This is the first post in a series on the topic of Master Data. I hope you’ll enjoy the series.
We are increasingly more connected in may different ways. The amount of data we are exposed to seems to have exploded. Organizations need to be as agile as they can to survive in this competitive eco system or in trying to get the competitive edge in their industry. The data organizations are exposed to is largely redundant and sometimes even conflicting. However we do recognize that information sharing and operational collaboration is and important factor to be(come) succesful.
Business applications till this very day are designed to meet specific business needs. Due to this vertical focus, these verticals all need their own: Data definitions, dictionaries table structures fitting the functionality they provide. All of it designed with the business vertical in mind. As a result the “enterprise” is a compositions of applications relying on disparate sets of data. Data quality control mainly resides with line of business management (The vertical).
If the data described is only used as fuel for the operation within the vertical the likelihood of running into issues is very low. However if there is a desire to transform data into information and actionable knowledge that’s where the challenge starts. This is where it becomes apparent that the “enterprise” needs a consistent view on its data drawn from many different data sets.
To use information for both operational and analytical processes the “enterprise” must be able to clearly define its business objectives and concepts. This has to be done before they will be able integrate its data consistent view across the organization.
The real master data challenge starts at that exact point. How to organize and enterprise view and govern the data quality of the business (data) objects. Once successfully done these optimized information objects can then be used to pursue operational, analytical and strategic business objectives across the enterprise.