What is the difference between Business Intelligence and Decision Intelligence?

Business Intelligence (BI) outputs information through dashboards and reports, while Decision Intelligence (DI) outputs actions—recommended next steps, decision policies, and sometimes automated decisions with guardrails. BI success is measured by adoption, whereas DI success is measured by outcomes like speed, consistency, risk reduction, and measurable business impact. Gartner defines Decision Intelligence as a discipline that explicitly engineers how decisions are made and how outcomes are evaluated and improved via feedback.

Why does BI quality matter for Decision Intelligence initiatives?

Decision Intelligence is only as strong as the BI layer underneath it. If your BI environment has duplicated reports, drifting metric definitions, stale data refreshes, unclear ownership, or loose permissions, DI becomes a force multiplier for inconsistency and disagreement. Certified datasets, endorsed dashboards, and standardized metrics are the building blocks DI relies on to function effectively.

How does BI sprawl impact Decision Intelligence adoption?

BI sprawl creates a discoverability failure that undermines Decision Intelligence efforts. When teams can't quickly find, narrow, and prioritize trusted assets, governance stays reactive and decision-making suffers. Datalogz helps organizations address this by governing over 720,000 BI assets and identifying more than 1.4 million optimization issues across customer environments, ensuring decision-ready assets are easier to trust and scale.

What tools help prepare a BI environment for Decision Intelligence?

Organizations need BI observability platforms that monitor security, performance, governance, and cost across their BI estate. Datalogz provides this foundation by tracking usage, identifying unused or duplicate content, and surfacing BI sprawl across platforms like Tableau, Power BI, and Qlik. This creates the certified, reliable data products that Decision Intelligence systems depend on.

What are the hidden costs of messy BI environments?

Messy BI environments impose an operational tax through duplicated reports, rising platform costs, and reactive governance. Beyond direct costs, there's a discoverability failure that slows decision-making and erodes trust in analytics. Datalogz customers have identified over $8.2 million in avoidable BI spend through cost management alerts alone, demonstrating the significant financial impact of unmanaged BI sprawl.

By Datalogz in Data Dive — Mar 29, 2026

Data Dive #54: What We Heard at Gartner D&A & FabCon💡

From presenting at Gartner with PepsiCo and Georgia-Pacific to FabCon and SQL Saturday, we’ve been busy. And our next event is in NYC!

Earlier this month, Logan Havern, Founder & CEO of Datalogz, joined Matt Robuck, VP of Data and Analytics at Georgia-Pacific, and Richard Martin, Senior Director for Strategy and Transformation – Data, Analytics and AI at PepsiCo, for a theater session at the Gartner® Data & Analytics Summit 2026.

The conversation was blunt. And the throughline was one that data leaders keep circling back to, no matter how large or sophisticated the organization: you cannot build on top of a foundation you haven't cleaned up.

Here's what we're seeing across the environments we manage: a 77% year-over-year increase in reports and dashboards from 2025 to 2026. Dataset reuse sits at 14%. Roughly 80% of assets are unused. AI didn't create this problem. It's accelerating it.

When Logan asked what the biggest risk was to making that transformation happen, both Matt and Rich landed on the same two things: context engineering and change management. Getting metadata right so agents know what to trust. Getting people to actually use what you build.

Both are BI problems before they're AI problems. The organizations that win aren't the ones with the most ambitious AI roadmap. They're the ones who govern first. Then automate.

📅 A Busy March on the Road

March was a full one. Gartner® Data & Analytics Summit, Microsoft FabCon, SQL Saturday Atlanta, and our own Data Leader Kaffeeklatsch. Four events, different rooms, same reckoning: data leaders are finally asking what's actually underneath their BI investment.

The question has shifted from "how do we modernize?" to "how do we actually control what we've built and prepare it for AI?" Everywhere we went, data leaders kept focusing on the following themes:

AI will amplify whatever already exists in the analytics estate, including duplication and low-trust content.
Semantic consistency is becoming essential for reliable enterprise AI.
Cost optimization is now part of analytics strategy, not just an admin exercise.
The organizations that win with Fabric and AI will be the ones that rationalize before they scale.

🤖 State of AI in BI

AI is changing BI fast. But the biggest shift is not just in dashboards or copilots. It is happening at the consumption layer, where business users expect AI to build reports, surface insights, and help drive decisions.

In our second annual report, State of AI in BI 2026, featuring insights from 70+ data leaders, we explore what that means in practice: why AI compounds BI sprawl, why governance remains a major challenge, and why the semantic layer is becoming essential to trusted AI in BI.

Download the report now!

🗽 Future Frontiers: New York City, April 22

We’re back! On April 22nd, Datalogz and co-sponsor definity are hosting the next Future Frontiers dinner in NYC. It's a curated, invitation-only evening for CDOs, CIOs, and senior analytics leaders at Fortune 1000 enterprises.

Expect a fireside chat and dinner built around one question: how do you rationalize a sprawling BI estate without disrupting the teams that depend on it? It's the tension every data leader is sitting with right now. More platforms, more stakeholders, more AI ambition, and a growing pressure to show that the foundation underneath it all is actually sound.

RSVP Here