AI-powered analytics is everywhere right now. But the payoff? Not so much.
Two patterns show up again and again. The first is an “AI everything” backlog that expands faster than teams can deliver. The second is an insights bottleneck that still forces the business to wait in line for basic answers while analysts drown in ad hoc requests.
What follows is a practical playbook pulled from how experienced data leaders run their programs: how they prioritize, how they govern, and how they scale access without losing trust.
Self-Service Analytics And Data Democratization: The Foundation For Speed And Trust
Senior data leaders at high-performing organizations are converging on the same playbook: value-first prioritization, governed self-service analytics, and an AI-native foundation that speeds up decisions instead of simply generating more friction.
A quick definition of terms:
Self-service analytics means business users can explore trusted data without filing tickets.
Data democratization is the system that makes self-service safe and scalable: access, governance, shared definitions, and data literacy.
Play 1: Make value the gate, not the afterthought
When organizations say they want AI, what they usually mean is they want faster decisions, lower cost, better customer outcomes, or new revenue. The fastest way to lose executive support is to greenlight work first, then try to “find the ROI” later.
At Ecolab, a global company where analytics demand outpaced team capacity, “good ideas” started crowding out work that actually moved the business. Anand Iyer, Senior Vice President and Chief Data Officer, kept the filter simple.
"The key is to be able to really identify initiatives that you can actually draw a straight line between the initiative and where it's impacting from a dollar standpoint."
Anand Iyer
SVP and Chief Data Officer, Ecolab
He also made the standard for “efficiency” explicit. “If you cannot point out how a specific AI initiative is either impacting the top line or the bottom line or SG&A, then it’s going to be very difficult to have a sustained conversation with any C-level leader,” Iyer said.
Dow landed in the same place from a different starting point. In a complex organization, the issue was not a lack of ideas. It was ensuring the right initiatives rose to the top and that someone owned the outcome. Chris Bruman, Chief Data and Analytics Officer, said the team tries to “put financial value against every idea that comes in.”
Then he added the part most organizations skip: accountability before the build. Bruman said he wants to know “who’s the senior leader” signing up so that when the work is complete, there is measurable value tied to the business.
The throughline is discipline: fewer projects, clearer ownership, and measurable outcomes.
What this looks like in practice:
Write the value hypothesis in plain language. Revenue, cost, risk, retention, or productivity. Pick one primary outcome and how you will measure it. If it is “productivity,” define what changes: cycle time, throughput, error rate, or hours saved per week.
Require a senior sponsor before the build begins. Waiting until the project is done to find the value owner is how good work dies quietly.
Baseline, ship, measure. Take a “before” snapshot so “after” is not a debate.
Treat “cool” as a tax, not a goal. If a project cannot show impact, it is not a pilot. It is a distraction.
🎧 Learn how to build business cases that actually stick and avoid the AI hype trap on The Data and AI Chief podcast.
Play 2: Treat Self-Service Analytics tools as a product, not a rollout
Self-service analytics often fails when it is framed as “we gave you a dashboard, good luck.” It works when teams treat self-service analytics tools like a product: built around real decisions, powered by certified data, and reinforced through enablement and feedback loops.
At Elevance Health, Robert Garnett’s team rolled out ThoughtSpot by starting where the pain was obvious: high ad hoc volume and a business group with “very, very low technical acumen.” He said the goal was to let associates ask questions “in a natural language manner and interact directly with the data,” with the right controls in place.
“We’ve reduced, I’d say well over 50% of the ad hoc volume that we had previously, which is something really exciting.”
Robert Garnett
Vice President of Government Analytics and Health Benefits Cost of Care, Elevance Health
He also called out a failure mode that kills adoption even when the tooling is fine. Garnett said he has “seen far too many dashboards” that get “accessed very little.”
In other words, if the product does not match the problem, and people are not enabled to use it, it becomes shelfware. The “after” is not more dashboards. It is fewer, clearer paths to answers built around repeatable decisions.
SharkNinja comes at this from an adoption-and-trust angle. When you ship fast across a wide product portfolio, analytics only matters if it lands with the people making decisions, not just the people building reports.
Elpida Ormanidou, Vice President of Analytics & Insights, put it plainly. “Value gets created at the time of consumption,” she said.
On governance, Ormanidou framed trust as something you operationalize, not something you publish once and forget.
“It’s not a policy or a set of standards alone,” she said. “This is embedded into everything that we do. Very stringent controls around it.”
What this looks like in practice:
Start with the highest pain, not the highest sophistication. Prioritize the decisions that generate the most back-and-forth, the most ticket volume, or the most costly delays.
Build certified pathways to answers. Data democratization is not “everyone connects to everything.” It is governed access, trusted models, and clear definitions.
Meet people where they are. If users think in business language, the experience should support business-language exploration, including natural language, without losing governance.
Design for iteration. Deploying and walking away kills adoption. A feedback loop with the business is what makes the system compound.
Play 3: Build an AI-native foundation that can move at business speed
Leaders described the bottleneck as foundational: if it takes too long to find trusted data and align on definitions, AI just makes the drag more visible.
At Dow, teams moved from scattered sources to an integrated hub that made multi-source questions practical.
On a churn use case that pulls signals from structured data and text, Dan Futter, Chief Commercial Officer, described the before-and-after. “Two years ago, there was no way we could have interrogated all those different sources,” he said. “Now we can do it quickly, with the help of AI.”
The “after” is speed with scope: more sources, more signal, less time lost to manual stitching.
Ecolab’s foundation has to support both internal performance and customer-facing products. Iyer described the mandate as building analytics and AI that help functions “optimize operations,” as well as customer technologies that “generate digital revenue.”
SharkNinja offers a gut-check that keeps this play honest: foundation work only matters if it improves decisions where work happens.
As Ormanidou put it, “value only shows up when people can actually use it.”
If it still takes weeks to find the right data, reconcile definitions, and stitch sources together, you are not operating in an AI-native way yet. You are layering AI on top of friction.
What this looks like in practice:
Invest in the unglamorous layer: shared definitions, reusable datasets, consistent access patterns, and performance that holds up under real usage.
Optimize for speed-to-question, not just speed-to-dashboard: AI-powered analytics only helps if people can ask, refine, and iterate quickly.
Treat reliability as a feature: If the business cannot trust the answer, it will not use it.
Make it usable where work happens: AI-native is not a tool. It is analytics embedded into workflows, so the moment of decision is the moment of insight.
Play 4: Build a data culture where analysts stop being a ticket queue
The endgame of self-service analytics is not replacing analysts. It is getting analysts out of low-value production work so they can do higher-complexity work that actually changes outcomes.
That shift shows up as a partnership model within Elevance’s work culture. Garnett emphasized building solutions “from a business perspective,” and he tied adoption back to enablement and ongoing collaboration. As he put it, “Analytics should be at the table, not a takeaway from the table.”
He explained:
“So I think analytics, when they’re sitting around the table with the business when they're making decisions or they're working through a problem, is a very different construct than traditional models where the business convenes, works through a problem, then decides well we need more data, or we need data to drive a decision here, go ahead and put in a ticket or seek additional data and bring it back.”
“So I think analytics, when they’re sitting around the table with the business when they're making decisions or they're working through a problem, is a very different construct than traditional models where the business convenes, works through a problem, then decides well we need more data, or we need data to drive a decision here, go ahead and put in a ticket or seek additional data and bring it back.”
The before is a queue of requests. The after is analysts spending more time on the work that changes decisions because routine questions are answered through governed self-service analytics.
Dow highlighted the same cultural mechanics, especially the role of enablement in scaling adoption. Bruman talked about providing support so teams “build the skills,” “gain the confidence,” and then “start to demonstrate the benefits.” That is what a durable data culture looks like in practice: not just access, but capability.
SharkNinja adds the trust layer. When more people can self-serve, governance cannot live in a document or a centralized team. It has to show up in how leaders work and how decisions get made. Ormanidou described it as something that needs to be “embedded into everything that we do.”
What this looks like in practice:
Redefine the analyst job. Fewer one-off pulls, more decision support, experimentation, and proactive insights.
Make data literacy part of the role, not optional training. Self-service analytics works when teams understand what they are looking at and how to act on it.
Operationalize trust. Access controls, certified definitions, and a culture that treats data use as a responsibility.
Measure adoption like a product team. Are people returning? Are they finding answers faster? Are debates about “whose number is right” going down? Are decision cycles shrinking?
A quick data democratization checklist to pressure-test your strategy
If you are trying to drive business value with analytics and AI, use this as your gut-check:
Can every major initiative state its value in one sentence and tie it to a metric the CFO recognizes?
Do you have governed, business-friendly paths to answers so self-service analytics does not become chaos?
Is your foundation AI-native enough to support real iteration speed?
Are you building a culture where value shows up when decisions get made, not weeks later in a status update?
The bottom line
If your goal is data democratization that actually speeds up the right decisions, start with one business area that has high question volume, clear value metrics, and repeatable decisions.
Then treat self-service analytics like a product: governed datasets, business-first experiences, training, and a feedback loop supported by the right self-service analytics tools.
If you want to see what that looks like in your environment, request a demo and bring one high-friction use case you are tired of ticketing.
Frequently asked questions
1. How do I get executives to approve AI projects when they want guaranteed ROI?
Turn “guaranteed ROI” into a go/no-go gate: one measurable outcome, one accountable Executive Sponsor, and a decision date to scale or stop. Leadership support follows when the work has a clear owner and a measurable impact.
2. What is self-service analytics, really?
Self-service analytics means business users can ask and answer questions on trusted data without filing tickets, while governance stays intact. It means faster answers for the business, less ad hoc drag for analysts, and fewer debates caused by competing numbers.
3. What is the fastest way to prove self-service analytics works without creating data chaos?
Pilot one certified path to answers, not broad access. The common failure mode is “dashboard shelfware” without enablement. Governance keeps self-service from turning into competing numbers.
4. How do I convince my analytics team self-service will not eliminate their jobs?
Make the role shift explicit. Self-service absorbs repetitive requests so analysts can move into higher-complexity decision support. The point is better decisions where work happens, not fewer analysts.
5. How do we handle messy external partner data without lowering standards?
Separate exploratory from certified. Excluding external and community data leaves value on the table, but the guardrail is discipline. When AI amplifies analytics, weak inputs do not stay small. Only data that meets agreed thresholds should graduate into governed reporting.
6. What should we look for in self-service analytics tools?
Look for tools that make answers easy to get and hard to misinterpret: governed access, clear definitions, and an experience that supports iterative questions in plain language. That is what turns data democratization into everyday behavior instead of a dashboard graveyard. Want to see it in action?
7. How do I measure data democratization beyond adoption numbers?
Measure whether decision-making gets faster and cleaner, not just whether people logged in. Look for shorter cycles from question to decision, fewer recurring requests that should be self-serve, fewer “whose number is right” debates, and more decisions made on shared metrics.
