By David Miron, Senior Manager of Business Process Optimization
Colors, layout, and chart selection matter, but they don’t separate a useful dashboard from one that gets ignored. After more than 40 years of serving retail and commercial clients across the procure to pay (P2P) lifecycle, we know the most important work happens before a single visual appears.
Published findings in the Gartner® Top Trends in Data and Analytics for 2026 report confirm our experiences, as well as our positions on critical data and analytics (D&A) imperatives, and what the next few years likely hold for AI and agentic automation adoption.
Let’s take a closer look.
The dashboard collective
The future of reporting will be built on better foundations constructed by experts across the organization, not just the business intelligence (BI) team. A dashboard is the surface layer of a much deeper system co-owned by a collective:
- Finance owns the budget
- Operations owns data activity
- Client teams own relational context
- Engineering owns pipelines
- BI owns complex-to-clarity
Misaligned definitions, undermaintained data fields, and missed process steps happen, but when any link in the co-ownership chain breaks, the dashboard suffers. Not because a BI developer did something wrong, but because the inputs were never fully aligned.
“Successful organizations will leverage a unified platform to drive business success through AI-first initiatives.” — Gartner 2026
Teams working disparately in silos form their own logic because they have (and solve) different needs. But an opportunity exists for teams to operate as part of the greater whole, building data sets with the collective dashboard in mind. If your data feeds the dashboard, you are part of what makes it work. When people across the organization take that ownership seriously, reporting becomes stronger, more reliable, and more useful.
Collaboration is especially important for common data and analytics challenges, like gap filling. For example, you find a question worth answering only to discover there’s no data behind it yet. The first step isn’t to build a report; it’s to build a holistic process for tracking initiation.
Alignment before all else
We sum up what the Gartner report says about aligning people on metric definitions, “semantics at the core." This is more than a technical concept. It’s also practical. To achieve semantics alignment, encode cross-functional decisions:
- Build a shared layer (where definitions, logic, and rules live in one place)
- Define revenue once
- Define productivity once
It's not glamorous work, but an aligned foundation persists as you build on top of it. As D&A leaders rightfully shift from legacy technology oriented to AI-centric mindsets, alignment plays a significant role in success. Neglecting this work for the sake of expediency or savings will cost more in the long run.
“Cost pressures won’t stop AI, but cutting corners on semantics and tool silos will stop AI value realization.” — Gartner 2026
Beyond semantics, aligning siloed tools is also a prerequisite to achieving optimal AI value.
Addressing tech silos
Gartner predicts, “By 2030, over 50% of enterprises will have leveraged a single platform that converges data, analytics, governance and agentic features to advance their AI-first strategy.” That doesn’t leave much time to ensure you’re a frontrunner, not a laggard.
When reporting, data management, and governance live on disconnected platforms, maintaining consistency is a real challenge, if not impossible. Consolidating onto fewer, well-integrated platforms frees up energy to focus on the insights instead of reconciling the differences.
Gartner sums up the practical outcomes of data and analytics tool unification nicely, stating it “can create the clarity needed to drive trust,” and “gives organizations the flexibility they need to become AI-first.”
For viability, investments in semantic and tool unification must show a return.
Data-driven, not just aware
Once foundational work is done and logic lives in one place, reporting can become predictive (vs reactive) and more meaningfully contextual through AI-generated summaries that consider deeper measures and dimensions in ways that traditional search queries never could.
Predictive reporting
Instead of only showing leaders what happened last month, predictive reporting can highlight what is likely to happen next month and where intervention may matter most. That shifts the conversation from "Why did we miss?" to "Where should we focus now?" But those models are only as credible as the inputs behind them, and that credibility depends on the organization contributing to the quality and consistency of those inputs.
Interactivity
Most people don't engage with a dashboard thinking in terms of measures and dimensions. They arrive with questions. Why is this number changing? What is driving the variance? What should I look at next? As reporting tools become more conversational through natural language queries and AI-generated summaries, the experience starts to feel less like navigating a user interface and more like following a line of thought or ideation.
Both capabilities depend on a strong foundation underneath. Predictive models inherit your historical logic. AI-driven queries inherit your business definitions. If those are consistent, the tools work well. If they are not, the tools simply accelerate the inconsistency.
“An AI-first enterprise can leapfrog its peers by using a strategic approach to maximize AI benefits through D&A, achieving better business outcomes.” — Gartner 2026
“Garbage in, garbage out” (GIGO) is real. Your body can’t thrive on junk food. Your vehicle can’t run smoothly on bad fuel. Your AI will never perform optimally on bad data due to misalignments.
Final thoughts
Reporting is moving closer to where decisions happen, embedded into applications and workflows, not just delivered through standalone dashboards. When insight appears at the right moment and in the right context, the gap between seeing something and acting on it gets much smaller. That said, proximity isn't enough. Tracking KPIs is not the same as driving outcomes. “All green” indicators can still leave leaders without a clear picture of what to do next.
The strongest reporting weaves data into a story. Why is something changing? What's behind the variance? Where should attention and investments go? When a report clearly and succinctly answers these questions, it stops being a scorecard and becomes a critical strategic and operational guide.
Dashboards must become part of a broader business intelligence system. We see it working with our retail and commercial customers. We believe the Gartner report substantiates this. But success can only be achieved with the whole organization recognizing that:
- Building an AI-first system takes more than a BI team.
- Each team owns a piece of what makes it work.
- Failing to build will be the difference between leading or chasing.
There's real value in being a champion of data, advocating quality and consistency across teams, and helping the organization figure out what’s worth measuring next, but the goal isn’t a better dashboard. It's a better decision on the other side of it.
That's not a technology trend, but it might be the most important one.
To read the full Gartner report or to learn more about how Cotiviti can support your business with audits and support across the procure-to-pay lifecycle, reach out to us at answers@cotiviti.com.
We’re here to help!
Gartner, Top Trends in Data and Analytics for 2026, by David Pidsley, Robert Thanaraj, Christopher Long, Ramke Ramakrishnan, Rita Sallam, 17 February 2026.
GARTNER is a trademark of Gartner, Inc. and/or its affiliates.
About the author
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David Miron is a senior manager of business process optimization, with a specialization in business intelligence and analytics. His work at Cotiviti centers on improving how data and reporting are used to support operational efficiency and decision making across retail and commercial organizations. |
