Over the years, enterprise data strategies have been in a perpetual pendulum swing – from no strategy at all to way too much.
On one extreme, every analyst, data scientist or analytic application development team must find, access, translate and integrate all the data they need. This leads to some success with individual use cases, but burdens practitioners with excessive and redundant work while creating an unmanageable mess of data across the organization.
On the other extreme, data management professionals create a “foundation” of data for any and all uses, deploying one data domain at a time while attempting to identify every attribute and solve every data quality issue within each domain. This leads to projects that take too long, cost too much and deliver a fraction of the value that was expected.
Thus, the pendulum swings back and forth, back and forth.
The solution to this enduring dilemma is to find the middle way; that is, to carefully construct projects to focus directly on near-term value while contributing to an enterprise foundation at the same time.
An effective data strategy delivers (mostly) only the data needed for near-term use cases. To support these applications (preferably delivered by separate projects outside of the data team’s responsibility), the approach integrates only the data elements needed and solves only the data quality problems that affect the in-scope business objectives. And to simultaneously build an enterprise foundation, each small, focused data delivery also contributes its puzzle piece of data to fit into the larger enterprise data puzzle…
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