Data quality needs to be a key objective across the organization, in all functions and at all levels. Everyone touching customer data must ensure its integrity. All customer contact points -- branch staff, customer service, collections, and credit originations -- must make it a priority. Is data quality considered in employee reviews?Contact Us
Formal data quality checks can occur at the point of data capture but typically are not built into older systems. A limited set of values may be required for some fields. Certain ranges may apply to others. Negative numbers may or may not be permitted. Checks against existing data for consistency makes sense but can be a challenge at many companies. For example, one top 10 bank transfers customers around to update their address for each product because its centralized customer data captures data from individual product systems but does not update them. In addition to being very unfriendly to customers it increases the risk that inaccurate data is maintained in the source systems.
Of course, a formal data strategy must consider how to understand customers and their behavior fully. Your data strategy must take into account how to profile customers (e.g., credit and demographic characteristics) and how to track key customer events (e.g., credit limit changes) and performance (e.g., delinquency and loss). Are you able to answer important business questions using the data you have today? If not, it's time to rethink your data capture work.
It also is important to reconcile your data to their source systems and the general ledger. This is the simplest check you can make. Are you including all customers in your analysis? And are you grouping them in the same way? Analytic systems must support the business and how everyone in your company thinks about it.
What data issues are you tracking today? What issues have been identified? Are the issues pervasive across products and functions? CDG can help you get a handle on data quality and deliver solutions to help you realize the value of your data. Ensuring that the data is credible is crucial to improved decision-making and the future of your business.