Courtesy of Screen Africa
MD at Master Data Management Gary Allemann has written the
following opinion piece about data management and data cleansing.
He writes: We meet with many IT teams that are trying to establish
a data quality culture within their
In most cases they share a common concern with us: "The
business people aren't interested in solving data problems!" "We
don't get buy in from senior management for data
The axiom 'You 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
After all, data management is not about the data. It's about
addressing the business issues caused by data and improving the
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
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
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
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 we 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