Gartner Data & Analytics Summit 2019 18 - 19 February / Sydney, Australia

Registration hours for the day.

Increased transparency is a major force driving open-data initiatives but many government organizations struggle to show how data also increases accountability. This session will provide government data and analytics leaders with the techniques needed to establish a sustainable performance management program that tracks progress on organizational priorities and goals.

Sharing data information among government organizations and its partners remains an obstacle to transforming public services and improving outcomes. This session offers guidance to develop successful and sustainable data sharing practices in government so that data and analytics leaders can increase data sharing in their jurisdictions.

Join us for refreshments on arrival.

Registration hours for the day.

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?

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?

“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 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?

To be effective at implementing a modern data and analytics strategy, data and analytics leaders need to deploy an operating model to help drive effectiveness and efficiency through all their programs and projects. This latest research provides that operating models and gives you best practices for exploiting it and delivering business value. • What is an effective data and analytics operating model? • How to translate data and analytics vision and strategy to practical objectives? • How to operationalize your operating model with programs and projects?

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 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.

Join us for refreshments in a brief break between sessions.

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?

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.

A new wave of Fintech solutions is emerging with innovative offerings targeting both banks and their customers. Many successful "fintech" companies rely on innovative usage of analytics as a core aspect of their offerings. Banks can benefit from such innovation to enhance their products and services. In this session, we will cover: What is the current state of Fintech? How is analytics used in Fintech companies? How should financial institutions use analytics in their Fintech initiatives?

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 organizations plan and prepare for, and make the changes.

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?

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 intuitive 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.

Behavioral psychologist Dan Ariely said about applicability of AI, “Rather than trying to work against human nature I think we should be working with human nature." Use a metamodel of AI through the eyes of people, their expectations and hopes to fix past problems, address current needs and fulfill future aspirations. Most importantly, this model will help to use the opposites of AI to surface how to truly take advantage of AI.

Quality insights required by digital business depend on trusted, high-quality data. However, data quality is often treated as after-thought inconvenience. What are the 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?

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 trends and how you need to fully leverage it in your organization.

This new research explains the next phase in how data and analytics will drive every business moment and outcome improvement. The emerging data and analytics platform will be defined and demystified, and a clear roadmap for how to assemble it in your organization will be shared. Don’t panic and buy some AI yet. Start here first. • How does understanding decision making add value to your digital business platform? • What is a data and analytics platform and how does it support digital business? • How and where can you start leveraging your data and analytics today?

Successful implementations of digital platforms remain elusive. Data and analytics sit 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?

Join attendees for lunch and take advantage of the opportunity to network and catch up on the day's activities.

Businesses of all sizes and many industries continue to struggle to maintain a consistent, shareable and accurate single version of master data across their organizations — a requirement that is growing in importance. With the increased focus on enterprises' digitalization, managing their key master data is also important. The ability to achieve and maintain a single, semantically consistent version of customer master data is crucial for customer-centric organizations. More importantly, trusted data sits at the center of a digital business platform, so demand for effective MDM will continue to increase in coming years.

Fresh hot roles for the information-savvy organization are emerging to help organizations become more data-driven. New skills and competencies are required for existing roles to adapt to the changing role of data and analytics. This session will highlight the key roles and responsibilities in data and analytics to be ready for the digital business and the impact on the organization model. Key issues: What's happening in data and analytics forcing change? What's the impact on the organization? What's the impact on roles and skills?

In landscape of autonomous business units and departments, finding the optimal balance between data anarchy and overly strict control of data management is as much an art as it is a discipline. This session will explore how an adaptive approach to governance can lead to higher levels of data and analytics maturity.

The Future of Data Science, Machine Learning and AI presentation is for people looking to 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 organizations plan and prepare for and make the changes.

Organizations increasingly 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?

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-outcome-driven analytics evolution roadmap with a strong commitment to users and a shift from technology to business impact.

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.

Data integration is a persistent practice that is not specifically focused on the market as it is today. The presence of legacy, resilient systems and innovation all in the same market, requires robust and consistent delivery of highly traditional practices and emerging or innovative approaches. In 2018, the biggest change in the market is the shift from an anticipated future demand for metadata-driven solutions to a current market expectation that these solutions will be delivered as part of the data integration platform. Currently, there is no forgiveness for inadequate metadata capabilities. Another major shift is customers asking for hybrid deployment, but now, with the expectation of multicloud and cloud-to-cloud integration.

The explosive growth of features, and the vendors emerging to implement them, continue to slow down. The features that initiated the expansion, such as storing new data types, geographically distributed storage, cloud and flexible data consistency models, have become common. Today, nearly every established or emerging DBMS vendor supports them to some degree. In 2016, the OPDBMS market shifted from a phase of rapid innovation to a phase of maturing products and capabilities. This continued in 2017.

Join us for refreshments in a brief break between sessions.

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 is at the core of a digital transformation. Enabling the use of data across the organization 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?

Many organizations are dispensing with the notion of classic enterprise reporting and BI competency centers (BICC) within IT organizations. In their place, self-service analytics and data science organizations have emerged with nascent analytics centers of excellence (ACE) to govern, guide and support them. Yet many such functions tend to lack a significant number of key capabilities.

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?

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 three challenges in data management. 1. How can organizations incorporate augmented and stand-alone 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?

In the fast-paced world of racing, seconds make or break a winner! During the race, real time data is processed across many CPUs using the latest live data to constantly ask questions like: What if the Safety Car comes out ‘now’? or next lap? What if our competitors do X or Y? These questions are being asked the whole time, so that best response answer is already calculated for the risk profile if indeed the event being modelled does happen. In this keynote address, Neil outlines how the simulation and analytics have been used throughout the organization to understand and mitigate ‘risk.’

More organizations initiate diversity programs every year that aim to employ minorities, women and those with disabilities. Yet, truly beneficial diversity must span beyond gender and ethnicity, while welcoming men to be ardent supporters. How do you thoughtfully design diversity programs to avoid unintended consequences and maximize their effectiveness?

Join your peers for an informal discussion on data and analytics trends specific to government. To kick off the table conversation, attendees will be presented with some of the key findings from the government results of the 2019 CIO Agenda survey.

Analytics and its derivations are critical for banking and securities firms to succeed in the future. Firms look to apply analytics, in conjunction with other technologies such as AI, across most of the business functions to compete better and operate efficiently. In this session, we will explore what the specific new trends, emerging technologies and how both incumbents and their fintech challengers are applying analytics in their business.

Join us for refreshments on arrival.

Registration hours for the day.

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.

Traditional data and analytics governance that focuses only on policy, compliance and enforcement, is now obsolete for digital business. Adaptive governance practices that are contextual and elastic are urgently needed for successful digital business initiatives. This session discusses why current practices are now inadequate and how adaptive governance practices can be adopted to enable better outcomes.

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.

Introducing the term information as a second language (ISL/I2L) as an umbrella for driving data literacy as a core, strategic business discipline. Why is it critical to assess diversity of your information and analytics community? How can you recognize areas inhibited by shared language? How can you take initial steps to bridge the communications gap and establish your ISL foundation?

Data integration is foundational to any traditional or modern data and analytics initiative. Harnessing data precisely at each business moment compels organizations toward pervasive integration. Data management and integration must address blended data types, integrator roles, data delivery patterns, machine-learning and AI. Design considerations include the data lake, data hub, semantic tiers and the logical data warehouse. Diverse technology choices include everything from traditional batch, to data virtualization or semantics through distributed services, microservices and APIs. 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?

An accurate and precise system of measurement is a major factor in an organization’s success. Despite improvements in our ability to measure growth, cost, efficiency, quality, risk, and more; most organizations tend to fall victim to the traditional performance measurement pitfalls that have plagued the business world and public sector for decades. Common pitfalls include measures that fail to yield actionable insight, produce a sense of apathy, or employee rebellion, and unintentionally encourage negative behavior. This presentation will examine how technical capabilities to collect and analyze data when coupled, the right performance measurement discipline can create enormous value. Data and analytic leaders interested in how to improve their organization’s ability to measure performance and link metrics to business outcomes will benefit from this session.

With an increasing focus on cost optimization across data and analytics portfolios and the advent of newer technologies to renovate data and analytics infrastructure, data and analytics leaders are often caught in a conundrum on how to balance the two. This session guides them through some of the most innovative and upcoming pricing trends, licensing models and best practices to negotiate with new and existing vendors with a goal to optimize costs. What is the current scenario in the data and an analytics pricing landscape? What are the existing and upcoming pricing trends and new licensing models that you should be aware of? What are the best practices to negotiate with vendors to save millions?

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 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.

Join us for refreshments in a brief break between sessions.

Everyone wants a data lake, but most attempts fail to live up to the 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?

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. 1. What are the specific AI governance concerns? 2. What are the common topics in EIM and AI governance? 3. What are the early lessons learned? 4. What are the next practices for AI governance?

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 do the top 3 or 4 innovations look like and what value do they provide? How do you prioritize your technology investments? Which are the vendors to watch? What challenges should you consider?

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 cover trends and best practices around hiring and training not only data scientists, but the entire skill mix necessary to build successful data science teams: 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?

This workshop will demonstrate the importance of understanding that one negotiation process doesn't fit all vendors. The content and action items will provide a set of four steps via a framework called "T4" that must be followed and tailored for each megavendor to effectively plan for and negotiate the optimal deal. These steps focus tactics (T1), templates (T2), T&Cs (T3) and timing (T4) for negotiating megavendor deals for cost optimization and risk mitigation.

Join attendees for lunch and take advantage of the opportunity to network and catch up on the day's activities.

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 operations and data management in production. 1. How can machine-learning-enabled data catalogs help data and analytics leaders to find, catalog and inventory their heterogeneous data assets? 2. What are the market offerings in this space, their various segments and which offering would make sense according to your existing use case requirements? 3. How to plan and implement data catalogs which assist with metadata management and governance and don’t introduce metadata silos?

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 widens as we move forward, between the continuous availability of data and 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 science techniques harness predictive insights to drive prescriptive action. This presentation will provide an approach for extending from predictive analytics to prescriptive approaches. Questions addressed include: How do predictive and prescriptive analytics complement each other? What are the most common techniques for both? What are best practices and recommendations for combining them as demonstrated through case studies?

Data and analytics leaders often find themselves in political discussions, which can be high stakes, and challenging to successfully navigate. This session helps data and analytics leaders know when they are in a political conversation, and provides practical techniques for managing the potential conflict and reaching a successful outcome.

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.

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 continue 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.

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.

Increasingly your organization is sharing an ever wider variety of information internally and externally. This session will describe what terms and conditions should be considered for every such agreement, including software agreements that deal with data ownership and rights. You will also see some of the best and worst examples of contract language out there today.

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.

IoT deployments tend to be one sensor for one measurement for a single purpose. But, data and analytics can enhance and expand the value of IoT to integrate data from many sensors and measurements with organizational and third-party data for a greater purpose. But, the ability to integrate data depends on the ability to unambiguously identify the IoT devices, correlate over the same time periods, and connect the data with each other in a meaningful way. This session will explain the role of MDM and the unique ways that has to be deployed in order to accomplish just that.

Join us for refreshments in a brief break between sessions.