MARKETING EVOLUTION
joined with first – party data, which also looks different for every advertiser – email data, direct mail data, conversions data, web traffic data and so on.
So when we look at marketing data as a whole, we don’ t see one giant data set that’ s ready to go. We see dozens of fragmented data sets that are often incomplete and sparse in places. Some are at the national level, some at the ZIP code level and some at the DMA level. Historically, that has forced data engineers to spend enormous amounts of time building systems, writing queries or creating Python scripts to process data on an ad hoc basis for whatever downstream analysis is needed.
But if you can fit that data into a uniform structure, erase those issues of granularity and unify across the silos, the picture changes completely.
We can bust through the silos. We can break down the clean room walls and bring the data into one location where we can see Amazon Ads next to Google next to Meta, right alongside channels like CTV, Linear TV and OOH, all in the same language and with the same structure. All of a sudden, you’ re comparing apples to apples instead of apples to oranges and watermelons.
We have systems of record for customers in CDPs, for content in DAMs and for execution in MAPs, but there is no equivalent for marketing
90 July 2026