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Data Quality in Financial Institutions

Written by Jose Manuel Almeida

Data Quality in Financial Institutions

Written by Jose Manuel Almeida on Jan 13, 2020 11:38:24 AM

Datacon Africa DataCon Africa Insights Data and Analytics

In the last decade regulatory requirements in financial services increased significantly.

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, 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 the capability 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, and unless this is addressed proactively this is impacting your organization this very moment.

There are, however, some straight actions that can be taken immediately to push your organization to move in the right direction.


Create an environment where the importance of data quality is recognized across all the organization and where the existence of data quality problems is accepted, (Avoiding denial: https://www.linkedin.com/pulse/dealing-denial-approaching-data-quality-from-business-almeida/), and handling data as an asset is a priority.


Identify 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 are always clear.

Bad data impacts business in many ways, either affecting the management confidence in the organizations data, resulting in missed opportunities by losing the capability to derive insights that can lead to competitive advantage, leading to lost revenue in many ways, resulting in reputational costs or undermining efforts to improve customer experience.

A very important driver of data quality initiative is regulatory compliance especially in the banking sector.

With a regulatory framework that keeps growing, it customary for banks to demand longer time-frames to prepare for each new directive, seeming 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, maintaining a silo-based infrastructure.

Often regarded as a necessary evil, data quality initiatives related with compliance are approached as a series of isolated initiatives, 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.

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.

In this point it is important to conduct data quality assessments, focusing on business processes most likely to be affected by bad data, allowing to gather the necessary inputs to build a consistent roadmap for data quality initiatives.

Data Management Program

Taking a broader view of data in the organization and looking at it as an important asset, creates the need to manage it in a more systematic way.

Starting with a data management strategy providing a framework and an architecture for the data management program. Ensuring 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 its affected by everyone in the organization, so a data program will affect everyone, from employees to customers, to all the processes that relate with data, everything.

Written and contributed by Jose Manuel Almeida


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