Conference Updates

London, U.K., May 24, 2023

Gartner Data & Analytics Summit 2023 London: Day 3 Highlights

We are bringing you news and highlights from the Gartner Data & Analytics Summit, taking place this week in London, U.K. Below is a collection of the key announcements and insights coming out of the conference. You can read the highlights from Day 1 here and Day 2 here.

On Day 3 from the conference, we are discussing how to avoid data management mistakes, how CDAOs can raise the performance of stalled AI projects and what D&A leaders need to know about data observability. Be sure to check this page throughout the day for updates.


Key Announcements

What Every CDAO Should Learn About Data Management

Presented by Aaron Rosenbaum, Sr Director Analyst, Gartner

Centralization of data management is not always a good idea, but sometimes it is. Enterprise data can use distributed and federated governance and let the owners do more than half of the governance work. In this session Aaron Rosenbaum, Sr Director Analyst at Gartner, explored the many false assumptions around data management, the resulting mistakes and how to avoid them.

Key Takeaways

  • “Many CDAOs fall into the trap of data hoarding or paranoid data control. Data itself is an example of entropy and a better model of balancing controlled chaos with observable behavior helps reduce the friction between governance, application developers and users.”

  • “As a CDAO, it is important to avoid falling into the trap of assuming that one data design is “better” than another. The moment a system or application is imbued with authority—instead of the data content—the use of the data from that application becomes limited.”

  • “Instead of planning to integrate data “later”, start trying to reuse it immediately upon deployment. Compare it to other similar data right away—and where possible automate that process.”

  • Gartner recommends that CDAOs focus on seven principles of data management:

    • Learn from users already accessing data.

    • Don’t design from scratch or assume you will redeploy data.

    • Find as much metadata as you can.

    • Purposeful redundancy is necessary.

    • Start with passive metadata, but know that you will need active metadata capabilities.

    • Existing data management solutions work. Focus on them working together. 

    • Integration, data quality, data mastering and security do not go away.

How to Improve the Performance of Stalled AI Projects

Presented by Leinar Ramos, Sr Director, Advisory, Gartner

Data and analytics leaders often see their AI projects stall — either right from the start or after deployment. In this session Leinar Ramos, Sr Director, Advisory at Gartner, shared some AI performance accelerators that can help push stalled AI projects into production, or raise their performance to the next level.

Key Takeaways

  •  A Gartner survey found that 46% of AI projects never make it into production. “The problem is that many AI projects hit a wall. We often make some good initial progress in AI projects, but as time goes by we hit a performance barrier.”

  • “When we ask organizations what are their key barriers for AI implementation, measuring and understanding business value comes right at the top.”

  • Don’t confuse model performance with business performance. Models make predictions, but capturing business value requires you to act. We need to redesign our process and measure the factors that actually drive business value.”

  • Gartner developed an AI compass focused on four cardinal directions - Strategy, Data, Modeling and Testing and Infrastructure and Operationalization - to help teams navigate the wide range of tactics to improve performance.

  • Five key actions to take to push stalled AI projects into production:

    • Share the AI Compass with teams and use it to prioritize next actions

    • Involved domain experts in AI projects

    • Expand data collection internally and externally

    • Define an action plan for AI projects to capture value

    • Invest in AI engineering to accelerate and improve AI value at scale

Data Observability: A New Trend You Need to Know for Building Reliable Data Landscapes

Presented by Jason Medd, Director Analyst, Gartner

Data observability as an emerging technology provides the ability to understand the health of an organization’s data landscape, data pipelines, and data infrastructure by continuously monitoring, tracking, alerting, analyzing and troubleshooting incidents. In this session Jason Medd, Director Analyst at Gartner, explained how it can help to build a stronger data landscape, given the modern, complex and distributed data stacks.

Key Takeaways

  • “Data observability provides continuous, holistic, and cross-sectional visibility into complex data landscapes and synthesizes signals across infrastructure, application, and data layers to provide a comprehensive understanding of individual components, data pipelines, and system performance.”

  • “It allows the enterprise to measure what matters in their data journey, experiment and adapt, and implement data strategies that align with business strategies and requirements.”

  • Data observability primarily focuses on five things: Observing data; observing data pipeline; observing data infrastructure; observing data users; and observing cost and financial impacts.

  • “Data observability is powerful, but has limits, such as only covering data at rest, not data in motion or doesn’t deliver data or fix data issues.”

  • “Partner with business stakeholders to evaluate and demonstrate the business value of data observability practices by tracking improvement of data issues management within data pipelines to show tangible benefits.”

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