Insights from Gartner Data & Analytics Summit Orlando 2023

It’s the place where dreams come true.

I’m talking about not just Walt Disney World, but also this year’s Gartner Data & Analytics Summit, which took place last month in Orlando at the landmark resort. Alation was proud to have been among the thought leaders at the annual gathering of data experts from around the world.

Here are some of the things we’re still talking about from the event, which offered ways to grant more data-related wishes — using marketplaces, products, mesh, fabric, AI, and active governance — than the genie of Aladdin’s lamp could accommodate.

Align your people, align your business

Lest we forget that data is only as powerful as the community of people who use it, the keynote address, by Gartner VP analysts Rita Sallam and Kurt Schlegel, reminded us that if there’s a lack of business alignment at your organization, it’s likely because of your culture, not your tech.

“The most common roadblocks to the success of D&A initiatives are all human-related challenges,” they noted, citing:

• Skills shortages

• Lack of business engagement

• Difficulty accepting change

• Poor data literacy throughout the organization

With D&A leaders under increasing pressure to show ROI, business alignment is critical.

Mesh or fabric? (And when the answer is “Yes!”)

Many D&A leaders were eager for guidance on which approach to take for their modern data stacks: mesh or fabric? Ehtisham Zaidi, Gartner’s VP of data management, and Robert Thanaraj, Gartner’s director of data management, gave an update on the fabric versus mesh debate in light of what they call the “active metadata era” we’re currently in.

Both architectural approaches offer risks and benefits, but what’s notable about data mesh is its ability to offer data products, used for data-sharing use cases, that are:

• Consumption-ready (trusted by consumers)

• Current (kept up-to-date by engineering teams for agreed-upon SLAs)

• Approved for use (governed by data contracts and agreements)

These products, in turn, enable data marketplaces as collaboration platforms that allow:

• Data custodians to publish

• Data consumers to self-serve

• Data subject owners to approve and audit

• Product managers to manage the full lifecycle of the data products

So, data mesh “wins,” right? Actually, with Solomon-like wisdom, Zaidi and Thanaraj suggest a scenario where data fabric and data mesh work together — a Reese’s Peanut Butter Cup of data architecture, representing a “meshy fabric” scenario I presented last year.

The foundations of successful data governance

The state of data governance was also top of mind. Gartner VP analyst Saul Judah declared, to the surprise of probably no one, that the support for data governance programs has yet to be matched by their success.

To level-set, Judah defined data governance as “the specification of designation rights and an accountability framework to ensure appropriate behavior in the valuation, creation, consumption, and control of data and analytics” — and listed its 7 must-haves:

1. Accountability and decision rights

2. Trust

3. Transparency and ethics

4. Risk and security

5. Education and training

6. Collaboration and culture

7. Value and outcomes

Note that none of those foundations start with a platform or software; achieving governance initiatives requires the business alignment and attention to human needs I mentioned earlier.

Want more buy-in? Judah recommended that you:

• Demonstrate that governance is a way to get things done, rather than something that gets in the way.

• Assure stakeholders that governance is not a one-size-fits-all approach and that different kinds of governance satisfy different needs.

• Encourage engagement and ownership — governance is a team sport! — while enlisting stakeholder champions to help you modernize governance for the digital age.

Governance drives a bold new world at WoodmenLife

Kam Rokon, a data officer at WoodmenLife, a fraternal benefit society that operates a privately held insurance company for its members, joined me and former Gartner VP analyst Sanjeev Mohan to discuss how Alation helps the Omaha-based company manage its data and migrate it to the cloud.

Alation is helping WoodmenLife find all its data and prioritize it for migration into what Rokon called “a new, intentional landscape” that includes Azure, Snowflake, and Power BI. Rokon noted that “we’re also using it to figure out who’s going to own the data — all these subject matter experts that have a lot of information — and get it all into one place, especially as long-tenured employees leave the company.”

The most important element of this new landscape? Data governance. “I use the word ‘intentional’ because that’s what you have to do with your data,” said Rokon, “and data governance is the way to get there.”

He compared governance to the U.S. Interstate Highway System: If there weren’t signs telling you where you were headed and how fast you can go, no one would use the roads. Likewise, “when you’re not doing things like pulling data from every location, saving copies in different places, publishing things left and right, and updating code without documentation — that’s good governance.”

The active metadata helix

Indeed, automation was on everyone’s minds. Nowhere was this more clear than in Mark Beyer’s presentation, “The Active Metadata Helix: The Benefits of Automating Data Management.”

Beyer entreated the audience to stop designing data, and to start observing metadata to learn from human-data behaviors and automate alerts for when things change.

Metadata, Beyer argued, tells illuminating stories, offering “the narrative and evidence of real use cases, from real users and real requirements.” We couldn’t agree more. Indeed, one of Alation’s most vital capabilities, query-log ingestion, learns from how people interact with data to automate future data suggestions and guidance for newcomers.

Conclusion — and what’s truly “next”

Finally, although tech summits often focus on “what’s next,” it’s more vital than ever to consider not just that “next” cutting-edge innovation, but rather ways to future-proof enterprise data stacks against the “next” trend. Can your stack accommodate the next big thing?

In a world where all signs lean toward increased decentralization (think data mesh, for starters) to foster a data culture that encourages data literacy beyond just your data experts, you’ll need to think architecturally — the entire forest, not just a new breed of tree — to future-proof your stack for the long term.

That’s why I’m looking forward to what’s next.