Barclays cuts costs through improved data management

Master data management project has also led to fewer customer complaints and increased staff productivity

Barclays has put in place a master data management (MDM) initiative that it says has resulted in a host of benefits, including cutting costs, reducing customer complaints, reduced risk of regulatory fines, better customer data, increased staff productivity, better tackling of fraud and improved financial reporting.

MDM is a method of understanding corporate data and its use to better meet the needs of the business.

Saul Judah, head of data quality and profiling at Barclays, spoke at Gartner's Master Data Management Summit 2011 yesterday, to explain how the bank derived these benefits from simply having better control, understanding and governance of its data.

"Take a mineral water bottle example. The water is taken from the source, a spring, and it is extracted and filtered. The bottles are made in a factory, and then moved and distributed to shops worldwide," said Judah.

"But if water that you're drinking isn't of sufficient quality, what's the point? The value of all the effort put in by those extracting, filtering, packaging and distributing has been lost. With information, it's a similar analogy."

The project to put together a MDM strategy required an enterprise-wide effort.

To implement effective MDM, there needs to be a culture of continuous improvement across the organisation; maintaining quality data is a project that does not end, Barclays said.

Sponsorship for the project was needed from the top level of management and a team of six employees, a mix of part-time and full-time staff, was created to take on the work.

As a starting point for the strategy, Judah said Barclays needed to define a real business case, with objectives and targets to drive it.

"We looked at the organisation and started off with a 'heat map' to see and indicate where the biggest hot spots were; where the most important data was being used. That gave us a sense of priorities of what data to tackle first.

"Once that was identified, we carried out further work within areas to explore business processes we were using that consumed the data we were interested in. It wasn't picked at random, we needed to have a very focused, specific and clear methodology," he said.