like?

The proliferation of connected ecosystems, platforms and "things” is fast exceeding human capacity to optimally connect and orchestrate a vast set of operations critical for managing data. What challenges are creating a need for AI in data management? How will AI impact data management? How will AI augmentation enhance data management infrastructure?

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

If governance is the new black, AI governance is the new gray, with no black and white decisions. AI leaders are concerned about AI governance, while AI practitioners are blissfully unaware of it.
What are the specific AI governance concerns?
What are the early efforts in AI governance?
What are the common topics in EIM and AI governance and what is different?
What are the next practices for AI governance?

The democratization of ML and the popularity of open sources languages are facilitating the proliferation of ML models. However, as analytical assets are being deployed across organizations, the scalability of their performance and integrity (and their economic value) is in question. Organizations need to establish data science operational strategies to scale and systematically monetize their data science efforts.

Fidelity is a large financial services organization with USD2.7 trillion under management and USD7.4 trillion under administration. Fidelity’s Asset Management business unit is on a journey to use AI/ML solutions to help business make better decisions. In this case study, you will get insights into the five critical lessons that they have learned on the way to become an AI powered organization – 1) how to identify correct AI use cases, 2) need for an AI platform strategy, 3) technology choices for AI use cases including next generation data architecture, 4) need for a new software development lifecycle and 5) the culture changes required.

In creating a business case for artificial intelligence projects, data and analytics leaders should focus on harnessing AI techniques to link proposed solutions to specific business benefits. Here, we address seven factors that make AI business cases different, including:
- How to manage unrealistic expectations?
- How to account for the importance of data?
- How to overcome the dangers of lack of algorithmic transparency?
Please Note: Reserved for end-users only. Preregistration required.

This session will cover trends and best practices around hiring and training the entire skill mix necessary to build successful data science teams: Data scientists, data engineers, developers, machine learning specialists and domain experts. Training and upskilling have also become vital components of data science initiatives, as citizen data scientists become the leading source of new models and even expert data scientists struggle to keep up with the latest techniques and innovations.
- How many traditional data scientists do I need and what does their supporting cast look like?
- What are the best options for training and education in data science and machine learning?
- What are the organizational principles for placing data science teams?

Data Integration is foundational to any traditional or modern data and analytics initiative. Precisely harnessing data at each business moment compels enterprises to leverage diverse data types, integrator roles, and blending hybrid deployment, machine learning and AI approaches. Demands of data lake, data hub, semantic tiers and the logical data warehouse, among growing scenarios require flexible integration designs spanning batch, event-driven, virtualized, through distributed data delivery patterns. How do evolving information demands create data integration challenges? What are key trends in modernizing data integration? How can organizations pursue data integration as a strategic capability?

Faster and faster decisions are more and more critical using an increasing amount of data for business models that have become moving targets. The gap widening as we move forward, between the continuous availability of data & information and the decisions organizations have to execute upon. The suspended moment between data and action is where decisions live – where organizations combine AI techniques for maximum business differentiation and outcomes.

Augmented analytics is a disruptive trend that leverages machine automation and AI to transform how data is prepared, how insights are generated and shared and how data science and ML models are created and operationalized. This presentation provides an overview of the key trend and how you need to fully leverage it in your organization.

The growth of digital business and increased customer expectations are elevating the importance of real time, contextualized customer experiences for competitive advantage. This session will cover the emerging practice of 'always on' continuous intelligence in the customer engagement hub to prescribe the right action at the right time based on the most relevant business moment.

This session will provide attendees the platform to engage into deeper dives of machine learning: e.g., how to become more effective? What are the best platforms strategies? What new technologies and vendors are out there? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Organization’s confusion and the unbridled hype around AI techniques is preventing them from setting the right expectations regarding business outcomes. Technology leaders should start fruitful business conversations focused on real use cases vs. technology jargon. It is possible to mitigate the negative aspect of the hype surrounding AI while preserving the enthusiasm rightfully associated with its power, unleashing the power of AI techniques to deliver differentiating benefits.

Focus is going to be how the cutting edge is moving in AI and advanced analytics. We also explore some of the more exciting, yet not fully business-proven ideas in advanced analytics: From knowledge graphs, simulation, deep learning variations and many more.

Government of India is embracing analytics and AI for inclusive growth and better service delivery. This session highlights the transformative journey of how the government is using data analytics for governance, co-operative federalism in working with states and measuring various aspects of government programs to ensure they are on track. Dr Sarkar will also talk through the future roadmap of NITI Aayog in playing the critical role of a catalyst in supporting partnerships, providing access to infrastructure, fostering innovation through research and creating the demand by seeking solutions for addressing various governmental needs.

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

Blockchain's data provenance features and trusted interactions could change how data is controlled, shared and governed. Although big technological hurdles remain, innovative data management opportunities are emerging. It's time for data and analytics leaders to start experimenting. This session explores:
● How does blockchain compare to today's databases?
● How might blockchain disrupt your data management program?
● What is the maturity level of data management on blockchain, and where should you begin?

This session will provide a high-level introduction to data science and machine learning and their proper function in a data-driven organization. Content will include hype vs. reality, key trends, proven use cases and an overview of leading technologies. How do data science and machine learning fit within both the organization’s analytics and AI strategies? What are the early steps data and analytics leaders should take to invest in data science and machine learning? What do the first two years of a data science and machine learning initiative look like?

"What should my data and analytics strategy look like" is one of the most common inquiries Gartner's D&A research community gets. This is because the role of D&A is changing. From being a discipline of itself, to becoming a set of capabilities in support of the wider digital strategy or transformation. This presentation will share the best practices that have emerged from hundreds of document reviews and thousands of inquiries.

The technology for event stream processing is changing quickly. This interactive session will discuss attendee questions on stream analytics applications and tools, including open source and commercial products.

● How are stream analytics being used today?

● What are the technology trends?

● How do open source products, such as Spark Streaming, compare to commercial
products?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Successful information management requires a balanced attention to vision, strategy, metrics, organization and roles, governance, lifecycle and infrastructure. Gartner's EIM Maturity Model provides a framework for self-assessment and planning. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

The democratization of ML and the popularity of open sources languages are facilitating the proliferation of ML models. However, as analytical assets are being deployed across organizations, the scalability of their performance and integrity (and their economic value) is in question. Organizations need to establish data science operational strategies to scale and systematically monetize their data science efforts.

Continuous intelligence brings dramatic, measurable benefits in revenue generation, resource allocation, customer service and other metrics. More than ever, Data and Analytics leaders use real-time analytics to improve the performance of their businesses.
Why is continuous intelligence proliferating now?
What are best practices for deploying analytics into operations?

Monetizing information assets is critical to digital business success. This interactive discussion will explore methods that enable Data and Analytics Leader to deliver new business value by managing information supply chains and curating the inventory of information assets. How to facilitate, broker and mediate the underlying business value of the data? How to make data access and consumption more agile, flexible and relevant for both operational processes and to analytics? How to champion business narratives that leverage data and foster a data-driven culture? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data and analytics leaders have multiple options while working with external service providers. They can look for strategic services, implementation services or even managed services. Also the delivery model ranges from traditional labor based on-premise services to platform-based-consulting, packaged applications or even asset based consulting. In this interactive workshop we help you identify what you need support for, what different sourcing options exist and help you make the decision on build, buy or outsource. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Effective end-to-end data management has quietly become the last frontier of effective communication between IT and other parts of the business. DataOps brings much needed discipline, buy-in, and a repeatable process to maximize data business value:
1) What is DataOps?
2) Why do organizations need to explore this discipline?
3) Who and what is required to make DataOps a reality across data managers and data consumers?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

An exclusive workshop session designed with practical ideas and guidance to help CDOs lead data-driven culture change as data literacy advocates and program leads. Session will include key techniques that CDOs can use to create the case for change, and facilitate their own pilot data literacy workshops. Topics include: What is data literacy, and how does it help foster a data-driven culture? What is Information as a Second Language (ISL) and how does it relate to my organization’s needs? How do I get started with driving a data literacy program, and how does it relate to other organizational change/design efforts?

Data and analytics leaders are responsible for both information governance and data monetization. But as a relatively new function in many organizations, data and analytics teams (led by the CDO) are under the spotlight to produce high-quality results quickly and efficiently. This foundational session will cover: What new skills and roles are needed for data and analytics? How to organize for success? And how to avoid potential pitfalls?

This session will cover trends and best practices around hiring and training the entire skill mix necessary to build successful data science teams: Data scientists, data engineers, developers, machine learning specialists and domain experts. Training and upskilling have also become vital components of data science initiatives, as citizen data scientists become the leading source of new models and even expert data scientists struggle to keep up with the latest techniques and innovations.
- How many traditional data scientists do I need and what does their supporting cast look like?
- What are the best options for training and education in data science and machine learning?
- What are the organizational principles for placing data science teams?

Data Integration is foundational to any traditional or modern data and analytics initiative. Precisely harnessing data at each business moment compels enterprises to leverage diverse data types, integrator roles, and blending hybrid deployment, machine learning and AI approaches. Demands of data lake, data hub, semantic tiers and the logical data warehouse, among growing scenarios require flexible integration designs spanning batch, event-driven, virtualized, through distributed data delivery patterns. How do evolving information demands create data integration challenges? What are key trends in modernizing data integration? How can organizations pursue data integration as a strategic capability?

Faster and faster decisions are more and more critical using an increasing amount of data for business models that have become moving targets. The gap widening as we move forward, between the continuous availability of data & information and the decisions organizations have to execute upon. The suspended moment between data and action is where decisions live – where organizations combine AI techniques for maximum business differentiation and outcomes.

Data and Analytics leaders including CDO’s often lack the internal resources or skills needed to speed up and scale D&A and drive digital business value and they look for external support. Although the D&A services market is mature it is also facing disruption, changing the behavior of system integrators and consultancies. So what do DA leaders need to do, to find and choose from hundreds of possible service providers?

Key issues:
When to select an external service provider?
How to select an external service provider?
How to manage the relationship?

Data storytelling promises a more engaging means of communicating findings than BI reporting or data visualization alone. This trend is an extension of the now dominant self-service model of BI, combining data visualization with narrative techniques. What is a data story? When and how should data storytelling be used? What new skills and techniques are needed to create compelling data stories? This workshop includes best practices in visualizing data and mistakes to avoid. As part of the workshop, we will analyze examples you bring as a group, and recommend ways to more effectively communicate data. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Innovation has been the cornerstone of all human evolution. Numerous evidences have been documented about how early humans used inventive approaches to survive in unpredictable environments and made steady progression into culturally advanced ecosystems. Likewise, companies today are faced with unprecedented changes in their business environment owing to volume of data and overwhelming technology disruptions. Surviving and leading in this new business landscape requires companies to continuously re-think and re-design their business models in line with the right leadership (emotional intelligence) and ‘responsible AI’. Through this session, the speaker presents his thought leadership in leveraging organizational capability and cultivate the culture of innovation and collaboration to stay ahead.

This session will provide attendees the platform to engage into deeper dives of machine learning: e.g., how to become more effective? What are the best platforms strategies? What new technologies and vendors are out there? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Organization’s confusion and the unbridled hype around AI techniques is preventing them from setting the right expectations regarding business outcomes. Technology leaders should start fruitful business conversations focused on real use cases vs. technology jargon. It is possible to mitigate the negative aspect of the hype surrounding AI while preserving the enthusiasm rightfully associated with its power, unleashing the power of AI techniques to deliver differentiating benefits.

Government of India is embracing analytics and AI for inclusive growth and better service delivery. This session highlights the transformative journey of how the government is using data analytics for governance, co-operative federalism in working with states and measuring various aspects of government programs to ensure they are on track. Dr Sarkar will also talk through the future roadmap of NITI Aayog in playing the critical role of a catalyst in supporting partnerships, providing access to infrastructure, fostering innovation through research and creating the demand by seeking solutions for addressing various governmental needs.

While database applications hold the “what” of your business and even the rear-view mirror of the “who,” analyzing unstructured content exposes the “why” in regards to the drive of customers and employees, and exposes the "whom" is being impacted now and in the future.

The theme of the fourth CDO survey was business impact of the CDO. The following key challenges before CDOs were explored: Ability to quantify the value of organizational information (infonomics), level of embracing emergent and disruptive technologies (such as AI and Blockchain) and operational model (services only or combination of services and enablement). Learn how successful CDOs have addressed each of these challenges in the delivery of positive business impact.

"What should my data and analytics strategy look like" is one of the most common inquiries Gartner's D&A research community gets. This is because the role of D&A is changing. From being a discipline of itself, to becoming a set of capabilities in support of the wider digital strategy or transformation. This presentation will share the best practices that have emerged from hundreds of document reviews and thousands of inquiries.

Successful information management requires a balanced attention to vision, strategy, metrics, organization and roles, governance, lifecycle and infrastructure. Gartner's EIM Maturity Model provides a framework for self-assessment and planning. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data and analytics leaders and chief data officers need to create a business case for data quality improvements that connects directly with better business outcomes, and then put in place the mechanics to make it work. How do you justify a data quality improvement program? What roles and processes are required? What is the role of tools and technology? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The democratization of ML and the popularity of open sources languages are facilitating the proliferation of ML models. However, as analytical assets are being deployed across organizations, the scalability of their performance and integrity (and their economic value) is in question. Organizations need to establish data science operational strategies to scale and systematically monetize their data science efforts.

Continuous intelligence brings dramatic, measurable benefits in revenue generation, resource allocation, customer service and other metrics. More than ever, Data and Analytics leaders use real-time analytics to improve the performance of their businesses.
Why is continuous intelligence proliferating now?
What are best practices for deploying analytics into operations?

Traditional business models are often siloed, whether by design or by practice. Indeed, the entire business culture tends to be oriented toward executing the current process rather than analyzing it, understanding it and looking for opportunities to change and add value. This foundational session on data-driven culture will examine:
- How to encourage a more curious business culture?
- How to foster more creativity and tackle underlying business challenges with courage and sensitivity?
- How can data and analytics help to inject critical thinking into every aspect of the business?
- How can the CDO be the Chief Curiosity Officer?

Monetizing information assets is critical to digital business success. This interactive discussion will explore methods that enable Data and Analytics Leader to deliver new business value by managing information supply chains and curating the inventory of information assets. How to facilitate, broker and mediate the underlying business value of the data? How to make data access and consumption more agile, flexible and relevant for both operational processes and to analytics? How to champion business narratives that leverage data and foster a data-driven culture? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The number of CDOs and top data and analytics leaders continue to grow globally and Gartner’s annual CDO survey shows that strategy and transformation are top of their mind. New, emerging and “in-flight” CDOs have the opportunity to explore essentials like data storytelling, how to sustain critical executive partnerships and ways to drive value creation. The session which delivered by Gartner research analysts in these respective areas, will also highlight key findings from Gartner’s Fourth Annual CDO Survey.
*** Executive table discussion to follow.

Data and analytics leaders have multiple options while working with external service providers. They can look for strategic services, implementation services or even managed services. Also the delivery model ranges from traditional labor based on-premise services to platform-based-consulting, packaged applications or even asset based consulting. In this interactive workshop we help you identify what you need support for, what different sourcing options exist and help you make the decision on build, buy or outsource. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Many organizations fixate on their own wealth of information assets, but our research shows that the highest-value analytics solutions make use of alternative (external, exogenous) data. This session will expose you to some of the most interesting data sources and impressive use cases of open data, syndicated data, partner data, social media content, harvested web content and data marketplaces.

An exclusive workshop session designed with practical ideas and guidance to help CDOs lead data-driven culture change as data literacy advocates and program leads. Session will include key techniques that CDOs can use to create the case for change, and facilitate their own pilot data literacy workshops. Topics include: What is data literacy, and how does it help foster a data-driven culture? What is Information as a Second Language (ISL) and how does it relate to my organization’s needs? How do I get started with driving a data literacy program, and how does it relate to other organizational change/design efforts?

Data and analytics leaders are responsible for both information governance and data monetization. But as a relatively new function in many organizations, data and analytics teams (led by the CDO) are under the spotlight to produce high-quality results quickly and efficiently. This foundational session will cover: What new skills and roles are needed for data and analytics? How to organize for success? And how to avoid potential pitfalls?

In creating a business case for artificial intelligence projects, data and analytics leaders should focus on harnessing AI techniques to link proposed solutions to specific business benefits. Here, we address seven factors that make AI business cases different, including:
- How to manage unrealistic expectations?
- How to account for the importance of data?
- How to overcome the dangers of lack of algorithmic transparency?
Please Note: Reserved for end-users only. Preregistration required.

Have aspirations to take on the CDO role? How do you guide your career path and what types of skills do you need organization with? This session will help you navigate the how. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data and Analytics leaders including CDO’s often lack the internal resources or skills needed to speed up and scale D&A and drive digital business value and they look for external support. Although the D&A services market is mature it is also facing disruption, changing the behavior of system integrators and consultancies. So what do DA leaders need to do, to find and choose from hundreds of possible service providers?

Key issues:
When to select an external service provider?
How to select an external service provider?
How to manage the relationship?

Data and analytics are revolutionizing decision-making, enabling new revenue sources and changing the nature of work. The types of innovation are as diverse as the industries being transformed by them and operationalizing them is a critical skill. Internal and external data monetization, augmented analytics, data sciences, artificial intelligence, and other advances are driving new value propositions. Delivered by Gartner research analysts in these respective areas, this session will share real-world innovative ideas to inspire and guide your innovation efforts.
***Executive table discussion to follow.

Organizations' increasing need to connect things that share data — disparate data and analytics programs, MDM and master data stores, applications, processes, teams and external partners. But without a well-planned strategy based on requirements for mediation and governance, it's hard to enjoy a smooth flow of trusted data. What are data hubs and how do they support data sharing and governance? What are the most effective starting points for a data hub strategy? What are the best approaches to architect and deploy data hubs?

Everyone wants a data-driven culture, right? The promise of better decisions, faster, with more organizational and personal success is irresistible. Becoming data-driven requires culture change and individual change on the part of many people. But people resist change. In this workshop D&A leaders will be introduced to the new science of human behavior change and how to overcome change resistance. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?

The traditional data warehouse still serves as the basis for analytics programs and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data warehouse architecture has to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop. Advanced architectures like the logical data warehouse help make this a reality.

This session will address your questions about the current trends in data management. Topics will include rebalancing of collecting vs. connecting your data assets, use of cloud/PaaS, implementing data lakes and other architectures for big data and other trends in data management. Bring your questions and explore the future of data management. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Faster and faster decisions are more and more critical using an increasing amount of data for business models that have become moving targets. The gap widening as we move forward, between the continuous availability of data & information and the decisions organizations have to execute upon. The suspended moment between data and action is where decisions live – where organizations combine AI techniques for maximum business differentiation and outcomes.

While database applications hold the “what” of your business and even the rear-view mirror of the “who,” analyzing unstructured content exposes the “why” in regards to the drive of customers and employees, and exposes the "whom" is being impacted now and in the future.

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?

The traditional data warehouse still serves as the basis for analytics programs and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data warehouse architecture has to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop. Advanced architectures like the logical data warehouse help make this a reality.

Data and analytics leaders have multiple options while working with external service providers. They can look for strategic services, implementation services or even managed services. Also the delivery model ranges from traditional labor based on-premise services to platform-based-consulting, packaged applications or even asset based consulting. In this interactive workshop we help you identify what you need support for, what different sourcing options exist and help you make the decision on build, buy or outsource. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

The technology for event stream processing is changing quickly. This interactive session will discuss attendee questions on stream analytics applications and tools, including open source and commercial products.

● How are stream analytics being used today?

● What are the technology trends?

● How do open source products, such as Spark Streaming, compare to commercial
products?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

The traditional data warehouse still serves as the basis for analytics programs and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data warehouse architecture has to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop. Advanced architectures like the logical data warehouse help make this a reality.

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

Organizations everywhere struggle with data, and not just the mechanics like finding, cataloging and governing. Because of the disconnect between data producers and consumers, organizations struggle with the basics: how and why data was created, what it represents and what value it might provide. DataOps promises to resolve this disconnect, but there are huge challenges to implement this practice. In this session, you'll learn what DataOps is and how you can implement some early practices in your organization.

Data and analytics Leaders are often tasked with complementing traditional data integration technologies and practices with modern and more self-service ways of managing integration. Attend this session to uncover best practices to inculcate modern integration practices for complete and faster analytics and data science.

1. How can data and analytics leaders complement traditional integration like ETL/ESB with modern integration technologies like data virtualization?

2. What are the use cases that are enabled by modern integration technologies like data virtualization, data preparation, data engineering and stream data integration?

3. What are the vendor and market offerings and how to complement existing tools with newer technologies for your use case requirements?

PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Continuous intelligence brings dramatic, measurable benefits in revenue generation, resource allocation, customer service and other metrics. More than ever, Data and Analytics leaders use real-time analytics to improve the performance of their businesses.
Why is continuous intelligence proliferating now?
What are best practices for deploying analytics into operations?

Data and analytics leaders have multiple options while working with external service providers. They can look for strategic services, implementation services or even managed services. Also the delivery model ranges from traditional labor based on-premise services to platform-based-consulting, packaged applications or even asset based consulting. In this interactive workshop we help you identify what you need support for, what different sourcing options exist and help you make the decision on build, buy or outsource. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Fidelity is a large financial services organization with USD2.7 trillion under management and USD7.4 trillion under administration. Fidelity’s Asset Management business unit is on a journey to use AI/ML solutions to help business make better decisions. In this case study, you will get insights into the five critical lessons that they have learned on the way to become an AI powered organization – 1) how to identify correct AI use cases, 2) need for an AI platform strategy, 3) technology choices for AI use cases including next generation data architecture, 4) need for a new software development lifecycle and 5) the culture changes required.

Everyone wants a data lake, but most attempts fail to live up to hype around this concept. This session explores the multiple reasons why data lakes fail and how you can avoid the traps. Specifically, this session covers:
What are the major scenarios for data lake failures?
How can you detect and correct these scenarios?

Data and Analytics Leaders continue to struggle with inventorying and analyzing their distributed data assets leading to failed projects. Modern data catalogs are now a compulsory investment to make in order to maximize on investments in Analytics/BI and data management — including data lakes and help move data pipelines and integrations in data engineering, data Ops and data management in production.

1. What are Data Catalogs and how can they help Data and Analytics Leaders to find, catalog and inventory their heterogeneous data assets?
2. How to plan and implement data catalogs which assist with metadata management and governance and don't introduce metadata silos?
3. What are the Market Offerings in this space, their various segments and which offering would make sense according to your existing use case requirements?

Many organizations fixate on their own wealth of information assets, but our research shows that the highest-value analytics solutions make use of alternative (external, exogenous) data. This session will expose you to some of the most interesting data sources and impressive use cases of open data, syndicated data, partner data, social media content, harvested web content and data marketplaces.

The DMSA market is increasingly polarized. On one hand, there is tremendous hype about new data types, new technologies to store and manage them efficiently, and new roles and skills to use them effectively. On the other hand, there is a recognition that investment in foundational traditional technologies will be essential to serve as a platform for the next wave of innovation. Both technologies are required for a modern DMSA platform.

The use of machine learning to tackle tasks such as medical diagnosis, portfolio management or help desk automation are key industry interests. An area of much less coverage is the application of these technologies in the creation of a modern data management environment. This session will highlight how a major pharmaceutical company implemented a large scale, production class, big data & analytics platform in less than a year leveraging bots, machine learning and data pipelines. Learn how the technologies were applied to the data sources, ingestion and rationalization processes to accelerate the implementation of an analytics-ready data ecosystem.

Organizations' increasing need to connect things that share data — disparate data and analytics programs, MDM and master data stores, applications, processes, teams and external partners. But without a well-planned strategy based on requirements for mediation and governance, it's hard to enjoy a smooth flow of trusted data. What are data hubs and how do they support data sharing and governance? What are the most effective starting points for a data hub strategy? What are the best approaches to architect and deploy data hubs?

Blockchain's data provenance features and trusted interactions could change how data is controlled, shared and governed. Although big technological hurdles remain, innovative data management opportunities are emerging. It's time for data and analytics leaders to start experimenting. This session explores:
● How does blockchain compare to today's databases?
● How might blockchain disrupt your data management program?
● What is the maturity level of data management on blockchain, and where should you begin?

This session will address your questions about the current trends in data management. Topics will include rebalancing of collecting vs. connecting your data assets, use of cloud/PaaS, implementing data lakes and other architectures for big data and other trends in data management. Bring your questions and explore the future of data management. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

While database applications hold the “what” of your business and even the rear-view mirror of the “who,” analyzing unstructured content exposes the “why” in regards to the drive of customers and employees, and exposes the "whom" is being impacted now and in the future.

Many organizations have created a data lake but find it difficult to use and maintain it. In this Roundtable, we will discuss the issues around the data lake and what can be done to realize greater value from the data lake. The discussion will encompass: 1. What the issues seen with a data lake; 2. How to change the infrastructure to make the data lake less complex; and 3. How do we gain more business value from the data lake. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The cloud as a platform for databases is growing rapidly with many dbPaaS and cloud hosting options to choose from. Questions range from "Do I use the cloud for my database?" to "Which cloud platform is best?" to "What is the TCO of the cloud?" We will answer your questions about the cloud vendors, the dbPaaS products and the value of migrating to the cloud for data infrastructure. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Successful implementations of digital platforms remain elusive. Data and analytics sits at the core of the digital platform, but what strategy should you pursue? This session starts the discussion by presenting three competing and complementary options, and how they are used to supercharge your existing business or to pursue net-new products and business models. Specifically, this session will explore:


● What are the differences between hubs, lakes and warehouses?

● How do you balance the trade-offs between these options?

● What are the technology options and how are they integrated?

Analytics should be a key enabler of organizations' goals but that is seldom the case. Most initiatives tend to focus on tools deployment and user support with little connection to business objectives. In this session, we describe a step-by-step methodology to design a business-outcomes-driven analytics evolution roadmap with a strong commitment to users and a shift from technology to business impact.

Effective governance is a critical success factor for data and analytics initiatives, and one of the most difficult challenges that organizations face. This session explains how to establish solid foundations needed for successful data and analytics governance today, and the future direction of governance as it evolves to address new business and technology challenges.

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

"What should my data and analytics strategy look like" is one of the most common inquiries Gartner's D&A research community gets. This is because the role of D&A is changing. From being a discipline of itself, to becoming a set of capabilities in support of the wider digital strategy or transformation. This presentation will share the best practices that have emerged from hundreds of document reviews and thousands of inquiries.

Successful information management requires a balanced attention to vision, strategy, metrics, organization and roles, governance, lifecycle and infrastructure. Gartner's EIM Maturity Model provides a framework for self-assessment and planning. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

If governance is the new black, AI governance is the new gray, with no black and white decisions. AI leaders are concerned about AI governance, while AI practitioners are blissfully unaware of it.
What are the specific AI governance concerns?
What are the early efforts in AI governance?
What are the common topics in EIM and AI governance and what is different?
What are the next practices for AI governance?

Data and analytics leaders and chief data officers need to create a business case for data quality improvements that connects directly with better business outcomes, and then put in place the mechanics to make it work. How do you justify a data quality improvement program? What roles and processes are required? What is the role of tools and technology? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Organizations everywhere struggle with data, and not just the mechanics like finding, cataloging and governing. Because of the disconnect between data producers and consumers, organizations struggle with the basics: how and why data was created, what it represents and what value it might provide. DataOps promises to resolve this disconnect, but there are huge challenges to implement this practice. In this session, you'll learn what DataOps is and how you can implement some early practices in your organization.

The democratization of ML and the popularity of open sources languages are facilitating the proliferation of ML models. However, as analytical assets are being deployed across organizations, the scalability of their performance and integrity (and their economic value) is in question. Organizations need to establish data science operational strategies to scale and systematically monetize their data science efforts.

Monetizing information assets is critical to digital business success. This interactive discussion will explore methods that enable Data and Analytics Leader to deliver new business value by managing information supply chains and curating the inventory of information assets. How to facilitate, broker and mediate the underlying business value of the data? How to make data access and consumption more agile, flexible and relevant for both operational processes and to analytics? How to champion business narratives that leverage data and foster a data-driven culture? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Many organizations fixate on their own wealth of information assets, but our research shows that the highest-value analytics solutions make use of alternative (external, exogenous) data. This session will expose you to some of the most interesting data sources and impressive use cases of open data, syndicated data, partner data, social media content, harvested web content and data marketplaces.

Data Lakes are emerging as a critical data management component aimed at limiting traditional enterprise data silos and enabling agile access to all the data needed for faster decision making. This session will highlight how Unilever creates and leverages a trusted data lake design to help democratize access to enterprise-wide data, and how this trusted data lake forms the foundation for all future machine learning/ AI ambitions. This session will also share implementation best practices developed by Unilever on building and operationalizing trustworthy insights and ML models on a data lake.

Organizations' increasing need to connect things that share data — disparate data and analytics programs, MDM and master data stores, applications, processes, teams and external partners. But without a well-planned strategy based on requirements for mediation and governance, it's hard to enjoy a smooth flow of trusted data. What are data hubs and how do they support data sharing and governance? What are the most effective starting points for a data hub strategy? What are the best approaches to architect and deploy data hubs?

While database applications hold the “what” of your business and even the rear-view mirror of the “who,” analyzing unstructured content exposes the “why” in regards to the drive of customers and employees, and exposes the "whom" is being impacted now and in the future.

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

This presentation highlights the foundations and key trends in analytics and BI, and is a must-attend for all data and analytics leaders and business practitioners involved in analytics who are interested in starting or expanding their subject-matter expertise.

This session will provide a high-level introduction to data science and machine learning and their proper function in a data-driven organization. Content will include hype vs. reality, key trends, proven use cases and an overview of leading technologies. How do data science and machine learning fit within both the organization’s analytics and AI strategies? What are the early steps data and analytics leaders should take to invest in data science and machine learning? What do the first two years of a data science and machine learning initiative look like?

The technology for event stream processing is changing quickly. This interactive session will discuss attendee questions on stream analytics applications and tools, including open source and commercial products.

● How are stream analytics being used today?

● What are the technology trends?

● How do open source products, such as Spark Streaming, compare to commercial
products?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?

AI is not the only thing driving rapid change in data and analytics. From the next generation of augmented analytics tools to the use of continuous intelligence to interpret streams of data from IoT or the potential of conversational interfaces, there is rapid evolution in how and where analysis can be deployed. Understanding the business impact of these changes will allow organizations to prioritize the innovations that will drive digital business.

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

The traditional data warehouse still serves as the basis for analytics programs and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data warehouse architecture has to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop. Advanced architectures like the logical data warehouse help make this a reality.

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

Monetizing information assets is critical to digital business success. This interactive discussion will explore methods that enable Data and Analytics Leader to deliver new business value by managing information supply chains and curating the inventory of information assets. How to facilitate, broker and mediate the underlying business value of the data? How to make data access and consumption more agile, flexible and relevant for both operational processes and to analytics? How to champion business narratives that leverage data and foster a data-driven culture? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Finding, integrating, cataloging and curating data for Analytics, Data Science or further data integration (and data engineering) by business users is consistently rated by data and analytics leaders as one of the top 3 challenges in data management.

1. How can organizations incorporated augmented and standalone machine learning enabled data preparation tools for analytics/BI and data science use cases?

2. What are the market segments and popular offerings in the rapidly competitive and popular data preparation space and what should be your evaluation criteria to select the best offering.

3. How must you plan your data management and analytics architecture to ensure the right balance between self-service and IT oriented data preparation to avoid a governance chaos.

The democratization of ML and the popularity of open sources languages are facilitating the proliferation of ML models. However, as analytical assets are being deployed across organizations, the scalability of their performance and integrity (and their economic value) is in question. Organizations need to establish data science operational strategies to scale and systematically monetize their data science efforts.

Data and analytics leaders have multiple options while working with external service providers. They can look for strategic services, implementation services or even managed services. Also the delivery model ranges from traditional labor based on-premise services to platform-based-consulting, packaged applications or even asset based consulting. In this interactive workshop we help you identify what you need support for, what different sourcing options exist and help you make the decision on build, buy or outsource. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The modern subsegment of the analytics and BI market segment continues to expand much more rapidly than the overall market, showing an estimated 28% growth in 2017, which will decelerate to 17% by 2021. Customers are currently expanding their deployments for users and content, but downward pricing pressure and a certain saturation point will contribute to this deceleration.

Everyone wants a data lake, but most attempts fail to live up to hype around this concept. This session explores the multiple reasons why data lakes fail and how you can avoid the traps. Specifically, this session covers:
What are the major scenarios for data lake failures?
How can you detect and correct these scenarios?

Data and Analytics Leaders continue to struggle with inventorying and analyzing their distributed data assets leading to failed projects. Modern data catalogs are now a compulsory investment to make in order to maximize on investments in Analytics/BI and data management — including data lakes and help move data pipelines and integrations in data engineering, data Ops and data management in production.

1. What are Data Catalogs and how can they help Data and Analytics Leaders to find, catalog and inventory their heterogeneous data assets?
2. How to plan and implement data catalogs which assist with metadata management and governance and don't introduce metadata silos?
3. What are the Market Offerings in this space, their various segments and which offering would make sense according to your existing use case requirements?

The DMSA market is increasingly polarized. On one hand, there is tremendous hype about new data types, new technologies to store and manage them efficiently, and new roles and skills to use them effectively. On the other hand, there is a recognition that investment in foundational traditional technologies will be essential to serve as a platform for the next wave of innovation. Both technologies are required for a modern DMSA platform.

Augmented analytics innovations such as AutoML are already impacting the world of data science and machine learning. But that is just the tip of iceberg. Augmented analytics will transform the entire analytics workflow making it easier for expert and citizen data scientists to generate, operationalize and manage advanced analytics models. In this session, you will learn about the impact of augment analytics in data science.

Augmented analytics is a disruptive trend that leverages machine automation and AI to transform how data is prepared, how insights are generated and shared and how data science and ML models are created and operationalized. This presentation provides an overview of the key trend and how you need to fully leverage it in your organization.

In creating a business case for artificial intelligence projects, data and analytics leaders should focus on harnessing AI techniques to link proposed solutions to specific business benefits. Here, we address seven factors that make AI business cases different, including:
- How to manage unrealistic expectations?
- How to account for the importance of data?
- How to overcome the dangers of lack of algorithmic transparency?
Please Note: Reserved for end-users only. Preregistration required.

Faster and faster decisions are more and more critical using an increasing amount of data for business models that have become moving targets. The gap widening as we move forward, between the continuous availability of data & information and the decisions organizations have to execute upon. The suspended moment between data and action is where decisions live – where organizations combine AI techniques for maximum business differentiation and outcomes.

Organizations' increasing need to connect things that share data — disparate data and analytics programs, MDM and master data stores, applications, processes, teams and external partners. But without a well-planned strategy based on requirements for mediation and governance, it's hard to enjoy a smooth flow of trusted data. What are data hubs and how do they support data sharing and governance? What are the most effective starting points for a data hub strategy? What are the best approaches to architect and deploy data hubs?

Organization’s confusion and the unbridled hype around AI techniques is preventing them from setting the right expectations regarding business outcomes. Technology leaders should start fruitful business conversations focused on real use cases vs. technology jargon. It is possible to mitigate the negative aspect of the hype surrounding AI while preserving the enthusiasm rightfully associated with its power, unleashing the power of AI techniques to deliver differentiating benefits.

Data science and machine learning platforms are increasingly available for a broad spectrum of users. These range from operational workers, who make day-to-day decisions based on sophisticated models working behind the scenes, to citizen data scientists, who need data science and machine learning capabilities but have minimal skills in advanced data science, to highly skilled engineers and data scientists, who design experiments and deploy models, to represent and optimize business decisions.

While database applications hold the “what” of your business and even the rear-view mirror of the “who,” analyzing unstructured content exposes the “why” in regards to the drive of customers and employees, and exposes the "whom" is being impacted now and in the future.

Many organizations have created a data lake but find it difficult to use and maintain it. In this Roundtable, we will discuss the issues around the data lake and what can be done to realize greater value from the data lake. The discussion will encompass: 1. What the issues seen with a data lake; 2. How to change the infrastructure to make the data lake less complex; and 3. How do we gain more business value from the data lake. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The cloud as a platform for databases is growing rapidly with many dbPaaS and cloud hosting options to choose from. Questions range from "Do I use the cloud for my database?" to "Which cloud platform is best?" to "What is the TCO of the cloud?" We will answer your questions about the cloud vendors, the dbPaaS products and the value of migrating to the cloud for data infrastructure. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Successful implementations of digital platforms remain elusive. Data and analytics sits at the core of the digital platform, but what strategy should you pursue? This session starts the discussion by presenting three competing and complementary options, and how they are used to supercharge your existing business or to pursue net-new products and business models. Specifically, this session will explore:


● What are the differences between hubs, lakes and warehouses?

● How do you balance the trade-offs between these options?

● What are the technology options and how are they integrated?

Analytics should be a key enabler of organizations' goals but that is seldom the case. Most initiatives tend to focus on tools deployment and user support with little connection to business objectives. In this session, we describe a step-by-step methodology to design a business-outcomes-driven analytics evolution roadmap with a strong commitment to users and a shift from technology to business impact.

This session showcases top disruptive innovations with live demonstrations from representative vendors leading in these areas. Key issues addressed include: How can you use the Hype Cycle to track emerging trends? What are 4 innovations that will transform your business and what value do they provide? How do you prioritize your technology investments? Which are the vendors to watch? What challenges should you consider? This session will also include live demos from representative vendors in each innovation area.

The proliferation of connected ecosystems, platforms and "things” is fast exceeding human capacity to optimally connect and orchestrate a vast set of operations critical for managing data. What challenges are creating a need for AI in data management? How will AI impact data management? How will AI augmentation enhance data management infrastructure?

The technology for event stream processing is changing quickly. This interactive session will discuss attendee questions on stream analytics applications and tools, including open source and commercial products.

● How are stream analytics being used today?

● What are the technology trends?

● How do open source products, such as Spark Streaming, compare to commercial
products?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Organizations everywhere struggle with data, and not just the mechanics like finding, cataloging and governing. Because of the disconnect between data producers and consumers, organizations struggle with the basics: how and why data was created, what it represents and what value it might provide. DataOps promises to resolve this disconnect, but there are huge challenges to implement this practice. In this session, you'll learn what DataOps is and how you can implement some early practices in your organization.

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

Data and analytics Leaders are often tasked with complementing traditional data integration technologies and practices with modern and more self-service ways of managing integration. Attend this session to uncover best practices to inculcate modern integration practices for complete and faster analytics and data science.

1. How can data and analytics leaders complement traditional integration like ETL/ESB with modern integration technologies like data virtualization?

2. What are the use cases that are enabled by modern integration technologies like data virtualization, data preparation, data engineering and stream data integration?

3. What are the vendor and market offerings and how to complement existing tools with newer technologies for your use case requirements?

PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Finding, integrating, cataloging and curating data for Analytics, Data Science or further data integration (and data engineering) by business users is consistently rated by data and analytics leaders as one of the top 3 challenges in data management.

1. How can organizations incorporated augmented and standalone machine learning enabled data preparation tools for analytics/BI and data science use cases?

2. What are the market segments and popular offerings in the rapidly competitive and popular data preparation space and what should be your evaluation criteria to select the best offering.

3. How must you plan your data management and analytics architecture to ensure the right balance between self-service and IT oriented data preparation to avoid a governance chaos.

Data and Analytics Leaders continue to struggle with inventorying and analyzing their distributed data assets leading to failed projects. Modern data catalogs are now a compulsory investment to make in order to maximize on investments in Analytics/BI and data management — including data lakes and help move data pipelines and integrations in data engineering, data Ops and data management in production.

1. What are Data Catalogs and how can they help Data and Analytics Leaders to find, catalog and inventory their heterogeneous data assets?
2. How to plan and implement data catalogs which assist with metadata management and governance and don't introduce metadata silos?
3. What are the Market Offerings in this space, their various segments and which offering would make sense according to your existing use case requirements?

Many organizations fixate on their own wealth of information assets, but our research shows that the highest-value analytics solutions make use of alternative (external, exogenous) data. This session will expose you to some of the most interesting data sources and impressive use cases of open data, syndicated data, partner data, social media content, harvested web content and data marketplaces.

Effective end-to-end data management has quietly become the last frontier of effective communication between IT and other parts of the business. DataOps brings much needed discipline, buy-in, and a repeatable process to maximize data business value:
1) What is DataOps?
2) Why do organizations need to explore this discipline?
3) Who and what is required to make DataOps a reality across data managers and data consumers?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The use of machine learning to tackle tasks such as medical diagnosis, portfolio management or help desk automation are key industry interests. An area of much less coverage is the application of these technologies in the creation of a modern data management environment. This session will highlight how a major pharmaceutical company implemented a large scale, production class, big data & analytics platform in less than a year leveraging bots, machine learning and data pipelines. Learn how the technologies were applied to the data sources, ingestion and rationalization processes to accelerate the implementation of an analytics-ready data ecosystem.

Data Integration is foundational to any traditional or modern data and analytics initiative. Precisely harnessing data at each business moment compels enterprises to leverage diverse data types, integrator roles, and blending hybrid deployment, machine learning and AI approaches. Demands of data lake, data hub, semantic tiers and the logical data warehouse, among growing scenarios require flexible integration designs spanning batch, event-driven, virtualized, through distributed data delivery patterns. How do evolving information demands create data integration challenges? What are key trends in modernizing data integration? How can organizations pursue data integration as a strategic capability?

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?

The traditional data warehouse still serves as the basis for analytics programs and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data warehouse architecture has to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop. Advanced architectures like the logical data warehouse help make this a reality.

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

Data and analytics Leaders are often tasked with complementing traditional data integration technologies and practices with modern and more self-service ways of managing integration. Attend this session to uncover best practices to inculcate modern integration practices for complete and faster analytics and data science.

1. How can data and analytics leaders complement traditional integration like ETL/ESB with modern integration technologies like data virtualization?

2. What are the use cases that are enabled by modern integration technologies like data virtualization, data preparation, data engineering and stream data integration?

3. What are the vendor and market offerings and how to complement existing tools with newer technologies for your use case requirements?

PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data and Analytics Leaders continue to struggle with inventorying and analyzing their distributed data assets leading to failed projects. Modern data catalogs are now a compulsory investment to make in order to maximize on investments in Analytics/BI and data management — including data lakes and help move data pipelines and integrations in data engineering, data Ops and data management in production.

1. What are Data Catalogs and how can they help Data and Analytics Leaders to find, catalog and inventory their heterogeneous data assets?
2. How to plan and implement data catalogs which assist with metadata management and governance and don't introduce metadata silos?
3. What are the Market Offerings in this space, their various segments and which offering would make sense according to your existing use case requirements?

Everyone wants a data lake, but most attempts fail to live up to hype around this concept. This session explores the multiple reasons why data lakes fail and how you can avoid the traps. Specifically, this session covers:
What are the major scenarios for data lake failures?
How can you detect and correct these scenarios?

The DMSA market is increasingly polarized. On one hand, there is tremendous hype about new data types, new technologies to store and manage them efficiently, and new roles and skills to use them effectively. On the other hand, there is a recognition that investment in foundational traditional technologies will be essential to serve as a platform for the next wave of innovation. Both technologies are required for a modern DMSA platform.

Data Lakes are emerging as a critical data management component aimed at limiting traditional enterprise data silos and enabling agile access to all the data needed for faster decision making. This session will highlight how Unilever creates and leverages a trusted data lake design to help democratize access to enterprise-wide data, and how this trusted data lake forms the foundation for all future machine learning/ AI ambitions. This session will also share implementation best practices developed by Unilever on building and operationalizing trustworthy insights and ML models on a data lake.

This session will address your questions about the current trends in data management. Topics will include rebalancing of collecting vs. connecting your data assets, use of cloud/PaaS, implementing data lakes and other architectures for big data and other trends in data management. Bring your questions and explore the future of data management. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Many organizations have created a data lake but find it difficult to use and maintain it. In this Roundtable, we will discuss the issues around the data lake and what can be done to realize greater value from the data lake. The discussion will encompass: 1. What the issues seen with a data lake; 2. How to change the infrastructure to make the data lake less complex; and 3. How do we gain more business value from the data lake. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The cloud as a platform for databases is growing rapidly with many dbPaaS and cloud hosting options to choose from. Questions range from "Do I use the cloud for my database?" to "Which cloud platform is best?" to "What is the TCO of the cloud?" We will answer your questions about the cloud vendors, the dbPaaS products and the value of migrating to the cloud for data infrastructure. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

Successful information management requires a balanced attention to vision, strategy, metrics, organization and roles, governance, lifecycle and infrastructure. Gartner's EIM Maturity Model provides a framework for self-assessment and planning. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data and analytics leaders and chief data officers need to create a business case for data quality improvements that connects directly with better business outcomes, and then put in place the mechanics to make it work. How do you justify a data quality improvement program? What roles and processes are required? What is the role of tools and technology? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The use of machine learning to tackle tasks such as medical diagnosis, portfolio management or help desk automation are key industry interests. An area of much less coverage is the application of these technologies in the creation of a modern data management environment. This session will highlight how a major pharmaceutical company implemented a large scale, production class, big data & analytics platform in less than a year leveraging bots, machine learning and data pipelines. Learn how the technologies were applied to the data sources, ingestion and rationalization processes to accelerate the implementation of an analytics-ready data ecosystem.

Organizations' increasing need to connect things that share data — disparate data and analytics programs, MDM and master data stores, applications, processes, teams and external partners. But without a well-planned strategy based on requirements for mediation and governance, it's hard to enjoy a smooth flow of trusted data. What are data hubs and how do they support data sharing and governance? What are the most effective starting points for a data hub strategy? What are the best approaches to architect and deploy data hubs?

This session will provide a high-level introduction to data science and machine learning and their proper function in a data-driven organization. Content will include hype vs. reality, key trends, proven use cases and an overview of leading technologies. How do data science and machine learning fit within both the organization’s analytics and AI strategies? What are the early steps data and analytics leaders should take to invest in data science and machine learning? What do the first two years of a data science and machine learning initiative look like?

The technology for event stream processing is changing quickly. This interactive session will discuss attendee questions on stream analytics applications and tools, including open source and commercial products.

● How are stream analytics being used today?

● What are the technology trends?

● How do open source products, such as Spark Streaming, compare to commercial
products?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

If governance is the new black, AI governance is the new gray, with no black and white decisions. AI leaders are concerned about AI governance, while AI practitioners are blissfully unaware of it.
What are the specific AI governance concerns?
What are the early efforts in AI governance?
What are the common topics in EIM and AI governance and what is different?
What are the next practices for AI governance?

The democratization of ML and the popularity of open sources languages are facilitating the proliferation of ML models. However, as analytical assets are being deployed across organizations, the scalability of their performance and integrity (and their economic value) is in question. Organizations need to establish data science operational strategies to scale and systematically monetize their data science efforts.

Finding, integrating, cataloging and curating data for Analytics, Data Science or further data integration (and data engineering) by business users is consistently rated by data and analytics leaders as one of the top 3 challenges in data management.

1. How can organizations incorporated augmented and standalone machine learning enabled data preparation tools for analytics/BI and data science use cases?

2. What are the market segments and popular offerings in the rapidly competitive and popular data preparation space and what should be your evaluation criteria to select the best offering.

3. How must you plan your data management and analytics architecture to ensure the right balance between self-service and IT oriented data preparation to avoid a governance chaos.

The use of machine learning to tackle tasks such as medical diagnosis, portfolio management or help desk automation are key industry interests. An area of much less coverage is the application of these technologies in the creation of a modern data management environment. This session will highlight how a major pharmaceutical company implemented a large scale, production class, big data & analytics platform in less than a year leveraging bots, machine learning and data pipelines. Learn how the technologies were applied to the data sources, ingestion and rationalization processes to accelerate the implementation of an analytics-ready data ecosystem.

Augmented analytics innovations such as AutoML are already impacting the world of data science and machine learning. But that is just the tip of iceberg. Augmented analytics will transform the entire analytics workflow making it easier for expert and citizen data scientists to generate, operationalize and manage advanced analytics models. In this session, you will learn about the impact of augment analytics in data science.

Data Lakes are emerging as a critical data management component aimed at limiting traditional enterprise data silos and enabling agile access to all the data needed for faster decision making. This session will highlight how Unilever creates and leverages a trusted data lake design to help democratize access to enterprise-wide data, and how this trusted data lake forms the foundation for all future machine learning/ AI ambitions. This session will also share implementation best practices developed by Unilever on building and operationalizing trustworthy insights and ML models on a data lake.

This session will cover trends and best practices around hiring and training the entire skill mix necessary to build successful data science teams: Data scientists, data engineers, developers, machine learning specialists and domain experts. Training and upskilling have also become vital components of data science initiatives, as citizen data scientists become the leading source of new models and even expert data scientists struggle to keep up with the latest techniques and innovations.
- How many traditional data scientists do I need and what does their supporting cast look like?
- What are the best options for training and education in data science and machine learning?
- What are the organizational principles for placing data science teams?

Faster and faster decisions are more and more critical using an increasing amount of data for business models that have become moving targets. The gap widening as we move forward, between the continuous availability of data & information and the decisions organizations have to execute upon. The suspended moment between data and action is where decisions live – where organizations combine AI techniques for maximum business differentiation and outcomes.

Augmented analytics is a disruptive trend that leverages machine automation and AI to transform how data is prepared, how insights are generated and shared and how data science and ML models are created and operationalized. This presentation provides an overview of the key trend and how you need to fully leverage it in your organization.

The growth of digital business and increased customer expectations are elevating the importance of real time, contextualized customer experiences for competitive advantage. This session will cover the emerging practice of 'always on' continuous intelligence in the customer engagement hub to prescribe the right action at the right time based on the most relevant business moment.

This session will provide attendees the platform to engage into deeper dives of machine learning: e.g., how to become more effective? What are the best platforms strategies? What new technologies and vendors are out there? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Organization’s confusion and the unbridled hype around AI techniques is preventing them from setting the right expectations regarding business outcomes. Technology leaders should start fruitful business conversations focused on real use cases vs. technology jargon. It is possible to mitigate the negative aspect of the hype surrounding AI while preserving the enthusiasm rightfully associated with its power, unleashing the power of AI techniques to deliver differentiating benefits.

Data science and machine learning platforms are increasingly available for a broad spectrum of users. These range from operational workers, who make day-to-day decisions based on sophisticated models working behind the scenes, to citizen data scientists, who need data science and machine learning capabilities but have minimal skills in advanced data science, to highly skilled engineers and data scientists, who design experiments and deploy models, to represent and optimize business decisions.

Focus is going to be how the cutting edge is moving in AI and advanced analytics. We also explore some of the more exciting, yet not fully business-proven ideas in advanced analytics: From knowledge graphs, simulation, deep learning variations and many more.

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

The traditional data warehouse still serves as the basis for analytics programs and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data warehouse architecture has to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop. Advanced architectures like the logical data warehouse help make this a reality.

Data and analytics Leaders are often tasked with complementing traditional data integration technologies and practices with modern and more self-service ways of managing integration. Attend this session to uncover best practices to inculcate modern integration practices for complete and faster analytics and data science.

1. How can data and analytics leaders complement traditional integration like ETL/ESB with modern integration technologies like data virtualization?

2. What are the use cases that are enabled by modern integration technologies like data virtualization, data preparation, data engineering and stream data integration?

3. What are the vendor and market offerings and how to complement existing tools with newer technologies for your use case requirements?

PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The DMSA market is increasingly polarized. On one hand, there is tremendous hype about new data types, new technologies to store and manage them efficiently, and new roles and skills to use them effectively. On the other hand, there is a recognition that investment in foundational traditional technologies will be essential to serve as a platform for the next wave of innovation. Both technologies are required for a modern DMSA platform.

This session will address your questions about the current trends in data management. Topics will include rebalancing of collecting vs. connecting your data assets, use of cloud/PaaS, implementing data lakes and other architectures for big data and other trends in data management. Bring your questions and explore the future of data management. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Many organizations have created a data lake but find it difficult to use and maintain it. In this Roundtable, we will discuss the issues around the data lake and what can be done to realize greater value from the data lake. The discussion will encompass: 1. What the issues seen with a data lake; 2. How to change the infrastructure to make the data lake less complex; and 3. How do we gain more business value from the data lake. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The cloud as a platform for databases is growing rapidly with many dbPaaS and cloud hosting options to choose from. Questions range from "Do I use the cloud for my database?" to "Which cloud platform is best?" to "What is the TCO of the cloud?" We will answer your questions about the cloud vendors, the dbPaaS products and the value of migrating to the cloud for data infrastructure. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

This session will describe the foundational concepts for the discipline of master data management and the technology solutions that support it. What is master data and why is it important? What are the most common business benefits of successful MDM programs? What are the discipline and technology components of a successful MDM program?

The theme of the fourth CDO survey was business impact of the CDO. The following key challenges before CDOs were explored: Ability to quantify the value of organizational information (infonomics), level of embracing emergent and disruptive technologies (such as AI and Blockchain) and operational model (services only or combination of services and enablement). Learn how successful CDOs have addressed each of these challenges in the delivery of positive business impact.

The proliferation of connected ecosystems, platforms and "things” is fast exceeding human capacity to optimally connect and orchestrate a vast set of operations critical for managing data. What challenges are creating a need for AI in data management? How will AI impact data management? How will AI augmentation enhance data management infrastructure?

"What should my data and analytics strategy look like" is one of the most common inquiries Gartner's D&A research community gets. This is because the role of D&A is changing. From being a discipline of itself, to becoming a set of capabilities in support of the wider digital strategy or transformation. This presentation will share the best practices that have emerged from hundreds of document reviews and thousands of inquiries.

The technology for event stream processing is changing quickly. This interactive session will discuss attendee questions on stream analytics applications and tools, including open source and commercial products.

● How are stream analytics being used today?

● What are the technology trends?

● How do open source products, such as Spark Streaming, compare to commercial
products?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

Continuous intelligence brings dramatic, measurable benefits in revenue generation, resource allocation, customer service and other metrics. More than ever, Data and Analytics leaders use real-time analytics to improve the performance of their businesses.
Why is continuous intelligence proliferating now?
What are best practices for deploying analytics into operations?

Data Integration is foundational to any traditional or modern data and analytics initiative. Precisely harnessing data at each business moment compels enterprises to leverage diverse data types, integrator roles, and blending hybrid deployment, machine learning and AI approaches. Demands of data lake, data hub, semantic tiers and the logical data warehouse, among growing scenarios require flexible integration designs spanning batch, event-driven, virtualized, through distributed data delivery patterns. How do evolving information demands create data integration challenges? What are key trends in modernizing data integration? How can organizations pursue data integration as a strategic capability?

Faster and faster decisions are more and more critical using an increasing amount of data for business models that have become moving targets. The gap widening as we move forward, between the continuous availability of data & information and the decisions organizations have to execute upon. The suspended moment between data and action is where decisions live – where organizations combine AI techniques for maximum business differentiation and outcomes.

The growth of digital business and increased customer expectations are elevating the importance of real time, contextualized customer experiences for competitive advantage. This session will cover the emerging practice of 'always on' continuous intelligence in the customer engagement hub to prescribe the right action at the right time based on the most relevant business moment.

Customer analytics is one of the primary drivers of analytic adoption. The sheer diversity of potential opportunities to apply analysis to deliver great customer experiences and improve customer relationships can be daunting.
- What are the key areas of customer analytics?
- What are the best practices in customer analytics?
- What are the key trends in customer analytics to plan for?

Data and analytics leaders have multiple options while working with external service providers. They can look for strategic services, implementation services or even managed services. Also the delivery model ranges from traditional labor based on-premise services to platform-based-consulting, packaged applications or even asset based consulting. In this interactive workshop we help you identify what you need support for, what different sourcing options exist and help you make the decision on build, buy or outsource. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Analytic applications offer packaged analytics to solve specific industry vertical or domain problems and are important part of an overall analytics strategy. But how to pick the right one? Where to find analytic applications? What are the most important things to consider in evaluating different vendor options?

The technology for event stream processing is changing quickly. This interactive session will discuss attendee questions on stream analytics applications and tools, including open source and commercial products.

● How are stream analytics being used today?

● What are the technology trends?

● How do open source products, such as Spark Streaming, compare to commercial
products?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?

Continuous intelligence brings dramatic, measurable benefits in revenue generation, resource allocation, customer service and other metrics. More than ever, Data and Analytics leaders use real-time analytics to improve the performance of their businesses.
Why is continuous intelligence proliferating now?
What are best practices for deploying analytics into operations?

This session will provide a high-level introduction to data science and machine learning and their proper function in a data-driven organization. Content will include hype vs. reality, key trends, proven use cases and an overview of leading technologies. How do data science and machine learning fit within both the organization’s analytics and AI strategies? What are the early steps data and analytics leaders should take to invest in data science and machine learning? What do the first two years of a data science and machine learning initiative look like?

Data and analytics leaders serve in many forms. Whether you are the chief data officer, a department head focused on the numbers, or simply the go-to data “guru” in your workgroup — your organization is counting on you. So, find your purpose. Perhaps you want to put AI at the center of your business model, create a data literate workforce or ensure the ethical use of data. Regardless of the initiatives, you choose, you will lead them in a world of continuous change where it is not always easy to do the right thing. Are we building a data-driven organization or creating a tyranny of metrics? Are we optimizing business outcomes with analytical insights or violating personal privacy? This opening keynote to Gartner’s 2019 Data & Analytic Summit will show you how to lead with purpose in an ambiguous world.

The theme of the fourth CDO survey was business impact of the CDO. The following key challenges before CDOs were explored: Ability to quantify the value of organizational information (infonomics), level of embracing emergent and disruptive technologies (such as AI and Blockchain) and operational model (services only or combination of services and enablement). Learn how successful CDOs have addressed each of these challenges in the delivery of positive business impact.

"What should my data and analytics strategy look like" is one of the most common inquiries Gartner's D&A research community gets. This is because the role of D&A is changing. From being a discipline of itself, to becoming a set of capabilities in support of the wider digital strategy or transformation. This presentation will share the best practices that have emerged from hundreds of document reviews and thousands of inquiries.

Successful information management requires a balanced attention to vision, strategy, metrics, organization and roles, governance, lifecycle and infrastructure. Gartner's EIM Maturity Model provides a framework for self-assessment and planning. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Traditional business models are often siloed, whether by design or by practice. Indeed, the entire business culture tends to be oriented toward executing the current process rather than analyzing it, understanding it and looking for opportunities to change and add value. This foundational session on data-driven culture will examine:
- How to encourage a more curious business culture?
- How to foster more creativity and tackle underlying business challenges with courage and sensitivity?
- How can data and analytics help to inject critical thinking into every aspect of the business?
- How can the CDO be the Chief Curiosity Officer?

Monetizing information assets is critical to digital business success. This interactive discussion will explore methods that enable Data and Analytics Leader to deliver new business value by managing information supply chains and curating the inventory of information assets. How to facilitate, broker and mediate the underlying business value of the data? How to make data access and consumption more agile, flexible and relevant for both operational processes and to analytics? How to champion business narratives that leverage data and foster a data-driven culture? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The number of CDOs and top data and analytics leaders continue to grow globally and Gartner’s annual CDO survey shows that strategy and transformation are top of their mind. New, emerging and “in-flight” CDOs have the opportunity to explore essentials like data storytelling, how to sustain critical executive partnerships and ways to drive value creation. The session which delivered by Gartner research analysts in these respective areas, will also highlight key findings from Gartner’s Fourth Annual CDO Survey.
*** Executive table discussion to follow.

An exclusive workshop session designed with practical ideas and guidance to help CDOs lead data-driven culture change as data literacy advocates and program leads. Session will include key techniques that CDOs can use to create the case for change, and facilitate their own pilot data literacy workshops. Topics include: What is data literacy, and how does it help foster a data-driven culture? What is Information as a Second Language (ISL) and how does it relate to my organization’s needs? How do I get started with driving a data literacy program, and how does it relate to other organizational change/design efforts?

Data and analytics leaders are responsible for both information governance and data monetization. But as a relatively new function in many organizations, data and analytics teams (led by the CDO) are under the spotlight to produce high-quality results quickly and efficiently. This foundational session will cover: What new skills and roles are needed for data and analytics? How to organize for success? And how to avoid potential pitfalls?

In creating a business case for artificial intelligence projects, data and analytics leaders should focus on harnessing AI techniques to link proposed solutions to specific business benefits. Here, we address seven factors that make AI business cases different, including:
- How to manage unrealistic expectations?
- How to account for the importance of data?
- How to overcome the dangers of lack of algorithmic transparency?
Please Note: Reserved for end-users only. Preregistration required.

Data and Analytics leaders including CDO’s often lack the internal resources or skills needed to speed up and scale D&A and drive digital business value and they look for external support. Although the D&A services market is mature it is also facing disruption, changing the behavior of system integrators and consultancies. So what do DA leaders need to do, to find and choose from hundreds of possible service providers?

Key issues:
When to select an external service provider?
How to select an external service provider?
How to manage the relationship?

Data and analytics are revolutionizing decision-making, enabling new revenue sources and changing the nature of work. The types of innovation are as diverse as the industries being transformed by them and operationalizing them is a critical skill. Internal and external data monetization, augmented analytics, data sciences, artificial intelligence, and other advances are driving new value propositions. Delivered by Gartner research analysts in these respective areas, this session will share real-world innovative ideas to inspire and guide your innovation efforts.
***Executive table discussion to follow.

Innovation has been the cornerstone of all human evolution. Numerous evidences have been documented about how early humans used inventive approaches to survive in unpredictable environments and made steady progression into culturally advanced ecosystems. Likewise, companies today are faced with unprecedented changes in their business environment owing to volume of data and overwhelming technology disruptions. Surviving and leading in this new business landscape requires companies to continuously re-think and re-design their business models in line with the right leadership (emotional intelligence) and ‘responsible AI’. Through this session, the speaker presents his thought leadership in leveraging organizational capability and cultivate the culture of innovation and collaboration to stay ahead.

Organization’s confusion and the unbridled hype around AI techniques is preventing them from setting the right expectations regarding business outcomes. Technology leaders should start fruitful business conversations focused on real use cases vs. technology jargon. It is possible to mitigate the negative aspect of the hype surrounding AI while preserving the enthusiasm rightfully associated with its power, unleashing the power of AI techniques to deliver differentiating benefits.

Everyone wants a data-driven culture, right? The promise of better decisions, faster, with more organizational and personal success is irresistible. Becoming data-driven requires culture change and individual change on the part of many people. But people resist change. In this workshop D&A leaders will be introduced to the new science of human behavior change and how to overcome change resistance. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.

This session will describe the foundational concepts for the discipline of master data management and the technology solutions that support it. What is master data and why is it important? What are the most common business benefits of successful MDM programs? What are the discipline and technology components of a successful MDM program?

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

Data and analytics leaders and chief data officers need to create a business case for data quality improvements that connects directly with better business outcomes, and then put in place the mechanics to make it work. How do you justify a data quality improvement program? What roles and processes are required? What is the role of tools and technology? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Organizations' increasing need to connect things that share data — disparate data and analytics programs, MDM and master data stores, applications, processes, teams and external partners. But without a well-planned strategy based on requirements for mediation and governance, it's hard to enjoy a smooth flow of trusted data. What are data hubs and how do they support data sharing and governance? What are the most effective starting points for a data hub strategy? What are the best approaches to architect and deploy data hubs?

This session will provide a high-level introduction to data science and machine learning and their proper function in a data-driven organization. Content will include hype vs. reality, key trends, proven use cases and an overview of leading technologies. How do data science and machine learning fit within both the organization’s analytics and AI strategies? What are the early steps data and analytics leaders should take to invest in data science and machine learning? What do the first two years of a data science and machine learning initiative look like?

Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?

Customer analytics is one of the primary drivers of analytic adoption. The sheer diversity of potential opportunities to apply analysis to deliver great customer experiences and improve customer relationships can be daunting.
- What are the key areas of customer analytics?
- What are the best practices in customer analytics?
- What are the key trends in customer analytics to plan for?

The theme of the fourth CDO survey was business impact of the CDO. The following key challenges before CDOs were explored: Ability to quantify the value of organizational information (infonomics), level of embracing emergent and disruptive technologies (such as AI and Blockchain) and operational model (services only or combination of services and enablement). Learn how successful CDOs have addressed each of these challenges in the delivery of positive business impact.

AI is not the only thing driving rapid change in data and analytics. From the next generation of augmented analytics tools to the use of continuous intelligence to interpret streams of data from IoT or the potential of conversational interfaces, there is rapid evolution in how and where analysis can be deployed. Understanding the business impact of these changes will allow organizations to prioritize the innovations that will drive digital business.

"What should my data and analytics strategy look like" is one of the most common inquiries Gartner's D&A research community gets. This is because the role of D&A is changing. From being a discipline of itself, to becoming a set of capabilities in support of the wider digital strategy or transformation. This presentation will share the best practices that have emerged from hundreds of document reviews and thousands of inquiries.

Successful information management requires a balanced attention to vision, strategy, metrics, organization and roles, governance, lifecycle and infrastructure. Gartner's EIM Maturity Model provides a framework for self-assessment and planning. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.

The traditional data warehouse still serves as the basis for analytics programs and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data warehouse architecture has to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop. Advanced architectures like the logical data warehouse help make this a reality.

If governance is the new black, AI governance is the new gray, with no black and white decisions. AI leaders are concerned about AI governance, while AI practitioners are blissfully unaware of it.
What are the specific AI governance concerns?
What are the early efforts in AI governance?
What are the common topics in EIM and AI governance and what is different?
What are the next practices for AI governance?

Monetizing information assets is critical to digital business success. This interactive discussion will explore methods that enable Data and Analytics Leader to deliver new business value by managing information supply chains and curating the inventory of information assets. How to facilitate, broker and mediate the underlying business value of the data? How to make data access and consumption more agile, flexible and relevant for both operational processes and to analytics? How to champion business narratives that leverage data and foster a data-driven culture? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Finding, integrating, cataloging and curating data for Analytics, Data Science or further data integration (and data engineering) by business users is consistently rated by data and analytics leaders as one of the top 3 challenges in data management.

1. How can organizations incorporated augmented and standalone machine learning enabled data preparation tools for analytics/BI and data science use cases?

2. What are the market segments and popular offerings in the rapidly competitive and popular data preparation space and what should be your evaluation criteria to select the best offering.

3. How must you plan your data management and analytics architecture to ensure the right balance between self-service and IT oriented data preparation to avoid a governance chaos.

Continuous intelligence brings dramatic, measurable benefits in revenue generation, resource allocation, customer service and other metrics. More than ever, Data and Analytics leaders use real-time analytics to improve the performance of their businesses.
Why is continuous intelligence proliferating now?
What are best practices for deploying analytics into operations?

Traditional business models are often siloed, whether by design or by practice. Indeed, the entire business culture tends to be oriented toward executing the current process rather than analyzing it, understanding it and looking for opportunities to change and add value. This foundational session on data-driven culture will examine:
- How to encourage a more curious business culture?
- How to foster more creativity and tackle underlying business challenges with courage and sensitivity?
- How can data and analytics help to inject critical thinking into every aspect of the business?
- How can the CDO be the Chief Curiosity Officer?

Data and analytics leaders have multiple options while working with external service providers. They can look for strategic services, implementation services or even managed services. Also the delivery model ranges from traditional labor based on-premise services to platform-based-consulting, packaged applications or even asset based consulting. In this interactive workshop we help you identify what you need support for, what different sourcing options exist and help you make the decision on build, buy or outsource. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Fidelity is a large financial services organization with USD2.7 trillion under management and USD7.4 trillion under administration. Fidelity’s Asset Management business unit is on a journey to use AI/ML solutions to help business make better decisions. In this case study, you will get insights into the five critical lessons that they have learned on the way to become an AI powered organization – 1) how to identify correct AI use cases, 2) need for an AI platform strategy, 3) technology choices for AI use cases including next generation data architecture, 4) need for a new software development lifecycle and 5) the culture changes required.

Data and Analytics Leaders continue to struggle with inventorying and analyzing their distributed data assets leading to failed projects. Modern data catalogs are now a compulsory investment to make in order to maximize on investments in Analytics/BI and data management — including data lakes and help move data pipelines and integrations in data engineering, data Ops and data management in production.

1. What are Data Catalogs and how can they help Data and Analytics Leaders to find, catalog and inventory their heterogeneous data assets?
2. How to plan and implement data catalogs which assist with metadata management and governance and don't introduce metadata silos?
3. What are the Market Offerings in this space, their various segments and which offering would make sense according to your existing use case requirements?

Many organizations fixate on their own wealth of information assets, but our research shows that the highest-value analytics solutions make use of alternative (external, exogenous) data. This session will expose you to some of the most interesting data sources and impressive use cases of open data, syndicated data, partner data, social media content, harvested web content and data marketplaces.

An exclusive workshop session designed with practical ideas and guidance to help CDOs lead data-driven culture change as data literacy advocates and program leads. Session will include key techniques that CDOs can use to create the case for change, and facilitate their own pilot data literacy workshops. Topics include: What is data literacy, and how does it help foster a data-driven culture? What is Information as a Second Language (ISL) and how does it relate to my organization’s needs? How do I get started with driving a data literacy program, and how does it relate to other organizational change/design efforts?

The DMSA market is increasingly polarized. On one hand, there is tremendous hype about new data types, new technologies to store and manage them efficiently, and new roles and skills to use them effectively. On the other hand, there is a recognition that investment in foundational traditional technologies will be essential to serve as a platform for the next wave of innovation. Both technologies are required for a modern DMSA platform.

Analytic applications offer packaged analytics to solve specific industry vertical or domain problems and are important part of an overall analytics strategy. But how to pick the right one? Where to find analytic applications? What are the most important things to consider in evaluating different vendor options?

Data and Analytics leaders including CDO’s often lack the internal resources or skills needed to speed up and scale D&A and drive digital business value and they look for external support. Although the D&A services market is mature it is also facing disruption, changing the behavior of system integrators and consultancies. So what do DA leaders need to do, to find and choose from hundreds of possible service providers?

Key issues:
When to select an external service provider?
How to select an external service provider?
How to manage the relationship?

In creating a business case for artificial intelligence projects, data and analytics leaders should focus on harnessing AI techniques to link proposed solutions to specific business benefits. Here, we address seven factors that make AI business cases different, including:
- How to manage unrealistic expectations?
- How to account for the importance of data?
- How to overcome the dangers of lack of algorithmic transparency?
Please Note: Reserved for end-users only. Preregistration required.

Faster and faster decisions are more and more critical using an increasing amount of data for business models that have become moving targets. The gap widening as we move forward, between the continuous availability of data & information and the decisions organizations have to execute upon. The suspended moment between data and action is where decisions live – where organizations combine AI techniques for maximum business differentiation and outcomes.

Organizations' increasing need to connect things that share data — disparate data and analytics programs, MDM and master data stores, applications, processes, teams and external partners. But without a well-planned strategy based on requirements for mediation and governance, it's hard to enjoy a smooth flow of trusted data. What are data hubs and how do they support data sharing and governance? What are the most effective starting points for a data hub strategy? What are the best approaches to architect and deploy data hubs?

This session will address your questions about the current trends in data management. Topics will include rebalancing of collecting vs. connecting your data assets, use of cloud/PaaS, implementing data lakes and other architectures for big data and other trends in data management. Bring your questions and explore the future of data management. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Government of India is embracing analytics and AI for inclusive growth and better service delivery. This session highlights the transformative journey of how the government is using data analytics for governance, co-operative federalism in working with states and measuring various aspects of government programs to ensure they are on track. Dr Sarkar will also talk through the future roadmap of NITI Aayog in playing the critical role of a catalyst in supporting partnerships, providing access to infrastructure, fostering innovation through research and creating the demand by seeking solutions for addressing various governmental needs.

While database applications hold the “what” of your business and even the rear-view mirror of the “who,” analyzing unstructured content exposes the “why” in regards to the drive of customers and employees, and exposes the "whom" is being impacted now and in the future.

Many organizations have created a data lake but find it difficult to use and maintain it. In this Roundtable, we will discuss the issues around the data lake and what can be done to realize greater value from the data lake. The discussion will encompass: 1. What the issues seen with a data lake; 2. How to change the infrastructure to make the data lake less complex; and 3. How do we gain more business value from the data lake. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

The cloud as a platform for databases is growing rapidly with many dbPaaS and cloud hosting options to choose from. Questions range from "Do I use the cloud for my database?" to "Which cloud platform is best?" to "What is the TCO of the cloud?" We will answer your questions about the cloud vendors, the dbPaaS products and the value of migrating to the cloud for data infrastructure. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Analytics should be a key enabler of organizations' goals but that is seldom the case. Most initiatives tend to focus on tools deployment and user support with little connection to business objectives. In this session, we describe a step-by-step methodology to design a business-outcomes-driven analytics evolution roadmap with a strong commitment to users and a shift from technology to business impact.

This session showcases top disruptive innovations with live demonstrations from representative vendors leading in these areas. Key issues addressed include: How can you use the Hype Cycle to track emerging trends? What are 4 innovations that will transform your business and what value do they provide? How do you prioritize your technology investments? Which are the vendors to watch? What challenges should you consider? This session will also include live demos from representative vendors in each innovation area.

Find out why information isn’t “the new oil,” it's much more valuable. This session will provide an overview of Gartner’s research on how leading organizations are generating economic benefits from information; how to apply asset management principles and practices for improved information governance, quality and availability; and how to use various information metrics to drive IT, business and organizational strategies.

"What should my data and analytics strategy look like" is one of the most common inquiries Gartner's D&A research community gets. This is because the role of D&A is changing. From being a discipline of itself, to becoming a set of capabilities in support of the wider digital strategy or transformation. This presentation will share the best practices that have emerged from hundreds of document reviews and thousands of inquiries.

Successful information management requires a balanced attention to vision, strategy, metrics, organization and roles, governance, lifecycle and infrastructure. Gartner's EIM Maturity Model provides a framework for self-assessment and planning. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Data and analytics leaders and chief data officers need to create a business case for data quality improvements that connects directly with better business outcomes, and then put in place the mechanics to make it work. How do you justify a data quality improvement program? What roles and processes are required? What is the role of tools and technology? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Organizations everywhere struggle with data, and not just the mechanics like finding, cataloging and governing. Because of the disconnect between data producers and consumers, organizations struggle with the basics: how and why data was created, what it represents and what value it might provide. DataOps promises to resolve this disconnect, but there are huge challenges to implement this practice. In this session, you'll learn what DataOps is and how you can implement some early practices in your organization.

The democratization of ML and the popularity of open sources languages are facilitating the proliferation of ML models. However, as analytical assets are being deployed across organizations, the scalability of their performance and integrity (and their economic value) is in question. Organizations need to establish data science operational strategies to scale and systematically monetize their data science efforts.

Traditional business models are often siloed, whether by design or by practice. Indeed, the entire business culture tends to be oriented toward executing the current process rather than analyzing it, understanding it and looking for opportunities to change and add value. This foundational session on data-driven culture will examine:
- How to encourage a more curious business culture?
- How to foster more creativity and tackle underlying business challenges with courage and sensitivity?
- How can data and analytics help to inject critical thinking into every aspect of the business?
- How can the CDO be the Chief Curiosity Officer?

Effective end-to-end data management has quietly become the last frontier of effective communication between IT and other parts of the business. DataOps brings much needed discipline, buy-in, and a repeatable process to maximize data business value:
1) What is DataOps?
2) Why do organizations need to explore this discipline?
3) Who and what is required to make DataOps a reality across data managers and data consumers?
PLEASE NOTE: Reserved for end-users only. Pre-registration required.

An exclusive workshop session designed with practical ideas and guidance to help CDOs lead data-driven culture change as data literacy advocates and program leads. Session will include key techniques that CDOs can use to create the case for change, and facilitate their own pilot data literacy workshops. Topics include: What is data literacy, and how does it help foster a data-driven culture? What is Information as a Second Language (ISL) and how does it relate to my organization’s needs? How do I get started with driving a data literacy program, and how does it relate to other organizational change/design efforts?

Data and analytics leaders are responsible for both information governance and data monetization. But as a relatively new function in many organizations, data and analytics teams (led by the CDO) are under the spotlight to produce high-quality results quickly and efficiently. This foundational session will cover: What new skills and roles are needed for data and analytics? How to organize for success? And how to avoid potential pitfalls?

This session will cover trends and best practices around hiring and training the entire skill mix necessary to build successful data science teams: Data scientists, data engineers, developers, machine learning specialists and domain experts. Training and upskilling have also become vital components of data science initiatives, as citizen data scientists become the leading source of new models and even expert data scientists struggle to keep up with the latest techniques and innovations.
- How many traditional data scientists do I need and what does their supporting cast look like?
- What are the best options for training and education in data science and machine learning?
- What are the organizational principles for placing data science teams?

Data and Analytics leaders including CDO’s often lack the internal resources or skills needed to speed up and scale D&A and drive digital business value and they look for external support. Although the D&A services market is mature it is also facing disruption, changing the behavior of system integrators and consultancies. So what do DA leaders need to do, to find and choose from hundreds of possible service providers?

Key issues:
When to select an external service provider?
How to select an external service provider?
How to manage the relationship?

Innovation has been the cornerstone of all human evolution. Numerous evidences have been documented about how early humans used inventive approaches to survive in unpredictable environments and made steady progression into culturally advanced ecosystems. Likewise, companies today are faced with unprecedented changes in their business environment owing to volume of data and overwhelming technology disruptions. Surviving and leading in this new business landscape requires companies to continuously re-think and re-design their business models in line with the right leadership (emotional intelligence) and ‘responsible AI’. Through this session, the speaker presents his thought leadership in leveraging organizational capability and cultivate the culture of innovation and collaboration to stay ahead.

Find out why information isn’t “the new oil,” it's much more valuable. This session will provide an overview of Gartner’s research on how leading organizations are generating economic benefits from information; how to apply asset management principles and practices for improved information governance, quality and availability; and how to use various information metrics to drive IT, business and organizational strategies.

This presentation highlights the foundations and key trends in analytics and BI, and is a must-attend for all data and analytics leaders and business practitioners involved in analytics who are interested in starting or expanding their subject-matter expertise.

Data and analytics Leaders are often tasked with complementing traditional data integration technologies and practices with modern and more self-service ways of managing integration. Attend this session to uncover best practices to inculcate modern integration practices for complete and faster analytics and data science.

1. How can data and analytics leaders complement traditional integration like ETL/ESB with modern integration technologies like data virtualization?

2. What are the use cases that are enabled by modern integration technologies like data virtualization, data preparation, data engineering and stream data integration?

3. What are the vendor and market offerings and how to complement existing tools with newer technologies for your use case requirements?

PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Finding, integrating, cataloging and curating data for Analytics, Data Science or further data integration (and data engineering) by business users is consistently rated by data and analytics leaders as one of the top 3 challenges in data management.

1. How can organizations incorporated augmented and standalone machine learning enabled data preparation tools for analytics/BI and data science use cases?

2. What are the market segments and popular offerings in the rapidly competitive and popular data preparation space and what should be your evaluation criteria to select the best offering.

3. How must you plan your data management and analytics architecture to ensure the right balance between self-service and IT oriented data preparation to avoid a governance chaos.

The modern subsegment of the analytics and BI market segment continues to expand much more rapidly than the overall market, showing an estimated 28% growth in 2017, which will decelerate to 17% by 2021. Customers are currently expanding their deployments for users and content, but downward pricing pressure and a certain saturation point will contribute to this deceleration.

Augmented analytics is a disruptive trend that leverages machine automation and AI to transform how data is prepared, how insights are generated and shared and how data science and ML models are created and operationalized. This presentation provides an overview of the key trend and how you need to fully leverage it in your organization.

Data storytelling promises a more engaging means of communicating findings than BI reporting or data visualization alone. This trend is an extension of the now dominant self-service model of BI, combining data visualization with narrative techniques. What is a data story? When and how should data storytelling be used? What new skills and techniques are needed to create compelling data stories? This workshop includes best practices in visualizing data and mistakes to avoid. As part of the workshop, we will analyze examples you bring as a group, and recommend ways to more effectively communicate data. PLEASE NOTE: Reserved for end-users only. Pre-registration required.

This presentation highlights the foundations and key trends in analytics and BI, and is a must-attend for all data and analytics leaders and business practitioners involved in analytics who are interested in starting or expanding their subject-matter expertise.

Monetizing information assets is critical to digital business success. This interactive discussion will explore methods that enable Data and Analytics Leader to deliver new business value by managing information supply chains and curating the inventory of information assets. How to facilitate, broker and mediate the underlying business value of the data? How to make data access and consumption more agile, flexible and relevant for both operational processes and to analytics? How to champion business narratives that leverage data and foster a data-driven culture? PLEASE NOTE: Reserved for end-users only. Pre-registration required.

Finding, integrating, cataloging and curating data for Analytics, Data Science or further data integration (and data engineering) by business users is consistently rated by data and analytics leaders as one of the top 3 challenges in data management.

1. How can organizations incorporated augmented and standalone machine learning enabled data preparation tools for analytics/BI and data science use cases?

2. What are the market segments and popular offerings in the rapidly competitive and popular data preparation space and what should be your evaluation criteria to select the best offering.

3. How must you plan your data management and analytics architecture to ensure the right balance between self-service and IT oriented data preparation to avoid a governance chaos.

The modern subsegment of the analytics and BI market segment continues to expand much more rapidly than the overall market, showing an estimated 28% growth in 2017, which will decelerate to 17% by 2021. Customers are currently expanding their deployments for users and content, but downward pricing pressure and a certain saturation point will contribute to this deceleration.

Data and Analytics Leaders continue to struggle with inventorying and analyzing their distributed data assets leading to failed projects. Modern data catalogs are now a compulsory investment to make in order to maximize on investments in Analytics/BI and data management — including data lakes and help move data pipelines and integrations in data engineering, data Ops and data management in production.

1. What are Data Catalogs and how can they help Data and Analytics Leaders to find, catalog and inventory their heterogeneous data assets?
2. How to plan and implement data catalogs which assist with metadata management and governance and don't introduce metadata silos?
3. What are the Market Offerings in this space, their various segments and which offering would make sense according to your existing use case requirements?

Analytic applications offer packaged analytics to solve specific industry vertical or domain problems and are important part of an overall analytics strategy. But how to pick the right one? Where to find analytic applications? What are the most important things to consider in evaluating different vendor options?

In creating a business case for artificial intelligence projects, data and analytics leaders should focus on harnessing AI techniques to link proposed solutions to specific business benefits. Here, we address seven factors that make AI business cases different, including:
- How to manage unrealistic expectations?
- How to account for the importance of data?
- How to overcome the dangers of lack of algorithmic transparency?
Please Note: Reserved for end-users only. Preregistration required.