Why Most Analytics Teams Get Stuck—And How to Move Beyond Reporting

The Analytics Hierarchy

One of the most influential books in the analytics space is Competing on Analytics. A diagram in the book breaks business intelligence and analytics into layers, starting with basic reporting and moving up toward advanced, decision-driving capabilities.

What's striking is how accurately that model still describes most organizations today.

Where Most Companies Stall

In practice, the majority of companies operate almost entirely in the lower layers of the analytics stack:

• Standard reports
• Ad hoc reports
• Querying and drill-down

These layers are useful. They answer important questions like:

• What happened?
• How often did it happen?
• Where did it happen?
• How big was the issue?

This is descriptive analytics, and it's foundational. But it's also where many teams stop.

At this level, analytics is reactive. Data is delivered to leaders, usually in dashboards or slide decks, and the next step depends on someone noticing an issue, interpreting it correctly, and deciding what to do. The insight exists, but the action is manual and inconsistent.

Where Analytics Actually Drives the Business

The upper layers in the Competing on Analytics framework are where analytics becomes truly valuable:

• Alerts and exception detection
• Statistical analysis
• Forecasting
• Predictive modeling
• Optimization

These layers shift analytics from describing the past to influencing the future. Instead of asking "What happened?", the questions become:

• What is likely to happen next?
• What should we do about it?
• What is the optimal action right now?

This is where analytics stops being a reporting function and starts becoming part of how the business operates day to day.

Why Moving Up the Stack Is Hard

Many analytics teams struggle to move into these upper layers not because they lack data, but because of how they are staffed, tooled, and positioned.

Traditional BI skill sets are optimized for:

• SQL
• Dashboarding tools
• Static reporting workflows

Those skills are necessary, but they are not sufficient for forecasting, prediction, or optimization. Advanced analytics requires a broader toolkit and a different mindset.

Expanding the Analytics Toolset

To operate in the upper layers, teams need to think beyond dashboards and adopt tools that support deeper analysis and automation.

This often includes:

• Programming languages that enable flexible data manipulation and modeling
• Statistical and machine learning libraries for forecasting, prediction, and experimentation
• Frameworks for building repeatable, production-grade analytics workflows
• Automation tools that can trigger downstream actions based on analytical results

The key shift is that insights should not just be visible—they should be operational.

From Insight to Action

One of the biggest gaps in most analytics organizations is the handoff between insight and action.

A dashboard might show rising risk, declining performance, or emerging anomalies. But unless someone is actively monitoring it and knows exactly what to do, the insight often goes unused or arrives too late.

When analytics is paired with automation, that gap narrows dramatically:

• Forecasts can trigger alerts
• Predictions can route work to the right teams
• Models can prioritize cases, customers, or opportunities automatically

In these scenarios, analytics is no longer something you look at. It is something that actively shapes decisions.

Automating Decisions, Not Just Reports

The real leap happens when organizations stop thinking about analytics as a reporting output and start thinking of it as a decision engine.

This does not mean removing humans from the loop. It means using data and models to:

• Surface issues earlier
• Reduce decision latency
• Apply consistent logic at scale
• Free people to focus on higher-value judgment calls

When analytics is embedded directly into workflows, the business benefits even if no one opens a dashboard that day.

The Role of Modern Analytics Teams

Analytics teams that deliver the most value tend to:

• Work closely with operators, not just leadership
• Build custom solutions instead of relying solely on off-the-shelf dashboards
• Treat models, forecasts, and alerts as production assets
• Measure success by business outcomes, not report adoption

This often requires teams to evolve their skills, their tooling, and their relationship with the business.

Final Thought

Reporting is not the finish line. It is the starting point.

The organizations that truly compete on analytics are the ones that move beyond describing the past and start shaping the future. They use advanced analytics and automation to embed intelligence directly into how decisions are made.

When you automate the decision, not just the report, data actually drives the business.