From data-centric to decision-centric: Insights from 2024 Gartner Data & Analytics Summit

Last month, thousands of data and analytics leaders converged in London for the EMEA Gartner Data & Analytics Summit to discuss the most forward-looking ways of harnessing the power of data and analytics. Linkurious’s CEO and Head of Products attended to participate in the discussions around the pioneering ideas and frameworks that will shape the next evolution of how enterprises can turn data into a competitive advantage.

The theme of the day (or rather, days) was decision making and decision intelligence. Here’s a quick summary of some of the key insights that emerged on how to drive impactful decision-making and generate more value through the collective power of people, data and AI:

• Shifting from data-driven to decision-centric: The focus is moving from being merely data-driven to adopting a decision-centric approach, using decision intelligence to improve how data informs business choices.
• Contextual, continuous, and connected data: Future data and analytics must be more contextual in integrating diverse data sources, continuous in providing up-to-date insights, and connected through technologies like graph analytics.
• Managing complexity: Successful organizations are embracing complexity, finding the right balance between sophisticated capabilities and user-friendly systems to outperform competitors.
• Ecosystem thinking: Data leaders need to understand their entire data ecosystem, including organizational architecture and external touchpoints, to drive impactful decision-making.
• AI and collective intelligence: Organizations are entering a new era combining human expertise with AI capabilities to drive superior business decisions and value creation.

Read on for a more in-depth look at these key insights from Gartner Data & Analytics Summit 2024.

One of the most important themes was the need to move beyond just being data-driven to adopting a decision-centric approach to data and analytics. According to Gartner’s research, 65% of organizations still use data selectively to justify decisions they've already made, rather than letting the data truly drive decision-making.

By adopting decision intelligence, organizations are essentially moving towards a decision-centric data and analytics approach. Decision intelligence captures data, interprets and models it, before synthesizing its value and delivering insights, including automated insights and decisions. “The crucial part of decision intelligence is the explicit understanding of how decisions are being made by business people,” says Pieter den Hamer, Vice President in Gartner Research. “We are opening the black box of decisions.”

Jim Hare, a Distinguished VP Analyst at Gartner, emphasized that the future of data and analytics must improve business decision making by being more contextual, continuous and connected (more on that in a moment). Data alone does not suffice - it must be surfaced and integrated in a way that provides true decision support.

To make data and analytics efforts more decision-centric, they need to become contextual by integrating more external and multi-structured data sources. They must also be continuous by moving from diagnostic to prescriptive analytics, with regularly updated information flows.

But most importantly, data and analytics must be connected - both in linking disparate data sources, but also in understanding how business decisions are interconnected. Graph technology is a key piece of the puzzle here. “Graph is really surfacing itself as a key enabler around decision intelligence,” says Jim Hare of Gartner. “Why? Because it allows you to connect more of your data, and you can also see how decisions are connected, not just the data itself.”

“The trend of decision intelligence is growing a new class of business intelligence products, made possible by graph technology,” says Linkurious CEO Sébastian Heymann. “Contextualized and high quality data are indeed critical to unlock new insights with high ROI.”

“We are in an ever accelerating, ever complexifying world, and we need to have the tools and the disciplines and the skills to be able to manage that complexity,” says Gareth Herschel, VP Analyst at Gartner. The key is embracing complexity - having skills to make sense of an increasingly complex world. AI and new technologies aren’t the silver bullet that will simplify complexity, but when used correctly they can build on existing foundations to make complexity manageable. 

This means finding the right balance between introducing valuable complexity and maintaining simplicity. Clean processes, easy-to-use systems, and intuitive interfaces are critical. But so is having the sophistication to outperform competitors, with capabilities like graph analytics to understand these ecosystems and link everything together.

Extending this notion of managing complexity, speakers emphasized the importance of “ecosystem thinking”. Data leaders need to understand their full data environment and the surrounding context. That means not only understanding data architecture, but also organizational architecture: the various moving parts, the different touch points, the people involved.

“One of the reasons we’re seeing growth in areas like graph analytics is because it helps us understand these ecosystems and link everything together,” says Gareth Herschel. As the trusted voice interpreting implications of changes across this broad ecosystem, data teams can drive impactful decision-making.

Last but not least, one of the central themes of Gartner Data & Analytics was the increasing convergence of human and machine intelligence - that is, AI - to drive better decisions and business value. According to the Gartner research, organizations that view AI as “strategic” have outperformed their peers by 80% over the past 9 years. 

The most successful leaders are focusing on changing culture, creating data & AI strategy, managing analytics functions, and implementing robust governance practices. D&A leaders should be working towards making data AI-ready - and working towards implementing new technologies where they can. “The details of a perfect strategy should not prevent you from executing a good strategy,” says Adam Ronthal, Research VP in Gartner's ITL Data and Analytics group.

At the same time, AI does not stand alone as a cure-all solution. Like we mentioned above, it’s not a silver bullet. The best decision intelligence strategies aren’t powered primarily by AI. “Don’t lead with technology,” says Alys Woodward, Senior Director in the CIO research group at Gartner. “Avoid a technology-driven approach. It’s too early. Instead, develop decision-making flows and look at how AI can be used within those.” In other words, AI should be used to enhance human intelligence.

Over the course of Gartner Data & Analytics 2024, the consensus that emerged is that to truly generate value, data and analytics efforts must transcend just working with data. They must be centered around informing and operationalizing decisions through connected, contextualized and continuously-updated insights. Embracing - and intelligently managing - this ecosystem-level complexity is the path to outperforming through superior decisioning.

In a data and analytics environment that is constantly evolving, to quote Gartner’s Gareth Herschel: “The only bad move is no move at all.”