Death by a Thousand Reports

In large companies, it’s common for analytics teams to be swamped with hundreds of requests every year to generate dashboards or reports for specific slices of data.

At one enterprise I worked with, over 60% of dashboards hadn’t been opened in six months.

If processes are left unchecked or undefined for the analytics team, this can quickly grow into a large, complicated mess. Preventing this requires structure, discipline, and a focus on clarity.

Imagine a marketing team that wants to know how many orders were created last week. They request separate reports for each customer. The analyst finds a previous report to copy, changes the customer filter, sets it on a schedule, and calls it a day.

This works once or twice, but over time, those reports evolve. Someone changes the criteria for what counts as an order, or adds a new filter for specific regions. These “variants” start to multiply. They carry the same metric name but mean slightly different things. Multiply this across multiple departments, and you end up with chaos: dozens of reports claiming to measure the same thing but producing different results.

This is how analytics teams die a slow death by a thousand reports.

To avoid this, organizations should put a few key guardrails in place:

1. Identify and document the most important and frequently used metrics that your analytics team reports on.

2. Implement a process for analytics team members to check this “top metrics list” before fulfilling new requests.

3. If a new request overlaps with something on the list, point the requester to an existing dashboard.

   • If a new visualization truly adds value, incorporate it into the existing dashboard instead of creating another standalone report.

4. Audit regularly.

   1. Schedule a quarterly “dashboard cleanup”. Remove unused reports, merge similar ones, and confirm that key dashboards still align with official definitions. Treat this as maintenance, not as an afterthought.

   2. Track report usage through access logs or BI audit tables. Anything unused for 90 days goes into an archive.

These steps sound simple, but they require consistent enforcement. Without it, report sprawl becomes inevitable.

Another challenge appears when there are multiple analytics teams, each tied to specific products or business areas. This setup might meet short-term reporting needs, but it often leads to data silos and unclear ownership. When boundaries aren’t defined, different teams build their own “sources of truth,” each slightly misaligned from the others.

For example, the marketing analytics team might define a “customer” as anyone who has ever made a purchase, while the product analytics team defines a “customer” as someone who has logged in within the past 90 days. Both teams report on “active customers,” but their numbers never match. Leadership loses confidence in the data, and meetings shift from discussing insights to debating whose number is correct.

There are three main ways to address the challenge of multiple analytics teams and siloed data:

• Restructure the analytics teams into a single unit.

  • This simplifies ownership but can be disruptive, since each team likely has its own culture, priorities, and workflows.

• Keep the product-aligned structure but make ownership explicit (and visible).

  • Team leaders should clearly define who owns which data, and they should encourage collaboration when dashboards span multiple domains.

• Adopt a hybrid model.

  • A central “analytics platform” or “data governance” team owns shared infrastructure, standardized metrics, and data definitions. Product-aligned analytics teams then operate within that framework to meet local needs. This structure preserves agility while maintaining consistency across the organization.

Regardless of structure, the cultural piece matters most. Analytics teams often equate volume of dashboards with visibility or value. Breaking that mindset, and rewarding clarity over quantity, is essential.

The goal should always be simplicity. A clear data ownership model and transparent reporting structure help everyone know where to go for answers. In this ideal (though difficult to achieve) state:

• End users know exactly who to contact for their data needs.

• Duplicate work is minimized, freeing up team capacity.

• Centralized visibility into requests ensures priorities are aligned.

• Increased confidence in reporting by leadership.

With well-defined processes and clear ownership, organizations can escape the slow death by a thousand reports.