Nine years ago, it seemed like data quality was primarily focused on algorithms and cleansing technology. Data went in, and the “best” solution was the one that could do the best job of fuzzy matching the data and cleaning more data than the other products.
Of course, no data quality solution could clean 100% of the data so “exceptions” were dumped into a file that were left as an “exercise for the user” to deal with on their own.
As a result, it was very much left to the user to handle, correct and amend in a spreadsheet , then someone would write an SQL query to write the corrections back into the database. Obviously, this is a hugely laborious and manual process, with little “effective”
governance on data quality.
The problem with this is that just in a few companies there are roles dedicated to stewarding and managing data integration effectively. Employers need to recognise that time and dedication is needed to ensure there is an effective way of consolidating
data, and making the appropriate changes. In the end, data quality is a business issue, supported by IT, but the business facing part of the solution has long been missing.
But that is about to change. There are solutions which can help to provide scalable ways to handle data quality management and integration processes. These solutions can provide a completely governed process for managing remediation of data exceptions by
business data stewards. This allows organisations to create their own customised process with different levels of review. Additionally, it makes it possible for business users to create their own data quality rules, describing the rules in plain language
– with no coding necessary.
Great data is not an accident. Great data happens by design. And for the first time, data cleansing can now be combined with a holistic data stewardship process, allowing business and IT to collaborate in order to create quality data that supports critical