Why 'just build a dashboard' doesn't work
The single most common mistake we see in data projects is building dashboards before the data is ready. A dashboard built on unreliable, inconsistent data doesn't just fail to solve the problem — it makes it worse, because now leadership is making decisions based on numbers nobody trusts.
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The architecture that makes it possible
A single source of truth requires three layers: ingestion (all data sources feeding into one place), transformation (a governed layer that defines what every metric means), and serving (a BI layer that reads from the transformation layer). Skip the transformation layer and you'll have a data lake. It looks like a single source of truth but it isn't — it's just a single place where all your chaos lives.
Defining metrics is the hard part
The technical infrastructure is usually the easier problem. The harder problem is organisational: agreeing on what 'revenue' means, whether that's gross or net, which date counts as the sale date, and how refunds are handled. These decisions need to be made once, documented, and enforced in the transformation layer. dbt is excellent for this — metric definitions live in code, are version-controlled, and are tested automatically.
Ankit Parihar
Founder & Principal Data Consultant · Paris, France