Implementing a data strategy that encompasses all aspects of data management is not advisable as it can take considerable amount of time, money and resources. Whereas the approach to begin small, have focus even on a single data domain, and take the most critical business needs into account, has a proven track record as reported by many organizations.
Data strategy has to be driven by business strategy which can be best understood by focusing on business and infrastructure challenges thereby limiting the scope of data strategy. The implementation of the data strategy must be time bounded and with a pre-defined quantifiable criterion to measure the success. Additionally, define mutually exclusive use-cases to achieve the above defined business challenges.
Mutual exclusivity is vital to progressively measure the success as it helps to dismantle the ultimate business challenges into sub-tasks thereby giving a much clearer picture towards progress.
The example below shows the step-by-step break-down of above approach towards data strategy leading to a successful data-driven business transformation. Example shows the business challenge of increasing sales by 10%, which is then broken down into mutually exclusive use-cases such that the progress of each use-case can be independently measured. Once the use-cases are identified, their respective data domains can easily be isolated and implemented within a foreseeable period of time.
