The Ins and Outs of Data Acquisition: Beliefs and Best Practices

Data acquisition involves the set of activities that are required to qualify and obtain external data — and also data that may be available elsewhere in an organization — and then to arrange for it to be brought into or accessed by the company. This strategy is on the rise as organizations leverage this data to get access to prospects, learn information about customers they already work with, be more competitive, develop new products and more.

Standardizing data acquisition can be an afterthought at the tail-end of a costly data journey — if it’s considered at all. Now we see companies starting to pay more attention to the finer, critical points of data acquisition as the need for more data grows. And this is a good thing because ignoring acquisition best practices and proper oversight will lead to a whole host of problems that can outweigh the benefits of bringing in new data.

The costly, problematic issues we see organizations grapple with include:

• Data purchases that, once brought into the organization, don’t meet the intended business needs.
• Data consumers don’t trust the acquired dataset.
• Acquired data duplicates what the company already owns (i.e., data that’s “hidden” in another line of business).
• No way to capture and manage the associated rich metadata, such as having an enterprise data catalog in place.
• Ineffective or nonexistent governance practices that aren’t set up to manage the data acquisition process.

Despite data acquisition’s inherent problems, when the process is thoughtfully developed and properly managed, it can facilitate a data purchase in a matter of weeks — not months, as it often takes some companies — that bypasses problems and delivers outcomes that meet or exceed business expectations.

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