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.
This Magic Quadrant covers external service providers that can support organizations for their data and analytics initiative.
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.
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 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?
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.
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.
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.
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.
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.
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?
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?
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?
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.
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?
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?
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.
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?
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 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?
When to select an external service provider?
How to select an external service provider?
How to manage the relationship?
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.
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 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?
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.
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.
As machine and deep learning models are critical parts of more and more business decisions, knowing how those models derive their insights is becoming necessary. From potentially harmful biases to legal and regulatory compliance, the anatomy of business decisions is vital to generate trust in the process before those models apply decisions at scale. A few best practices can help organizations in opening those black boxes.
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?
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.
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.
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?
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.
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?
This session will describe the foundational concepts for the discipline of master data management and the technology solutions that support it. What are the current states of the MDM discipline and its associated software market? What are the latest implementation trends and market developments? How can you prepare to effectively take advantage of these developments?
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?
Quality insights required by digital business depend on trusted, high-quality data. However, data quality is often treated as after-thought inconvenience. What are major challenges for data quality practices? What are the top trends of the modern data quality practices and tools? How to setup appropriate data quality programs to meet various business scenarios?
Master data management is a critical success factor in constructing optimal customer experiences. Learn the benefits of aligning the MDM discipline to CX and making it a part of your CX strategies. Why is MDM critical to the customer experience? How will MDM increase and optimize your 360-degree view and your CX capabilities? What new opportunities for managing customer information does MDM bring?
Building transparency in uses of data has come to the forefront with GDPR and data governance pressure. This has created increased interest in metadata management and data catalogs. Yet Data and Analytics leaders struggle to get the momentum needed to start and develop the metadata management practice.
1. How can metadata management deliver value?
2. How to set up a metadata management practice?
3. What technologies can help support the metadata management practice?