Master Data Management (MDM) is intended to provide a pervasive, consistent, enterprise view of a core set of master data. Not surprisingly, therefore, successful MDM programmes are highly dependent on data quality.
Data quality solutions, conversely, should provide fit-for-purpose data across the enterprise.
While MDM is certainly a driver for data quality, the benefits of data quality must extend beyond master data to encompass all business critical data, most notably, in order to support Business Intelligence, Compliance requirements and operational efficiency.
A good data quality solution must manage both master data and related transactions.
So where should you start?
Obviously, it depends on your business requirement.
To use an analogy. data quality is the horse to the master data management cart. For a while the horse will be able to carry the required load. Eventually, the load may become to much for the horse and a full Master Data Management solution may be required. In this case, the data quality "horse" will continue to provide the pulling power that allows the cart to function.
On the other hand, the expensive cart is fundamentally useless without a functional horse.
This position is summaroised by Aberdeen Research analyst, Nathaniel Rowe. "If you put MDM in place but you're using old, substandard data, you won't see much value from the effort," he said. "You'll have issues with the data if it isn't standardized."
"If you only have the budget to do data quality, that's more important, but keep looking toward the horizon for the next step,"
Data quality should not jut be a tick box in the MDM stack. Data quality management is a complex problem that is made worse when multiple sources of data are consolidated for MDM. Data quality should be assessed independetly of MDM to ensure that you have asolution that is appropriate for your needs.
We have delivered a number of MDM projects with varying architectures, depending largely on what could be leveraged within the existing environment.
The common factor in each project was to ensure that the underlying data was able to support the business requirement.
By blending a business and data focus we deliver incremental benefits that justify the exisitng spend and build the business case for additional phases.
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