What Is a Data Quality Framework? A Complete Guide to Implementation

In today's data-driven world, every decision, strategy, and opportunity hinges on the quality of the data you rely on. But what happens when the foundation is flawed?

While some businesses are concerned about data lineage, others question the accuracy and completeness of data available to them. There is no single solution to data quality management that would work for everyone. The only solution is to design and implement a data quality framework. Let’s take a closer look.

What is a data quality framework?

A data quality framework is a set of rules, tools and processes used to improve data quality. It allows organizations to measure data quality, set standards and take the action required to achieve these goals. Think of it as a roadmap for data quality initiatives.

Why do you need a data quality framework?

Relying on poor-quality data can have far-reaching consequences for business decisions and data systems. Over 50% of respondents to a survey reported that data quality issues affected at least 25% of their revenue. Hence the need for a systematic, measured approach to improving data quality.

Having a data quality framework allows you to:

• Define data quality standards for everyone to understand.
• Establish processes on how to store, handle and use data.
• Facilitate regular data monitoring to maintain a high-quality database.
• Encourage users to be accountable for the quality of data they use.
• Simplify compliance with data regulatory requirements.

Key components of a data quality framework

A data quality framework includes all the processes involved in making sure data is fit for its intended use. This includes:

• Data governance

This refers to the guidelines, policies and standards on how data is to be collected, stored and used within the company.

• Data profiling

This involves assessing the data collected from different sources and identifying data quality issues.

• Data quality rules

These are a set of rules that define the acceptable data quality standards.

• Data monitoring

This refers to regular data reviews based on the organization’s data quality rules.

• Data cleaning

This covers the tools and processes used to correct, update and complete erroneous data.

• Data issue management

This refers to how organizations report and resolve data quality issues.

• Continuous improvement

This refers to an organization’s ability to improve and adapt itself to evolving data quality requirements.

Types of Data Quality Frameworks

The ideal data quality framework depends on the type of data being handled, the size of your organization and so on. The most popular frameworks are:

Data Quality Assessment Framework (DQAF)        

DQAF focuses on improving the quality of data used for statistical purposes. It has a 5-part cascading structure that moves from addressing dimensions, elements and indicators common across all datasets to key points and focal issues tailored to the dataset.

Total Data Quality Management (TDQM)

TQDM takes a holistic approach to data quality across its entire lifecycle. It revolves around continuously defining, measuring, analyzing, and improving data quality dimensions. It is easy to adapt to varied data types and organizational needs.

Data Quality Scorecard (DQS)

This refers to a scorecard identifying and measuring data quality issues and the progress made toward improving overall duality standards. DQS focuses more on measuring quality rather than measures to improve it.

Data Quality Maturity Model (DQMM)

DQMM is a structured roadmap that assesses data quality capabilities on the level of data maturity. It has many versions that can be customized to suit varying business needs. This model makes it easy to identify opportunities for improvement and encourages a methodical approach to improving data quality.

Data Downtime (DDT)

This framework measures the time data is unusable because of quality issues and issues in the data pipeline. It highlights the impact of data quality and can guide an organization’s data quality improvement processes to minimize DDT.

Six Sigma

Six Sigma uses a 5-step process of defining, measuring, analyzing, improving and controlling data quality processes. It aims at identifying and removing erroneous data, reducing variation and improving efficiency.

Implementing a data quality framework

The established data quality frameworks as listed above all approach data from slightly different perspectives. Ideally, these must be customized to your requirements and circumstances. Here’s what you need to do:

• Understand Business Needs

Identify the data elements most important to your business decision-making tools and processes and any pain points that may exist.

• Define Goals

Identify and define goals for data quality dimensions such as accuracy, consistency, timeliness, relevance and consistency.

• Assess Existing Data Quality

Profile existing data to understand underlying patterns, errors and anomalies.

• Choose a Base Framework

Choose a data quality framework type as the foundation for your quality improvement measures.

• Customize the Framework

Designate a data governance team to customize the framework to your requirements and develop procedures for data access, usage and so on.

• Invest in Technology

Having the right tools is integral to the success of your data quality framework. Look for automated solutions that verify incoming data, standardize formats and enrich data. The tools should also be capable of regularly validating data existing in your database.

• Implement Data Quality Improvement Measures

Rather than keep a data quality framework exclusive to data teams, it must be rolled out throughout the organization. This includes providing relevant training to data users and winning enterprise-wide buy-in.

• Monitor and Adjust

Data quality is not a destination, but, an ongoing process. Assess the effectiveness of your data quality framework and make updates as required to keep it aligned with your business objectives.

Summing It Up

According to Gartner, 80% of organizations wanting to scale digital business initiatives fail because of outdated data governance approaches. Ad-hoc measures may temporarily improve data quality but don’t do much in the long term. On the other hand, having a well-defined data quality framework supported by the right tools can structure your data quality initiatives and thus make them sustainable and more likely to succeed.