AFTAs 2016: Best Data Management Initiative: Sell Side—Deutsche Bank

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While not a complete stranger to the American Financial Technology Awards winners’ circle, Deutsche Bank has been conspicuous by its absence since its inaugural AFTAs win back in 2008 when it won the best global deployment category thanks to its Bank g-WEB implementation.

But this year Deutsche Bank is back with a bang in what turned out to be one of the most keenly contested of all 16 end-user categories: best sell-side data management initiative, thanks to its Data Quality Management program underpinned by its Data Quality (DQ) Direct methodology. Given the prevalence of data management challenges at virtually all capital markets firms, irrespective of their size, level of complexity and location, it is understandable why this category received so many high-quality entries.

Deutsche Bank revamped its Data Quality Management program in order to centrally track bank-wide data-quality issues, a strategy that not only improved data quality across the bank, but also started it down the road to transforming the way the entire organization thinks, feels, and acts with respect to its data. The bank’s DQ Direct process addresses data-quality issues in a structured, transparent, and holistic way.

Using CODA, a proprietary data-quality management tool built in-house on the IBM BPM platform, anyone in the bank has the ability to raise data-quality issues. They can also use the tool to understand how their issues relate to other existing data-quality problems. Groups are able to communicate and work together to resolve data-quality issues, as opposed to operating in silos. 

CODA allows the chief data office governance framework to prioritize initiatives based on the concentration and correlation of data-quality issues. Individual groups such as Regional Finance or Bank Regulatory Reporting are then able to track the progress of their data-quality issues through root-cause analysis, remediation, and resolution.

 

Deutsche Bank revamped its Data Quality Management program in order to centrally track bank-wide data-quality issues, a strategy that not only improved data quality across the bank, but also started it down the road to transforming the way the entire organization thinks, feels, and acts with respect to its data.
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