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Master Data Management meets with many IT teams that are trying to establish a data quality culture within their organisations, writes Gary Allemann, MD at the company. 
In most cases they share common concerns:
* “The business people aren’t interested in solving data problems.”
* “We don’t get buy in from senior management for data governance.”
The axiom of people who “can’t see the wood for the trees” is apt when considering their approach to data management and data cleansing. They see so many issues caused by poor data that they assume that these will be obvious to everyone. So the problem that they are trying to address becomes “poor data quality” – rather than the business issue (or issues) that the data is affecting.
This common mistake has resulted in business people questioning the business value of data management projects, with sometimes a lot of money being spent but ultimately they do not yield any results and are perceived to be pointless. This is more than often due to data management projects that are frequently driven by technologists who do not speak the “business language”.
After all, data management is not about the data. It’s about addressing the business issues caused by data and improving the business outcome.
It is therefore vital to partner with a data management professional that understands the business language and how the technology can deliver results that can be linked back to business issues.
For example, a data person may ask for budget to “improve address data quality”, assuming that this is the business driver. After all, address inaccuracies are the cause of many business issues – ranging from returned mail, to the inability to trace and collect bad debts, to the increased risk associated with non-compliance.
In each of these cases, the business driver is not better address data quality. A project that asks for budget to “reduce overall debtor’s days by x%”, or to “cut the volume of returned mail by y%” is far more likely to get attention, and budget, from business than a project intended to “improve the accuracy of addresses”.
This approach also focuses the attention of any implementation on the specific end goal, or goals, ensuring that unnecessary effort and money is not expended cleansing data for the sake of it.
Of course, this approach can lead to duplication of effort. Do users need multiple projects, each with a different end-goal all working on the same data?
Pragmatic data governance assists to ensure that overlapping business goals are addressed by the same project, rather than having many tactical projects that may impact negatively on each other, or waste resources repeating a task that has been delivered by another project.
Experience can help to link the business and IT goals creating that all important business buy in and support.