Why Data Quality is Essential for Regulation Compliance - Part I

Introduction

The regulatory landscape is constantly shifting, with existing regulatory bodies altering current requirements, and new ones frequently being introduced. As a result, businesses must adapt, often investing significant time and effort to ensure compliance.

Data quality increasingly plays an important part in ensuring businesses remain compliant. This is a result of an ever-increasing dependency on data than ever before, with more data being stored, processed and used within applications. As the knowledge and usage of data expands and evolves, new regulations will be created to mitigate any risk to its security.

We have been working with numerous businesses on regulatory compliance, specifically on how data quality plays a significant role. In this two-part blog series, we will discuss how we have approached the situation, the top challenges we have faced, and some tips to ensure high data quality.

How do we define and approach data quality?

Data quality is the process of understanding the condition of data that is being used for a particular purpose. The measurement is mainly based on the core data quality domains: Accuracy, Timeliness, Completeness, Accessibility, Consistency and Uniqueness. However, there are also the associated processes of discovering required systems and data, implementing accountability, and identifying and fixing erroneous data, which all fall under the data quality umbrella.

Having good data quality does not necessarily mean that all data within the business is perfect with zero inaccuracies. It is better defined as having data of sufficient quality as required by its consumers and systems and to be adequate for their specific needs. For example, for a particular data product, it might not be necessary to have perfect data with zero errors across all fields within a source system. It might only be dependent on a few of those fields, which are the ones where data quality should be measured, and improved if necessary.

It is best to take a consistent and methodical approach to identifying, measuring and rectifying data quality across a system, domain or business, instilling data quality principles, business processes and tooling to ensure the data stored and processed is suitable for all purposes.

Where does data quality cross over with regulation compliance and what is the risk?

A large number of regulatory bodies require businesses to uphold a certain level of data quality, this is with the purpose of minimising and managing risk related to data. This is especially important when looking at PII and other sensitive forms of data — often this type of data will have specific regulations on how it should be handled, monitored and stored. Typically, data regulations require the business to:

• Capture, store and ensure security of specific data fields. For the business to be compliant with those regulations, it must ensure the data within that field is complete, accurate, have the right accessibility, consistency, available when required and correct uniqueness — fundamentally understanding the data quality of that field.
• Report back to regulatory bodies at different levels of granularity. It is imperative this reporting is accurate and timely.

Regulated businesses which do not have an adequate level of data quality run the risk of:

• Potential fines & penalties — in the cases where the business fails to comply with regulations. This includes financial fines and business restrictions.
• Inefficient business processes — the process of ensuring the business is secure from any risk and ensuring the business is compliant can be a much less comprehensive task with a good level of data quality.
• Security breaches — ensuring data that is captured, stored and processed has the correct level of accessibility within the business. Failure of this can lead to potential data breaches E.g. is it essential that any PII data within the business has a high level of security to ensure it isn’t leaked externally.
• Bad reputation — having poor data quality makes a business more prone to incidents related to data, which can result in having a negative impact on the reputation of the business — especially in the cases where consumers are impacted.

In the second part of our series, we’ll dive deeper into our approach to data quality and how to solve the biggest challenges in this space.