Ensuring you are utilizing, storing, and capturing the highest quality data for in financial services is a challenge, but also a necessity for organizations on their way to data-driven transformation. Having high quality data can mean anything from including the right offer to drive engagement for customers or ensuring that your organization stays in compliance with ever-changing regulatory requirements. On the flip side, poor data quality could mean denying somebody a home loan because the data used was somehow wrong – perhaps it became corrupted somewhere during creation, transmission, storage or analysis.
Needless to say, data quality is a key indicator to business success for all organizations, especially those within financial services industry. In fact, Gartner measures the average financial impact of poor data on businesses at $9.7 million per year. This of course doesn’t include lost of reputation, missed opportunities, and higher-risk decision making as a result of low confidence in data.
Accurate data helps you understand customer segments, improve how you manage risk, and make smarter decisions. But getting the most accurate customer data in a user-friendly format can undoubtedly be a challenge and something that organizations continue to struggle with.
All of this begs the question: What is really needed to ensure you’re using the highest quality customer data available? We decided to look to Forbes Insights: The Data Differentiator white paper for some answers. Here are four conditions that the experts at Forbes believe indicate good, suitable data for organizational use:
Data must of course be accurate and timely, but as Anthony Scriffignano, Dun & Bradstreet’s chief data scientist notes, ““The whole concept of accuracy is really nuanced, and it has to be taken in the context of the particular attribute that you’re talking about.” To start, though, accurate data should be fresh and must properly reflect customer transactions and purchase history.
If a bank can’t accurately identify its highest-value (or lowest-value) customers, then it could easily miss valuable opportunities to build the right customer relationships and offer products that make sense. Therefore, completeness of a data set needs to be constantly evaluated.
The ability to standardize input in a correct format, especially in the presence of input errors is important. Inputs and format names can vary widely between data sets. Finding meaningful ways to compare data sets that may differ is key.
Data sources must be authoritative, credible, and fit for purpose. The source of your data must have credibility that provides information in a complete fashion. Without credibility, output will be limited because it won’t be based on the best data.
There is of course more to the picture than these four components, but the list here offers a good starting point for those looking to enhance the use of data within their organization. For more on how you can ensure your organization is utilizing the highest quality data, download Forbes Insights: The Data Differentiator here.