Governance gaps made things worse. Siloed teams built their own reporting stacks with conflicting KPI definitions and mismatched refresh cycles. By the time those numbers reached executive meetings, the disagreements about which version of the data was correct consumed more time than the decisions themselves.
And then came the hunger for more. As digital transformation with AI matured across enterprise functions, demand for near-real-time data grew faster than the infrastructure could handle. Age-old BI tools weren’t built for that pace. The backlog of unmet analytical needs kept widening, and the ROI from years of enterprise AI solutions investment remained stubbornly out of reach.
THE BRILLIO WAY – CRED Framework
Most enterprises don’t have a data problem. They have a decision-support problem dressed up as one. Thousands of reports, competing KPI definitions, and a 30% adoption rate on tools that cost hundreds of millions to deploy. The gap isn’t technological. It’s philosophical.
Brillio’s approach starts with a different question: not “what reports can we build?” but “what decisions does this person need to make, and what’s getting in the way?” Design thinking, applied to analytics, means the decision maker sits at the center of every transformation conversation. That shift alone changes everything about how an enterprise AI solution gets scoped and delivered.
The CRED framework gives that philosophy a rigorous, quantitative spine. By applying machine learning models to existing report usage logs, Brillio classifies the entire reporting portfolio along four axes: Consolidate, Retain, Eliminate, and Develop. One large enterprise customer entered this process with over 1,500 reports. Usage data told a clear story before any human conversation happened.
But data only takes you so far. Brillio’s discovery workshops layer in persona mapping to validate what the numbers suggest. Who is this dashboard actually for? What’s their technical maturity? Where do they consume insights, and how much time are they willing to spend finding them? Those answers shape not just what gets built, but how it gets presented. Actionable, low-friction, and designed for the person who has a meeting in 20 minutes. That’s the standard Brillio’s analytics digital transformation consulting practice holds itself to.
CRED framework to rationalize existing report portfolio with a quantitative approach
Most enterprises don’t have a reporting problem. They have a trust problem, disguised as a reporting problem. Thousands of dashboards, conflicting KPI definitions, and analysts spending more time reconciling numbers than generating insight, that’s the reality Brillio consistently encounters when beginning an AI digital transformation engagement.
The CRED framework is Brillio’s answer. Built on machine learning applied directly to report usage logs, it classifies every dashboard in an existing portfolio across four actions: Consolidate, Retain, Eliminate, and Develop. The logic is deliberate. Before any enterprise AI solutions or modern self-service tools enter the picture, the data has to tell you what’s actually being consumed, and what’s quietly costing bandwidth without delivering value.
Consumption frequency, user reach, and report similarity scores drive the initial classification. Reports with overlapping content and divergent KPI definitions are flagged for consolidation. Those with near-zero usage and no clear owner become elimination candidates. High-frequency reports serving distinct personas get retained as foundation assets. And genuine white-space, unmet analytical demand confirmed through discovery sessions, surfaces as development opportunities.
The quantitative output doesn’t operate in isolation. Brillio’s persona mapping workshops overlay behavioral and qualitative context onto every CRED recommendation, anchoring decisions in how different users actually consume insights rather than how IT assumed they would. One enterprise entered this process with 1,500-plus reports and a 30% active usage rate. The CRED analysis gave leadership a defensible, data-backed path to a portfolio their teams could navigate, trust, and build on, without starting from scratch.
Sustainability pillars
Getting the CRED framework right is one thing. Keeping it right is another problem entirely. Most enterprise analytics programs collapse not because the initial transformation failed, but because no one built the infrastructure to sustain it. New data sources appear. Business processes shift. KPI definitions drift. And before long, you’re back to spaghetti.
Three pillars hold the model together over time. Continuous change management ensures that every stakeholder, from the executive sponsor to the frontline analyst, understands what a KPI means, how it’s calculated, and why it matters. That shared understanding is what prevents the duplication and conflicting versions of truth that derail even well-resourced digital transformation consulting programs.
Shifting the analytics focus from lagging indicators to leading ones is the second pillar. Predictive insights draw more users in. They create advocates, not just consumers, and those advocates actively resist the siloed behaviors that cause report proliferation in the first place. Enterprise AI solutions, including generative AI capabilities now embedded in modern BI platforms, make this shift more achievable than ever.
Teaching users to self-serve is the third, and arguably the most durable, of all. When analysts spend less time building canned reports and more time generating actual insight, the entire analytics culture changes. Self-serve capabilities compound over time. Each new user who can navigate and interrogate data independently reduces the demand burden on central teams and raises the organization’s collective data literacy. That’s what a sustainable analytics ecosystem looks like in practice.
