In my previous post, I talked about how a bank realized that data quality was central to some very basic elements of its initiatives, such as know your customer (KYC), customer on-boarding and others. In this blog, let’s explore what this organization did to foster an environment of data quality throughout its customer data.
The Process
In the initial scope, we outlined three different types of profiles – identity, demographics and communication. We decided to educate the business users and IT on the data quality assessment approach so that they feel comfortable in repeating the process themselves. Success of data quality, master data management (MDM) and data governance initiatives largely depends on how quickly organizations feel comfortable in embracing these ideas and owning the responsibility of executing them.
As a result, we made sure everyone involved in the project understood the high level functional capabilities and the ease of use of SAS data management solution. Data management projects often suffer from a lack of buy-in because end users find these tools far too complex. Without making data management and quality processes business-friendly, the evolution of keeping data consistent for business purposes stops after taking first few steps.
The Results
Business friendly approach helps
The bank received the results of the data quality assessment in business-friendly manner, and the findings were consistently linked to the business processes mentioned above. Here are a few examples of data quality issues found after conducting a quick assessment of customer on-boarding data.
- A majority of customers names were in non-standardized format.
- Data was captured in multiple languages, so customer names needed transliteration for consistency purposes.
- Customer names were sporadically stored in various columns as well as in free text format.
- Pattern analysis conducted on Social Security Numbers resulted in numerous examples of inconsistent and duplicate numbers.
- Customers data fed the website lacked standardization. This was impacting the effectiveness and response of marketing campaigns.
Better buy-in from other business units
This approach not only demonstrated the value of data quality process to end users, but it also helped the data quality initiative in getting buy-in from other business units. After the initial assessment, it was important to move forward with high-impact projects to create a chain reaction of data quality adoption. We saw that risk data governance was a key, with the mandate of monitoring risk data quality and providing anomaly reports to critical stakeholders(board, senior management, risk managers, etc.) It was a no brainer to align the data quality and governance project with the compliance and risk data warehouse team.
Data governance help aligns Business and IT owners in establishing data quality policies and controls
Conclusion
It doesn’t matter if you are discussing topics such as data governance, master data, reference data, metadata, compliance data, counter party data, KYC data or risk data. Essentially, you are discussing data overall. There is no better way of telling the story than by showing the ugly face of bad data to get things moving in right direction.
