Dealing with data quality challenges post-merger. Are you prepared to navigate the complexities?
Curious about conquering data hurdles after a merger? Share your strategies for ensuring data quality and integrity.
Dealing with data quality challenges post-merger. Are you prepared to navigate the complexities?
Curious about conquering data hurdles after a merger? Share your strategies for ensuring data quality and integrity.
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One thing I’ve found helpful at the beginning of any system merge initiative is to establish the business objective. In my experience there are numerous way to merge 2 or more systems. The best scenario is to adopt 1 system as the common process, and the rest of system transform to follow the process. The most complex scenario is inclusive of several different processes in the target system. This approach usually ended up with inefficiency, low user adoption and high technical debt. It’s should avoid at all cost. In any case, initial data audit or data discovery process allow you to take inventory of data elements to simplify the scope. You can take action remove/retire unused data, transform similar data to common data structures.