There is a common misconception about data-informed decision making. It goes that once we implement the right tools, and figure out how to analyze the data correctly, the data will automatically turn into insights and will translate to better business decisions. It sounds great in theory.
All marketers have to deal with essentially the same problem when managing their online campaigns and looking for the right attribution model: the data they need for evaluation is scattered across various systems, and collecting and collating it takes too much time. If they decide to use solutions intended to help them to handle this problem, they often find such solutions are not, in themselves, enough.
When I speak to people who are thinking about implementing BI, they are often overwhelmed by all the things they could measure. Many start by wanting to measure everything, which doesn’t necessarily help them. That’s because there’s an inherent cost in measuring things – everything you report and track creates an ongoing burden that your organization has to maintain. That’s why it’s important to be selective about what you measure from the get-go.
