Last week, hundreds of data professionals gathered in Orlando, FL for Gartner’s annual Data & Analytics Summit. The three-day event was full of engaging keynote presentations, expert panels, 1:1 discussions, and interactive exhibits giving insights into the future of data and analytics, and where companies should aim next in their plans to harness and sustain the full value of their data.
As a Silver sponsor of the event, Syniti had the opportunity to hear from customers, partners, and Gartner’s analysts about the future of D&A strategy and enterprise data management. Here are some of our key takeaways from the week.
D&A Performance Must Tie to Business Strategy
Rita Sallam, Distinguished VP Analyst at Gartner and Kurt Schlegel, VP Analyst at Gartner, delivered the opening keynote addressing how data and analytics teams can better connect with stakeholders. According to a Gartner report released this month, less than half of surveyed D&A leaders (44%) reported their team being effective in providing value to their organization. In order to retain C-suite buy-in and deliver measurable results, Chief Data and Analytics Officers must make it a priority to make business stakeholders the hero in their value story. They can do this by connecting them with relevant data and analytics insights that will help them meet their KPIs and enable key objectives. Investing in these cases that speak to the business can help data leaders succeed in changing the organization’s culture to become more data-driven, with leaders across functions having a stake in the D&A vision.
According to Sallam and Schlegel, this will be critical for data and analytics leaders to advance their strategic objectives in 2023 and beyond. “D&A leaders are still not speaking the language of business, but are under intense pressure to demonstrate the value of their initiatives to other business peers and executives. […] We must operate through the lens of the impact we have on organizational outcomes.”
Wider Adoption of AI Will Change the Way D&A Teams Operate
AI and D&A teams already work closely together, but with this year’s explosion of generative AI tools like ChatGPT and their myriad use cases across the enterprise, Gartner predicts these teams will see increased collaboration – both with each other, as well as with cognitive science and other scientific disciplines.
With many organizations already on their journeys to becoming AI-native enterprises, AI and ML teams have become infused into the business. The rise of generative AI will call for new interdisciplinary teams to be built; these teams should include a range of talent such as computational linguistics, anthropologists, and neuroscientists, with Gartner anticipating an increase in diversity of staff skills by 800%. Due to this shift in the way AI teams are structured and staffed, Gartner estimates that by 2026, about 20% of the top data science teams will have rebranded as cognitive science teams.
The Value of Active Metadata Will Go Beyond Dashboards
Metadata was a recurring topic at this year’s conference as it has been playing a critical role in the evolution of data management. D&A teams must move from dashboards to data stories, and many are relying on metadata to personalize the user experience.
Gartner estimates that by 2026, half of analytics and BI tools will activate their user’s metadata to offer insights and data stories with recommended contextualized journeys and actions. This may not seem like news – after all, Gartner and many other research firms have been covering the topic of dynamic data stories and dynamically generated insights within analytics and BI platforms for nearly a decade now. However, recent advances in generative AI and large language models have enabled these platforms to scale those capabilities and give them an even more human-like understanding of situational context. This will be a key enabler in organizations’ ability to apply continuous analytics across where, how, and when their data is used. Rather than D&A users relying on a dashboard to interact with their analytic content, each user can have their own automated, dynamic data story generated for them each day based on context that takes into account the user’s role and what business objectives are important to them at that point in time.
You can expect to see a wave of organizations over the next year begin to adopt active metadata practices to accelerate automation, insight discovery and recommendations. As customers seek to harness the value of their metadata, the demand will also grow for augmented data platforms that are capable of accumulating and enriching metadata from a wide array of systems and users.
As Machines Grow More intelligent, Human Judgment Remains Invaluable
We are currently at an inflection point for generative AI, with ChatGPT being the catalyst that’s causing data and analytics users across domains to consider how the technology will affect not only their work, but society as a whole.
With a wave of major software vendors including Salesforce, Tellius, and Slack announcing generative AI integrations within the last few weeks alone, the technology is set to make a big impact on productivity. Although these function models present a significant advance, human judgment is still necessary in order to capture real business value. Careful training is required, and users may find that the models can deliver unacceptable results due to their black-box nature. D&A leaders must make an effort to “facilitate seamless human-machine interaction by trying a range of different prompts for the problem you are trying to solve,” says Arun Chandrasekaran, Distinguished VP Analyst at Gartner. “Different formulations of the same prompt which might sound similar to humans can lead to generations that are quite different from each other.”