Data analytics best practices

Most organisations do not lack dashboards. They lack dashboards people trust and use. These practices are what we see in analytics teams that earn that trust.

1. Define metrics once

If revenue is calculated three different ways in three tools, every meeting starts with an argument. Put business definitions in a metrics or semantic layer — as code, version-controlled and reviewed — and have every dashboard read from it.

2. Design for a decision

Before building a dashboard, write down:

• Which decision it supports
• Who makes that decision, and how often
• What they would do differently depending on the numbers

If you cannot answer those questions, you probably need a conversation, not a dashboard.

3. Test your data like code

Analytics breaks quietly. Add tests for freshness, volumes, uniqueness and accepted values to your transformation layer, and alert the owning team when they fail — before the CFO notices.

4. Make self-serve small and curated

Self-serve fails when people are handed a raw warehouse. It works when they get a small set of well-documented, governed datasets and metrics, plus training and office hours.

5. Retire what nobody uses

Track dashboard usage. Archive anything not opened in 90 days, after checking with its owner. Fewer, better dashboards beat a sprawling catalogue.

6. Govern access without slowing people down

Apply row- and column-level security on sensitive data in the platform itself, so dashboards inherit the right permissions automatically.

7. Measure analytics like a product

Adoption, time to answer a new question, and hours of manual reporting removed are better measures of an analytics team than the number of dashboards shipped.

Analytics sits on top of everything else in the data stack. If the numbers do not agree, the fix is usually upstream — in data engineering or master data. Our data analytics team works across all three.