Have you been able to fix the data foundations of your organization, particularly with regard to data quality?
As a data business, quality is one of our most important value-adds and differentiators to our clients. In the financial services context, data quality takes on an added importance as many of the use cases that rely on our data are heavily regulated activities with sometimes painful repercussions for bad data or software glitches. I am proud to say we continue to make material strides in advancing quality.
First, we define several dimensions including accuracy, timeliness, and consistency. Second, our perspective is that data quality can be defined Inside-Out and Outside-In. Inside-Out essentially tracks how quality is controlled across our data supply chain. In the first instance, we are consumed with understanding the data extraction quality from over 20,000 raw sources (asset manager public filings, new sources, exchanges, and more) that come in a variety of formats (such as excel, PDF, images, websites) across dozens of markets.
We measure all deviations from expected values as the raw data flows from source document to final delivery, including all intermediate calculations. We can zoom-in at any stage of the supply chain to monitor the data on these three dimensions or zoom-out to look across datasets across the supply chain. Outside-in refers to the client perceptions with our data and our data’s relationship versus competitors. In a sense, data quality is more important when defined as people’s perceptions of quality than reality. To do this, we track all client support issues raised in Salesforce and tie those issues to specific datasets to identify trends and address proactively with clients in quarterly data quality scorecard conversations that are unique to each client. Finally, we run independent audits using benchmark data compiled from key global and local competitors to give us another perspective on how our quality compares.