The Unlikely Marriage of Data Warehousing & Marketing

Traditionally, CIO and CMO organizations have operated separately with different mandates. One was responsible for technologies that enabled company operations, while the other was responsible for marketing and advertising to drive revenue growth and brand awareness. However, the most successful marketing practices today have shifted away from a siloed “Mad Men” approach to one that leverages technology and science to complement and improve its art. Advances in data warehousing technologies have built the foundation for quickly and scalably unifying marketing data across many disparate sources — making CIO and CMO organizations even more inextricably linked.

Seasoned marketers are often familiar with a large rolodex of industry technologies — multi-touch attribution models, ad servers, DMPs, DSPs, CDPs — but less so on the foundational role data warehouses play in making their ad spend more effective. Where does website clickstream and cookie data live? What about data around how many customers redeemed the latest promotional code? How can we consolidate cost data from all advertising partners and build a dashboard to track QTD spend against budget? How do we combine our marketing data with critical customer attributes, like lifetime value, multi-product adoption, or even fraudulent activity, to better measure the true ROI of marketing campaigns? Whether marketers realize it or not, these sets of data are being collected, stored, cleaned, and consolidated in their company’s data warehouse. This means that firms need to break down silos between marketing and IT; they’re each other’s critical stakeholders and must closely collaborate to ensure effective ad spend.

​Cloud-based data warehouses like Snowflake are not only faster than many on-premise alternatives, but also much more efficient

Why should marketers care about something that traditionally belongs with an IT team? Historically, IT teams maintained on-premise data warehouses. If marketing wanted to store more customer data, they had to convince their IT team to order more servers, which was an expensive and time-consuming process. Marketing teams had to share a fixed amount of database compute power with the rest of the company, so someone else’s bad query could actually break reporting needed to track budgets and optimize campaigns. On top of all this, the company not only needed to employ an army of database engineers to maintain the underlying systems, but also manage large business intelligence teams to build and maintain codebases of pre-defined data cubes. Outside of the Fortune 500, companies rarely had the resources or scale needed to justify such a large undertaking.

As a result, marketers had no choice but to look for 3rd party platforms to consolidate and use their data. These platforms would provide out-of-the-box solutions that would sound too good to be true — and were. They couldn’t meet the inevitable client-specific business needs, and no client could look under the hood to ensure data was correct and being used appropriately; brands’ own data sets were essentially locked into black box platforms.

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