Data quality is a strategic decision

Crédito y Caución integrates data quality into its management model

Solvency II includes financial requirements and also requirements for the quality of data handled by insurance companies. Miguel Angel Serantes says that Crédito y Caución could have simply built processes to comply with those new requirements, but they wanted to go much further. So the company built a data quality program that was good for the business.

The goal was to achieve 100 percent quality of the data generated by the company – the data it can control. That gives the company 100 percent confidence in the data. And Crédito y Caución wanted standardized criteria to help it get the maximum quality from external sources of data – data it had no control over.

Time for a positive change

“Information is our biggest asset,” explains Serantes, IT Development Manager of Crédito y Caución. “We are experts in finding it, storing it, analyzing it and deriving business intelligence from it. Solvency II compliance was our opportunity to incorporate the quality ratio into the management of that information and integrate that information into our procedures.”

Serantes says the first step in doing that was evaluating the current state of the data. He and his team made some critical decisions:

• The data needed to be accurate, complete and suitable for the company’s operations.
• Access levels needed to be defined to give responsibility for the data’s content, definition, use and administration.
• Compliance ratios for each category of data had to be set, so that by using a system of indicators the company could have an immediate view of the quality level of all data.

What the company did

“We decided in favor of the SAS solution for several reasons,” says Serantes. “We have a long relationship with SAS and a resident team that works closely with us to ensure efficient integration of SAS into our information management system.”

The solution has four qualities that make it perfect for the company’s needs:

1. It allows them to set criteria and attributes for defining the quality of data.
2. It has options for data evaluation.
3. It identifies quality problems and allows them to fix inaccuracies.
4. It facilitates the implementation of long-term strategies and gives them access to permanent quality monitoring.

The implementation of the data quality control system took about a year. But Serantes says that quality control is now an ongoing process. And it’s constantly evolving.

What the company gained

The advantages and benefits of this new strategy – and the technology solution – were obvious from the outset. “To begin with,” says Serantes, “we have a well-defined data policy that is recognized companywide. Now we know what we have to do with the data and who is responsible for it. And we know where the deficiencies are and how to correct them.”

“The SAS solution shows us the cause of the errors in the data. In later phases, we will be able to obtain more qualitative benefits, such as the definition of quality objectives for each data type, which will allow us to focus the controls on what is relevant to the business.”