Having data is not enough. Analyzing data is not enough. Without a clear focus on the business objectives, even having a data strategy may not be enough.
An ability to correlate analytics programs with the success or failure of outcomes is another distinguishing factor between best-in-class organizations and the rest. Of leading organizations, only 2% reported a lack of visibility into the outcomes of their analytics programs. This figure rises to 18% for “lagging” organizations (about 10% of the total).
Being transparent about the metrics on your data programs can be a significant help in driving reciprocal improvements in operations, by creating an environment where successes are known — and by encouraging shifts in strategy that were taken for the right reasons.
From data collection to data strategy
Having data is not enough. Analyzing data is not enough. Without a clear focus on the business objectives, even having a data strategy may not be enough.
As with any good strategy, knowing when and how to apply it, when to change it, and how to measure its success are all critical for organizations to move from being ones that do analytics, to being truly analytics-driven.
Summary
A data strategy that yields value-creating insights begins with a working hypothesis and includes the ability to correlate results with the success or failure of outcomes.