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.
Awareness
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 and handling data as an asset is a priority.
Prioritization
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, ensure
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 it’s
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.
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