Informatica Data Quality

These days, savvy business executives understand that a report or analysis on their customers, markets, products or anything else is only as good as the data used to compile it. There is always a risk that the data used in the report may not be of sufficiently high quality. Similarly, business partners realize that integrating their systems with another company’s will only add value if the data flowing through the integration meets the required standards.

So more and more the message that data consumers are giving to data providers is: “before I accept this data from you, before I use it in my decision making processes or write it into my systems or pass it on to another party, prove to me that this is high quality data. Prove to me that the data is fit for purpose.
Even in highly regulated industries such as finance or utilities it has long been recognized that, while industry participants may diligently collect all of the required data from their customers and partners or on their business transactions, there is no guarantee that the quality of data collected is sufficient to effectively achieve the aims of the compliance regime. As a result regulators are expanding compliance legislation to include specific requirements around proving the quality levels of critical data.

So for data quality managers, this introduces a new dynamic – no longer are they analysing and measuring data quality simply to enable them to drive their own data quality improvement initiatives, but also to meet the demands of a wide range of data consumers. Essentially – wherever the data goes, some level of data quality measure or certification needs to go also.

To meet this need, data quality systems and processes need the capability to generate data quality scorecards or grades for any packet of data being delivered to a data consumer. The scorecard needs to rate the data against the minimum quality requirements of the consumer – i.e. whether the data is fit for purpose.

The important point here is that the data quality scorecard is not only specific to the data packet but also to the particular end-use of the data. A single packet of data going to multiple consumers may require multiple scorecards. As such data quality scorecards need to be generated at the point of handover (or point of integration) between the data provider and the specific data consumer.

This is why data quality measurement and scorecarding has become the number 1 priority and single biggest activity of many data quality programs.

For more information about data quality monitoring and scorecards see the Informatica white paper: Monitoring Data Quality Performance Using Data Quality Metrics with David Loshin