Data warehouse

Each system knows only its own slice. A warehouse combines marketing spend, leads and closed revenue, so the return by channel becomes something you calculate rather than guess. It also creates accountability: a team can see which campaigns and segments actually perform, and everyone reads the same numbers.

• Start with the systems that hold your key decisions: the CRM, the marketing tool and billing.
• Load raw data first and reshape it inside the warehouse, so remodelling later never means extracting again.
• Agree what a customer, a sale and revenue mean before building reports. A managed warehouse can be set up in an afternoon, but those definitions take longer, and that is where an analytics engineer helps most.
• Build a handful of shared reports instead of letting every team query it its own way.
• Add a new source only once the current ones are trusted and used.

Business tools such as a CRM, a billing system and an ad platform are built to run daily operations, one record at a time. A data warehouse is built for the opposite job: reading large amounts of data to answer questions that cut across systems. Well-known examples are Google BigQuery, Snowflake and Amazon Redshift.

Data reaches the warehouse through a data pipeline, and it stays separate from the live tools, so a heavy report never slows down the CRM. The warehouse holds a copy. The original system stays the place where records are created.

Take the question "which channel brings customers who stay for more than a year?" The ad platform knows the click, the CRM knows the deal and the billing system knows how long the customer paid. In a warehouse that question becomes one query. Without it, someone matches names across three exports by hand.

• Buying a warehouse when a spreadsheet would still do. A few thousand rows from two tools rarely need one.
• Treating it as a dumping ground with no definitions, so the same word means three things.