Data Intelligence Monthly
Issue 11 - Sep 2026
Welcome to the eleventh issue of Data Intelligence Monthly. In this issue, we will discuss an interesting phenomenon faced on the road from data to decisioning, and the next evolution of analytics.
Data Intelligence: Why More Analytics Isn't Producing Better Decisions
Organizations have never had more access to data. Dashboards are everywhere. Reports are automated. Data warehouses are expanding.
Self-service analytics has put visualization and reporting capabilities into the hands of more employees than ever before. And yet, a fundamental problem remains: More data does not necessarily produce better decisions.
In many organizations, analytics has become exceptionally good at describing the past—but considerably less effective at helping leaders determine what to do next.
The next evolution of analytics is therefore not simply about producing more reports, more dashboards, or more sophisticated visualizations. It is about closing the gap between information and action.
The Analytics Paradox
Consider the typical analytics cycle: Data → Report → Dashboard → Insight → Meeting → Decision
The problem is that the process often breaks down somewhere between Insight and Decision. A dashboard may reveal that sales are declining. An analysis may identify that customer acquisition has slowed.
A report may show that operating costs are increasing, but knowing what happened is not the same as knowing:
Why is it happening and is it likely to continue?
How significant is the issue and what factors are driving it?
What will happen if nothing changes and what actions could improve the outcome?
Who needs to act?
This is where traditional business intelligence begins to reach its limits.
The objective of modern analytics should not be to create an organization that is better informed.
It should create an organization that is better equipped to decide.
From Reporting to Intelligence
The evolution of analytics can be viewed as a progression:
Reporting → What happened?
Traditional reporting provides visibility into historical performance. This, of course is useful, but insufficient.
Analytics → Why did it happen?
Analytics adds context and explanation. The organization moves from simply observing performance to understanding it.Intelligence → What is likely to happen next?
This introduces predictive and forward-looking capabilities. The organization moves from explaining the past to anticipating the future
Decision Intelligence → What should we do? The critical step!
Decision intelligence connects analytical insight to an actual business decision. This is where analytics begins to operate as part of the business—not simply as a reporting function.
The Missing Layer: Decision
Many organizations have invested heavily in their data infrastructure but comparatively little in what could be called the decision layer.
The data platform answers → What data do we have?
The reporting layer answers → What happened?
The analytics layer answers → Why did it happen?Emerging neighbourhood opportunities
The predictive layer answers → What is likely to happen?
The decision layer asks → What should we do about it?
That distinction is important. A sophisticated dashboard that identifies an emerging customer-retention problem is valuable.
A decision-support capability that identifies the problem, determines which customer segments are most at risk, estimates the potential impact, identifies the likely drivers and recommends where intervention should occur is considerably more valuable.
Building an Analytics-to-Action Operating Model
Moving from analytics to intelligence does not require replacing every existing dashboard or investing in technology for technology's sake. It requires redesigning the analytics operating model around the decisions the organization needs to make.
A practical model consists of seven connected stages:
DATA—Reliable, accessible and appropriately governed data.
INFORMATION—Consistent metrics, definitions, benchmarks and business context.
INSIGHT—Identification of trends, relationships, anomalies and performance drivers.
INTELLIGENCE—Forecasts, predictions, scenarios, risks and opportunities.
DECISION—Clear choices supported by evidence and analytical context.
ACTION—Recommendations embedded into workflows, processes and accountability.
VALUE—Improved revenue, reduced cost, lower risk, better customer outcomes or stronger operational performance.
The critical insight is that value is created at the end of the chain—not at the beginning.
The Executive Test
Every significant analytics initiative should ultimately be able to answer one question: What decision will this analysis change?
If the answer is unclear, the organization may be building reporting rather than decision support.
A second question should follow: What will someone do differently because of this insight?
These questions fundamentally change how analytics products are designed.
Instead of asking: “What data can we put on the dashboard?”, ask: “What decisions are we trying to improve?”. Think of it as looking backwards.
Identify the decision → Determine the information → Determine the analytical signals → Identify the data
Then design the experience around the decision—not the data.
Bringing It All Together
Data is an asset. Analytics is a capability. Intelligence is an advantage.
But action is where value is created.
Organizations should therefore stop measuring the maturity of analytics solely by the number of dashboards deployed, reports automated, or data sources integrated.
A more meaningful question is: How much better are our decisions because of our analytics?
That is the real measure of analytics maturity.
The future of analytics isn't about giving people more information.
It is about helping them make better decisions—faster, with greater confidence, and with a clearer understanding of what to do next.
If you are interested in discussing, planning, or developing your data analytics strategy, please contact us for a free 30-minute consultation.