Last week’s Gartner Data & Analytics Summit in Orlando was a jam-packed three days of 150+ sessions, keynotes, and networking opportunities attended by 5,000+ data & analytics leaders and industry experts. Below is a distillation of our top seven takeaways from the event. Hope you find these valuable! 💜 Tellius
In This Post
Generative AI
AI and GenAI were unsurprisingly the top topics of the event, referenced in nearly every keynote, session, and vendor booth. From the opening keynote—which touched on the need for AI-ready data and governance to support AI ambitions—to the last sessions, AI was by far the most popular topic of the conference.
What was different this year compared to last year’s event, however, was the degree to which GenAI is actually being implemented. In fact, in a recent Gartner poll, ~55% of analytics leaders are piloting generative AI initiatives or have some already in production. In the Top Trends in AI session, Gartner said 2023 was “the year of exploration” and 2024 was “the year AI initiatives get real.” Due to economic pressures, there’s a mandated shift from experiment to execute and from execution to results.
Furthermore, it was interesting to hear several analytics leaders’ approaches to AI and GenAI. Mallory Freeman, PhD, VP of Enterprise Data and Analytics at UPS, for example, shared examples of UPS’ growing adoption of AI and GenAI across multiple domains and value areas such as revenue growth (CX, next-gen pricing, behavior trend segmentation), cost reduction (navigation optimization, network planning, payload forecasting), and risk management (fraud detection), while outlining the AI guardrails they have in place for ethics, security, compliance, AI product development, and a culture of responsibility.
Scott Richardson, CIO of Enterprise Data & Analytics at Ally Financial, also shared some insights into their enterprise platform that enables AI (including the orchestration layers and backend services layers); their approach to an AI factory delivery model; and their approach to responsible AI in order to “Do it Right with AI.”
Take: This year’s summit was packed with best practices for the responsible and secure implementation of AI into numerous business facets. It affirmed our approach to applying AI to analytics—in the form of a governed, human-in-the-loop, contextual approach.
Governance
Governance was a central theme of the summit, frequently referenced in lockstep with AI and in the context that AI-ready data (i.e., properly governed) is the fuel to AI ambitions. Mallory Freeman from UPS highlighted governance’s centrality succinctly when she said, “We can trust our data because we govern it.”
In a recent CIO survey, while 35% were exploring GenAI, 38% were piloting, and 21% were in production, 100% of the respondents were concerned with risk management and governance. Sessions referencing governance ranged from sessions on AI ethics (and the criticality of governance), regulations, AI observability, and much more. There was even a session by Melody Chien of Gartner—“Generative AI Is Changing the Way We Manage Data — Get Ready for it!”—which explored a number of GenAI-enabled data management use cases while also outlining risks such as inaccuracies, privacy/legal/security concerns, implementation costs, and other ethical factors.
In conversations with attendees and the numerous networking opportunities, it was fun to see leaders involved with data governance have a moment of recognition as the backbone of data, analytics, and AI initiatives rather than play the typical behind-the-scenes role.
Take: Governance is the bedrock of modern analytics and AI. Tellius’ natural language search, dashboarding, rapid root cause analysis, and GenAI analytics capabilities are built on our customers’ governance, MDM, and semantic layer and further robustified by our knowledge graph, enterprise-grade guardrails and security—enabling trustworthy GenAI-powered analytics. If you’re considering an AI analytics platform, governance capabilities should be a top priority.
Execution Over Strategy
In the opening keynote, Gartner analysts Debra Logan and Ehtisham Zaidi shared findings from a study of top-performing analytics leaders that showed that, empirically, leaders who focus on execution over strategy move the business needle more.
Eric Donkoh, CDAO of The Bermuda Monetary Authority, put into words what we think many D&A leaders are feeling: “In your quest for a perfect strategy and data—borne out of a fear of getting it wrong—don’t let ‘perfection’ stop you from executing on a ‘good’ strategy.” Gartner experts went on to share frameworks and value assessment approaches to determine which projects to execute upon. The focus was on iteration and quick learning, rather than multi-page analytics strategies and 10-year plans. Indeed, in a session by Gartner analyst Donna Medeiros—Senior Director with Gartner’s Chief Data Officer Research Team—she shared a single-page D&A strategy template!
Take: Value-adding execution and tangible wins are critical during tighter macroeconomic times. In this rapidly evolving technology environment, the danger of having an overly forward-looking strategy is that something changes or the business fails to fundamentally understand the D&A team’s value, D&A is viewed as a cost center rather than the real value driver that it is.
Business Value
Creating and communicating business value was a theme this year, like in years past. The twist, of course, was that AI and GenAI are key opportunities to show business value, but it’s critical to speak in the language of the business in terms of financial (e.g., revenue, productivity, cost and/or risk reduction) and strategic results (e.g., increasing know-how, improving the brand, boosting innovation). Analytics leaders we spoke to echoed this sentiment and pointed out the increasing expectations of data and analytics return on investment.
Recommendations from the summit regarding business value are to speak the language of the business, tell stories that clearly articulate the value, and think to influence long-term decision-making, not just the next shiny object. In a session specifically oriented toward CDAOs, Gartner recommended prioritizing GenAI initiatives by value, cost, complexity and strategic alignment with a preference towards Defend (task-specific improvement/more productivity) and Extend (enhancing product for differentiation) rather than Upend (creating new products, markets, core processes).
Practically speaking, you should start by documenting your initiative’s alignment to business value; list current capabilities and identify deficits for delivering value; and focus on analytics adoption. This might not be easy, but these are proven techniques to drive pervasive D&A usage.
Take: Identifying valuable use cases and engaging in stakeholder alignment and communications are critical to articulating the value of data and analytics investments. To hear about the value Tellius is bringing our customers, check out our success stories.
Self-Service Analytics
One way to make D&A pervasive in an organization is through self-service analytics, which was referenced in about a quarter of all sessions during the summit. Sessions on the topic included how/where it fits into an organization’s analytics maturity journey; how data mesh and data fabric enable self-service analytics; the implication for data ecosystems; how to power business resiliency; how self-service has changed from 2011-2021 and the three ingredients for a self-service analytics franchise (centralized and decentralized organizational structure; multidisciplinary teams; and prototype-first governance framework); and several others.
What was interesting was the age-old struggle between control and freedom (centralized vs. distributed) that continues to play out as enterprises seek new ways to make data, analytics, and increasingly, AI, more accessible to more people in the organization.
Take: Self-service analytics empowers business users to access and analyze data without IT or analyst support to drive value in the form of faster speed to decision-making—when done well. We enjoyed the opportunity to host a session with industry experts from eBay and AbbVie around their AI analytics and self-service analytics journey to accelerate decisions from enterprise data.
Narratives & Storytelling
Another theme from the summit was the importance of business-impacting storytelling. In one session by Gartner analyst Matt Wakeman, entitled “Why No One Uses Your (Marketing, Talent, Supply Chain, Etc.) Dashboards,” he advocated for an approach that considers the business decision flow and utilizing “in progress” designs to get more substantial stakeholder feedback.
In another session by Gartner analyst Aura Popa, titled “Data Storytelling in the Realm of Generative AI,” she explained the key elements of a good data story—visualization, narrative, context, emotional triggers, and decision options. She offered a rubric to determine if a story is important enough to tell, three key storytelling pitfalls to avoid, and the interconnections amongst the elements. For example, visualization and narratives should go hand-in-hand to explain “What am I looking at?” because visualizations should be brought to life. Next, context is critical to answering the next question of “Why is this happening?” because data alone is not enough to tell the story. Finally, emotional triggers and decision options provider the listener with an answer to the question “What are we going to do” because a story that fails to lead to insight and action fails its purpose.
Take: Tellius helps users quickly understand the “what,” “why,” and “how best to proceed” in their data. Our natural language search, auto visualization, live dashboarding, and proactive alerts tell you “what” is happening; our automated insights, including key drivers, root cause analysis, and automated cohort comparisons, explain “why”; and finally, predictive and what-if analysis helps users get to the “how” to proceed next. In our latest release, we’re also excited to further improve upon our data narratives and storytelling capabilities in the form of GenAI-powered narratives and human-powered data storytelling to more quickly and easily tell business-impacting stories with enterprise data.
Human in the Loop / Human Ingenuity / Change Management
Last but certainly not least was the importance of the people involved in analysis and decision-making. As many times as AI/governance was discussed, there was also mention of the need for humans to exercise critical thinking and imbue domain expertise.
In the “10 Best Practices for Scaling Generative AI Across the Enterprise” session, Gartner experts recommended data and analytics leaders “ensure there’s a human in the loop to vet the output of GenAI applications while exploring ways to automate the process” and to “create GenAI communities of practices to encourage knowledge sharing and collaboration.”
Another critical piece touched on at the summit was the importance of solid change management. Ally Financial’s Richardson, for example, pointed out that AI is a massive change management moment for an organization and that it’s paramount to get the awareness, learning, and adoption right for the technology to be a success.
Take: Our motto at Tellius is “AI Analysis, Human Decisions” because we recognize the critical role humans play in turning augmented data analysis into decisions and action. In our latest release, in fact, we continue to emphasize the balance of human and machine through GenAI-powered narratives and search, combined with human-powered collaboration and storytelling. To learn more about these new capabilities, join our upcoming webinar.
Bonus! Fun Takeaways from Guest Keynotes
Guest keynotes often provide fresh, provocative perspectives. This year was without exception. In her keynote on the future of human-robot interaction, Kate Darling, a leading expert in robot ethics and research scientist at the Massachusetts Institute of Technology (MIT) shared a key point that “the true potential of robots is not to recreate what we already have—it’s to do something new.” Similarly, in the data and analytics world, the true potential of GenAI is not to recreate our current approaches but to rethink new analytics paths.
Another interesting guest session was David Kwong’s “Creating Order Out of Chaos,” in which he posited that magic and puzzles share an interesting characteristic: Magic is about revealing the chaos under the surface, while puzzles start with chaos and end in order. In the session, David performed an amazing magic trick (pulling someone’s marked dollar out of an unopened kiwi) and also created a crossword puzzle in real time on stage. (As a New York Times crossword writer, he’s qualified!) Our takeaway from his session was that, just like a good magic trick that “messes with your reality up close to do something impossible,” last year’s debut of ChatGPT was, for many people, the moment AI and AI-powered copilots blew people’s minds and launched a generation of AI curiosity and excitement.
Take: Magic and robots are cool! But we’re data and analytics nerds, so we’re more excited about how generative AI can be thoughtfully applied to drive business value through AI-powered narratives, easing the burden of data prep/enrichment, and next-gen self-service analytics. Check out our latest improvements to our GenAI analytics capabilities here.
Summary
Overall, we had a blast at the summit and were grateful for the opportunity to connect with so many amazing data and analytics leaders! We look forward to incorporating these key takeaways and trends into our own work and continuing to innovate in the field of augmented analytics and decision intelligence. We left feeling energized and excited for the future of data and analytics and want to give a big shoutout to Gartner for hosting such an incredible event and thank everyone who attended who made it such a great experience! Until next time! 👋
