Regulatory requirements in financial services increased significantly over the last decade. In this context, quality data provided through effective data governance and data quality processes is essential to achieve effective compliance reporting, ensuring accurate reporting and improving business decisions that depend on quality data.
As in other industries, the financial services are not immune to data quality issues. From false mortgage applications to incorrect credit ratings and balance sheets, the list of data related problems is vast. Adding to this bad data impairs an organization's ability to make and execute decisions. No decision is better than the data it relies on.
Looking at this scenario it’s unquestionable that bad data directly increases costs and reduces revenue. Unless it is addressed proactively, this is impacting your organization this very moment. There are, however, some actions that can be taken immediately to push your organization to move in the right direction.
Promote Data Quality Awareness
Create an environment where the importance of data quality is recognized across all the organization. One where the existence of the data quality problems is accepted, organizational denial is fought at every turn and handling data as an asset is a priority.
"When we stop addressing data quality as a mere technical issue, easily solved by a set of processes, and start addressing the business processes underlying the data problem it is usual to find some resistance from the business"
Identify Key Data Quality Drivers
Look for business drivers to give a boost to data quality initiatives. It’s essential that the connection between data quality and its negative impacts in business is always clear.
Bad data impacts business in many ways, often affecting management's confidence in the organization's data. This results in missed opportunities. Losing the capability to derive insights that would create a competitive advantage leads to lost revenue in many ways, through reputational costs or undermining efforts to improve customer experiences, for example.
A very important driver of data quality initiatives is regulatory compliance, especially in the banking sector. With a regulatory framework that keeps growing, it's customary for banks to demand longer timeframes to prepare for each new directive. It seems incredible how data-driven organizations struggle to supply accurate data.
This is true for many financial institutions of every size, trying to manage internal and external requirements for data while maintaining a silo-based infrastructure. Often regarded as a necessary evil, data quality initiatives related to compliance are approached as a series of isolated initiatives, from a tactical perspective, to satisfy the minimum requirements to comply to a specific directive.
Compliance should be an opportunity to establish a data quality framework that will allow the organization to comply and accelerate the deliverables for new compliance directives.
Establish a Data Quality Framework
The definition and implementation of a data quality framework with a clear roadmap of initiatives is a critical step, allowing the organization to move from a tactical to a strategic approach to data quality.
At this point, it is important to conduct data quality assessments. Focus on the business processes most likely to be affected by bad data and gather the necessary inputs to build a consistent roadmap for data quality initiatives.
Launch a Data Management Program
Taking a broader view of data in the organization and looking at data as an important asset highlights the need to manage it in a more systematic way.
Starting with a data management strategy, provide a framework and an architecture for the data management program. This should establish consistent project and integration approaches, best practices in design and implementation, technologies and data policies.
This is typically a disruptive process within the organization. Data touches every aspect of business and simultaneously is affected by everyone in the organization. So, a data program will affect everything, from employees and customers to all the processes that relate to data – everything.
