Dataflux says pressure is growing on firms to improve data quality, and its Data Quality Integration Solution 7 suite can help

The combination of a growing regulatory burden, the emergence of service-oriented architectures (SOAs), and accelerating adoption of data warehouses is increasing pressure on firms to address the quality of the data they hold.

That is the view of Tony Fisher, president and general manager of data management specialist and SAS subsidiary Dataflux, who said the last 18 months has seen a shift in companies' attitudes to data quality.

"All the regulations that have emerged in the past few years centre on how you report your data and what you report," he said. "So the ability to automatically check you haven't got data duplication or incomplete records has become more important." He added that SOAs that allow firms to flexibly move data between application components have had a similar effect, encouraging firms to ensure data is accurate and adheres to business rules regardless of which application uses it.

The growth of business intelligence (BI) reporting tools for creating reports based on data held in data warehouses has also cranked up pressure on IT directors to deliver accurate data, according to Fisher. "Firms have realised that decisions will be better if the data going into the warehouse is robust," he added. "So the ability to automatically cleanse and consolidate the data, check for anomalies, ensure rules are adhered to, and group together related data, such as customer information on everyone at one address, is increasingly attractive."

Fishers' comments follow the launch this week of an upgraded version of Dataflux's flagship suite, Data Quality Integration Solution 7.1. Dataflux said the new suite offers enhanced business rules capabilities so that firms can check if data complies with business rules as it enters applications rather than afterwards.

"There is a shift occurring from ad hoc data cleansing to transaction-based data cleansing," said Fisher. "The new product means users can't enter inaccurate data, such as an invalid salary range in an HR app, or duplicate customer data in a customer relationship management (CRM) system, so you tackle the data quality issues at source."