What is data interoperability?
Where logs originate can impact their efficacy—since this is not something with industry standardization, different exchanges and DSPs often have different data dictionaries, ways of defining log-level vs aggregate, and of course, different ways to implement the findings such as an improved bidding algorithm.
Advertisers and agencies have traditionally leveraged the DSP report and logs. While it makes it easy to use that DSP to optimize, it also locks a buyer to that technology and makes it difficult to switch. One solution is to get the data from another source like an SSP, that would also offer a fuller picture of success and lost bid. Another is to develop your own data definitions and preemptively align it with both the data and controls offered by any buying platform you may way to use. Here, it’s important to assess how often the available data changes—are there 20 release notes per year that change the attributes included? Or can you expect stability over a 3 year timeline? Do the available bid optimization controls line up with the dimensions you are adding to your optimization engine? Are these likely to change?
This can all be mitigated by using a common set of attributes and calculations, and increasing the interoperability of data. To be interoperable, a product—in this case the data—must have clear interfaces and be able to work with other systems without restrictions. More specifically, a buyer should demand that their data is portable, that the dimensions are not specific to the system sending it to them, and in general that it is easy to use anywhere.
With the growing availability of log-level data, better understanding of bidstream dimensions, and a fresh approach to broken addressability and attribution workflows, we expect to see more innovative use cases for log-level data.