What a week!
This year’s Gartner Data & Analytics Summit in Orlando, Fla., was a jam-packed three days of 220+ sessions, keynotes, and networking opportunities attended by over 4,000 data and analytics leaders and industry experts. It was exciting and inspiring to see the data and analytics community come together to share ideas, insights, and strategies for driving business value, navigating disruption, and tackling real challenges such as articulating ROI, telling data stories impactfully, and doing self-service analytics right, as well as hear best practices around new and old topics (e.g., decision intelligence, metrics stores, data ops, governance, analytics operating models, etc.). And of course, there was TONS of buzz—and caution—around generative AI and GPT. 🤖
Below are our key takeaways and notable trends from attending many sessions and having great conversations with people in the hallways, show floor, breakout rooms, and various breaks/ lunches/happy hours.
Hope you find this summary insightful!
Written by: Chris Walker,
Head of Product Marketing at Tellius
In This Post
Analytics ROI and Value Articulation
In the opening keynote, Gartner analysts Kurt Schlegel and Rita Sallam shared a staggering stat from a recent survey that 69% of data and analytics leaders struggle to deliver measurable return on investment (ROI).
Analytics leaders we spoke with echoed this sentiment, adding that in this current economic environment, gone are the days of data and analytics budget for nebulous future positive outcomes. Instead, as data professionals, we need to speak the language of the business, tell stories that clearly articulate the value, and think financially (e.g., revenue, productivity, cost and/or risk reduction) and strategically (e.g., increasing know-how, improving the brand, boosting innovation) to influence long-term decision-making, not just the next shiny object.
Take: Identifying valuable use cases and engaging in stakeholder alignment and communications are critical to articulating the value of data and analytics investments, and it was impressive to see Gartner’s Enterprise Value Equation applied to real-world use cases. To hear about the value Tellius is bringing our customers, check out our success stories.
Self-Service Analytics
Self-service analytics was another important theme with fascinating diversity and breadth of dialogue on the topic.
For example, one session touched on the evolution of self-service analytics from an IT-led analytics era to an author-led self-service analytics (ushered in by Tableau and other visual analytics platforms) to now an emerging era of consumer-led self-service enabled by natural language technologies, automated insights and data stories, and accessible advanced analytics.
Other sessions referenced self-service analytics as it relates to how/where it fits into an organization’s analytics maturity journey; implication for data ecosystems; powering business resiliency; and much more. It was an interesting dichotomy between rosy vendor sessions on SSA driving value for their customers and more pragmatic sessions by industry experts and analysts: e.g.,Gartner analyst Edgar Macari’s session, “7 Fatal Flaws of Self-Service Analytics,” which pointed out issues but also offered solutions to properly implementing a self-service analytics capability as part of an overall analytics strategy.
Take: Self-service analytics empowers business users to access and analyze data without IT or analyst support to drive short-term value, as well as long-term value, such as increased agility and expedited speed to decision-making—when done well. An interesting insight that emerged from the roundtable Tellius hosted with 22 D&A leaders on self-service analytics was the many shades and organizational expectations around self-service analytics (e.g., build your own dashboards vs. being able to click around pre-built dashboards vs. business users being able to create their own models, etc.), with a consensus of enabling enterprise analytics agility and curiosity. Gartner rightly pointed out that it’s important to identify technology and policies that balance control with freedom.
Decision-Making and Decision Intelligence
As providers of an AI-powered Decision Intelligence platform, we were thrilled to see a large emphasis on better decision-making and decision intelligence becoming increasingly important for organizations. For those new to the term, Decision Intelligence involves using advanced analytics, machine learning, and other techniques to help organizations make better and faster decisions. Regarding improving decision-making, Gartner analyst Jorgen Heizenberg held a session, “What Do CDAOs Need to Work on in 2023? Decision-Making Skills!,” to help chief data analytics officers improve their decision-making skills, while analyst Jim Hare described the multiple levers to improve decision-making performance by using the right combination of logic and data, instinct/training/experience, collaboration, and principles.
Take: Decision Intelligence takes the best of analytics/BI and data science/ML, while being powered by AI, to remove as much friction as possible when going from data to decision. Learn more about the rise of AI and augmented analytics for better business outcomes here.
Governance and Metrics Stores
The focus on governance and metrics stores was also a key theme of the summit. Governance is necessary when trying to grow a self-service analytics culture at an organization. It is very important to understand where the data is coming from, who owns and is using the data, and how the data is changing. As data and analytics become more pervasive across organizations, it becomes increasingly important for them to establish governance policies and metrics to ensure data quality, consistency, and security.
Take: This dovetails nicely with the prior point around finding a balance between control and freedom. Tellius’ natural language search, dashboarding, and rapid root cause analysis enables freedom, while our semantic layer, ontology, built-in data dictionary, aliasing, and row-level access and controls aid in the application of an organization’s governance and MDM policies.
Analytics Operating Models (Analytics Franchises)
We were happy to see so many companies exploring ways to optimize their analytics operating models to ensure that data and insights are shared and leveraged across organizational silos and ecosystems.
In the first day’s keynote, as well as his session on the Magic Quadrant for Analytics & Business Intelligence Platforms, Gartner’s Schelgel shared the metaphor of a franchise (e.g., like Starbucks) for empowering local teams to act locally in their domain while doing it in a very globally consistent way that shares best practices.
In casual conversations at the conference, we heard about embedded data teams within individual domains helping to maintain data quality across the organization and improve efficiency by working directly with the domain teams. In several other sessions, and during conversations in the hallways, it was clear that it’s important to think about the people behind the analytics, not just the technology.
Take: We couldn’t agree more that it’s the people that matter in, and surrounding, analytics. Analytics is a team sport. Business users and analysts and data experts should be able to collaborate and bring their best domain and technical knowledge together to move the business forward.
Generative AI/ ChatGPT & Other Exciting New Technologies
Finally, generative AI and chatGPT were very hot topics. Gartner estimated that they track over 500 start-ups focused on generative AI and LLMs beyond OpenAI’s GPT models from places like Cohere, Anthropic, and AI21 Labs, as well as OS LLMs like Hugging Face, Stability AI, and more.
Gartner’s Schlegel said, “Generative AI is a business game-changer. It will be as transformative as the internet and will experience similar highs and lows. Stay informed. Stay skeptical.”
Gartner analyst Whit Andrews noted that AI “will infuse every single aspect of our work,” noting that one in four people is “AI-friendly” (eager for AI to be employed at their company); one in four is “AI-averse” (they think it’ll make the world a less equal place); and the other half is “AI-conditional” (analogy: they’re okay with the doctor having access to their data, but not the government).
To counteract biases and potential misinformation, Gartner recommended putting in place responsible AI practices including AI usage guidelines and limitations documentation, as well as collaborating with key stakeholders on lessons learned while initially testing the technology.
Several vendors discussed chatGPT or generative AI integrations. Gartner analysts mentioned it in so many sessions that it became a running joke to count how many minutes would go by before a GPT mention. During breakfast and lunch conversations, a common icebreaker at the table was, “What are y’all doing with chatGPT?”
One anecdote came from an analytics leader at a university we spoke with who pointed out a fear that students will write entire papers using chatGPT, while others in the university simply roll with it and assign homework related to the latest headlines that chatGPT isn’t trained on. Gartner analyst Daryl Plummer made a great point: “Rather than worry if chatGPT is doing your student’s homework, worry instead on teaching students to be critical thinkers and evaluators of chatGPT outputs.”
Other hot topics aside from generative AI included temporal and geospatial data to power digital twins.
Take: Generative AI for analytics is a no-brainer, and it’s something every analytics leader should explore. But there is a caveat: LLMs are great at generating summaries, providing greater context and translation, and validating work where there is no definitive “right” answer. But when it comes to precise analytical answers, it pays to work with an AI-powered analytics vendor that has put proper guardrails and governance in place to join the power of generative AI with augmented analytics. Learn more about our Tellius Copilot, which takes our platform’s industry-leading augmented analytics capabilities to the next level by leveraging the power of GPT.
Summary
Overall, we had an amazing time at the summit and are 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 year!
