Gartner Data & Analytics Summit 2024 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 exploring what data AI-ready means, as well as highlighting the cost, risk and value of AI, and the key steps to reduce the environmental impact of data. Be sure to check this page throughout the day for updates.

Presented by Roxane Edjlali, Sr Director Analyst, Gartner

Chief data and analytics officers (CDAOs) and heads of data management are being asked to make their data AI-ready as their companies are eager to invest in AI and machine learning (ML). In this session, Roxane Edjlali, Sr Director Analyst at Gartner, discussed what AI-ready is and how data management should evolve to support these requirements.

• “AI-ready data means that your data must be representative of the use case, of every pattern, error, or outlier that is needed to train the AI model.”

• “Having data ready for AI drives greater business outcomes by 20%.”

• “To prove data is AI-ready, D&A teams will need to be capable of quickly iterating and converging to identify data that is fit for use.”

• “Make data management an AI strategic priority. Put in place cross-functional teams to support your AI-ready data requirements.”

Presented by Alys Woodward, Sr Director Analyst, Gartner

AI software platforms are evolving, with the AI operationalization phase giving way to the next evolution of AI software based on composite AI. In this session, Alys Woodward, Sr Director Analyst at Gartner, discussed how organizations should reassemble their decision mechanisms through practical decision intelligence techniques.

• “Focusing on making the next decision and successful execution of the decision are top reasons for not tracking results from decisions.”

• “Avoid taking a technology-driven approach to modeling decision-making.Instead, take a people- and business-driven approach by focusing on desired business outcomes.”

• “Develop decision-making flows as compositions that leverage composite AI (one or more AI techniques) like symbolic techniques and analytics to use the full power of decision-intelligence platforms (DIP’s) knowledge representation capabilities.”

• “Empower and incentivize staff, business and technical alike, to build and refine decision models by leveraging DIPs’ user interface generation capabilities.”

• “Use decision logs and business performance metrics to support auditing, compliance and continuous decision improvement.”

Presented by Gabriele Rigon, Sr Principal Analyst, Gartner

There is a significant focus on the environmental footprint of data centers and cloud services, but not enough on the data itself. In this session, Gabriele Rigon, Sr Principal Analyst at Gartner, explored three key steps organizations can take to reduce the environmental impact of their data.

• “Sustainability data measures the environmental, social and governance performance of an organization and its value chain.

• “Start off by assessing the current state of your sustainability data so that you can prioritize investments that drive stakeholder success.”

• “Second, create a strategy or a vision for what data and analytics (D&A) for sustainability will look like. This will ensure you receive buy-in from your stakeholders.”

• “Finally, execute your strategy to align sustainability data and governance with organizational best practices.”

• “Regardless of your maturity, it’s going to be important to create a system of governance, not only for others, but for ourselves. Where this all comes together, is around data stewardship.”