Stibo Systems - The Master Data Management Company

What is a Data Quality Framework?

Master Data Management Blog by Stibo Systems logo
| 3 minute read
January 11 2022

Data quality is essential for any organization that relies on data for decision-making. A data quality framework provides a systematic approach to assessing and improving data quality across an organization. It defines the processes, tools and methodologies used to ensure that data is accurate, complete, consistent and reliable.

In this blog post, we will explore the key components of a data quality framework, how it can help organizations improve the quality of their data and finally, how to build and implement a data quality framework in ten steps.

 

Table of contents:

 

data-quality-framework

 

What is a data quality framework?

A data quality framework is a structured approach to managing data quality within an organization. It provides a systematic and standardized way to define, measure, monitor and improve the quality of data across different systems and processes.

The key components of a data quality framework include:

  • Data quality requirements

    Define the data quality requirements for each data element, including accuracy, completeness, consistency, timeliness and relevance.

  • Data quality metrics

    Establish data quality metrics to measure the quality of your data, such as completeness, accuracy, consistency, timeliness and uniqueness.

  • Data quality rules

    Develop data quality rules to identify and correct data quality issues, such as data validation rules, data transformation rules and data enrichment rules.

  • Data quality processes

    Establish data quality processes to manage the data quality framework, such as data governance, data stewardship and data quality monitoring.

  • Data quality roles and responsibilities

    Define the roles and responsibilities for managing data quality, such as data stewards, data owners and data quality analysts.

  • Data quality tools and technology

    Identify the data quality tools and technology needed to support the data quality framework, such as data profiling tools, data cleansing tools and data quality dashboards.

  • Data quality improvement plan

    Develop a data quality improvement plan to continuously improve the data quality framework over time.

Overall, a data quality framework enables organizations to take on a proactive approach to data quality management, ensuring that data is accurate, complete, consistent and reliable. It provides a systematic and repeatable process for data quality management, enabling organizations to continuously improve their data quality over time.

 

Why is having a data quality framework important for organizations?

Having a data quality framework is important for organizations for several reasons:

  • Better decision-making

    A data quality framework ensures that the data used for decision-making is accurate, complete, consistent and reliable, enabling organizations to make better-informed decisions.

  • Increased efficiency

    A data quality framework helps to eliminate data quality issues, reducing the amount of time and resources spent on correcting errors and rework.

  • Improved customer satisfaction

    Accurate and consistent data improves the customer experience by ensuring that customer information is correct and up-to-date.

  • Compliance

    A data quality framework helps organizations comply with regulatory requirements by ensuring that data is accurate, complete and consistent.

  • Cost savings

    A data quality framework helps organizations save costs associated with data errors, such as rework, lost productivity and lost revenue.

  • Competitive advantage

    A data quality framework enables organizations to differentiate themselves from their competitors by providing higher-quality data for decision-making and customer service.

Overall, a data quality framework is essential for organizations that rely on data for decision-making and customer service. It ensures that data is accurate, complete, consistent and reliable, improving the quality of decision-making, customer service and overall business performance.

 

 

How to build and implement a data quality framework

Building and implementing a data quality framework involves the following steps:

1. Define data quality requirements

Start by identifying the data quality requirements for your organization. These may include accuracy, completeness, consistency, timeliness and relevance. Define the data quality requirements for each data element and establish the acceptable levels of quality.

2. Identify data sources

Identify the data sources for each data element and document the data lineage to understand the data's journey through your organization.

3. Establish data quality metrics

Define the data quality metrics to measure the quality of your data. Metrics may include completeness, accuracy, consistency, timeliness and uniqueness.

4. Assess data quality

Assess the quality of your data against the established data quality metrics. This may involve data profiling, data cleansing and data enrichment.

5. Establish data quality rules

Develop data quality rules to identify and correct data quality issues. These rules may be automated or manual, depending on the data element and the complexity of the rule.

6. Implement data quality processes

Establish data quality processes to manage the data quality framework. This may involve data governance, data stewardship and data quality monitoring.

7. Monitor data quality

Monitor the quality of your data on an ongoing basis to ensure that it meets the established data quality requirements. Use data quality dashboards and reports to track data quality metrics over time.

8. Establish data quality roles and responsibilities

Define the roles and responsibilities for managing data quality. This may involve establishing a data quality team, data stewards and data owners.

9. Train staff

Train staff on the importance of data quality and how to manage data quality using the established data quality framework.

10. Continuous improvement

Continuously improve the data quality framework by reviewing and updating data quality requirements, data quality metrics, data quality rules and data quality processes on an ongoing basis.

Building and implementing a data quality framework requires a systematic approach and a commitment to ongoing improvement. It involves collaboration across the organization to ensure that data quality is managed effectively and consistently.

 

Related blog posts:



Topics: 
Master Data Management Blog by Stibo Systems logo

Katja is a seasoned digital strategist with a 10-year track record of helping businesses across different industries drive digital growth and improve their digital experience.

Discover Blogs by Topic

  • MDM strategy
  • Data governance
  • Customer and party data
  • See more
  • Retail and distribution
  • Manufacturing
  • Data quality
  • Supplier data
  • Product data and PIM
  • AI and machine learning
  • CPG
  • Financial services
  • GDPR
  • Sustainability
  • Location data
  • PDX Syndication

Master Data Management Roles and Responsibilities

5/20/24

8 Best Practices for Customer Master Data Management

5/16/24

What Is Master Data Governance – And Why Do You Need It?

5/12/24

4 Common Master Data Management Implementation Styles

5/10/24

Guide: Deliver flawless rich content experiences with master data governance

4/11/24

Risks of Using LLMs in Your Business – What Does OWASP Have to Say?

4/10/24

Guide: How to comply with industry standards using master data governance

4/9/24

Digital Product Passports - A Data Management Challenge

4/8/24

Guide: Get enterprise data enrichment right with master data governance

4/2/24

Guide: Getting enterprise data modelling right with master data governance

4/2/24

Guide: Improving your data quality with master data governance

4/2/24

Data Governance Trends 2024

1/30/24

NRF 2024 Recap: In the AI era, better data can make all the difference

1/19/24

Building Supply Chain Resilience: Strategies & Examples

12/19/23

How Master Data Management Can Enhance Your ERP Solution

12/14/23

Shedding Light on Climate Accountability and Traceability in Retail

11/29/23

What is Smart Manufacturing and Why Does it Matter?

10/11/23

Future Proof Your Retail Business with Composable Commerce

10/9/23

5 Common Reasons Why Manufacturers Fail at Digital Transformation

10/5/23

How to Digitally Transform a Restaurant Chain

9/29/23

Three Benefits of Moving to Headless Commerce and the Role of a Modern PIM

9/14/23

12 Steps to a Successful Omnichannel and Unified Commerce

7/6/23

CGF Global Summit 2023: Unlock Sustainable Growth With Collaboration and Innovation

7/5/23

Navigating the Current Challenges of Supply Chain Management

6/28/23

Responsible AI relies on data governance

5/11/23

Product Data Management during Mergers and Acquisitions

4/6/23

Master Data Management Definitions: The Complete A-Z of MDM

3/14/23

4 Ways to Reduce Ecommerce Returns

3/8/23

Asset Data Governance is Central for Asset Management

3/1/23

How to Leverage Internet of Things with Master Data Management

2/14/23

Manufacturing Trends and Insights in 2023-2025

2/14/23

Sustainability in Retail Needs Governed Data

2/13/23

What is Augmented Data Management?

2/9/23

NRF 2023: Retail Turns to AI and Automation to Increase Efficiencies

1/20/23

What is the difference between CPG and FMCG?

1/18/23

5 Key Manufacturing Challenges in 2023

1/16/23

What is a Golden Customer Record in Master Data Management?

1/9/23

The Future of Master Data Management: Trends in 2023-2025

1/8/23

Innovation in Retail

1/4/23

5 CPG Industry Trends and Opportunities for 2023-2025

12/5/22

Life Cycle Assessment Scoring for Food Products

11/21/22

Retail of the Future

11/14/22

Omnichannel Strategies for Retail

11/7/22

Hyper-Personalized Customer Experiences Need Multidomain MDM

11/5/22

What is Omnichannel Retailing and What is the Role of Data Management?

10/25/22

Most Common ISO Standards in the Manufacturing Industry

10/18/22

How to Get Started with Master Data Management: 5 Steps to Consider

10/17/22

What is Supply Chain Analytics and Why It's Important

10/12/22

What is Data Quality and Why It's Important

10/12/22

A Data Monetization Strategy - Get More Value from Your Master Data

10/11/22

An Introductory Guide: What is Data Intelligence?

10/1/22

Revolutionizing Manufacturing: 5 Must-Have SaaS Systems for Success

9/15/22

An Introductory Guide to Supplier Compliance

9/7/22

What is Application Data Management and How Does It Differ From MDM?

8/29/22

Digital Transformation in the Manufacturing Industry

8/25/22

Master Data Management Framework: Get Set for Success

8/17/22

Discover the Value of Your Data: Master Data Management KPIs & Metrics

8/15/22

Supplier Self-Service: Everything You Need to Know

6/15/22

Omnichannel vs. Multichannel: What’s the Difference?

6/14/22

Digital Transformation in the CPG Industry

6/14/22

Create a Culture of Data Transparency - Begin with a Solid Foundation

6/10/22

The 5 Biggest Retail Trends for 2023-2025

5/31/22

What is a Location Intelligence?

5/31/22

Omnichannel Customer Experience: The Ultimate Guide

5/30/22

Location Analytics – All You Need to Know

5/26/22

Omnichannel Commerce: Creating a Seamless Shopping Experience

5/24/22

Top 4 Data Management Trends in the Insurance Industry

5/11/22

What is Supply Chain Visibility and Why It's Important

5/1/22

6 Features of an Effective Master Data Management Solution

4/30/22

What is Digital Asset Management?

4/23/22

The Ultimate Guide to Data Transparency

4/21/22

How Manufacturers Can Shift to Product-as-a-Service Offerings

4/20/22

How to Check Your Enterprise Data Foundation

4/16/22

An Introductory Guide to Manufacturing Compliance

4/14/22

Multidomain MDM vs. Multiple Domain MDM

3/31/22

Making Master Data Accessible: What is Data as a Service (DaaS)?

3/29/22

How to Build a Successful Data Governance Strategy

3/23/22

What is Unified Commerce? Key Advantages & Best Practices

3/22/22

How to Choose the Right Data Quality Tool?

3/22/22

What is a Data Domain?

3/21/22

6 Best Practices for Data Governance

3/17/22

5 Advantages of a Master Data Management System

3/16/22

A Unified Customer View: What Is It and Why You Need It

3/9/22

Supply Chain Challenges in the CPG Industry

2/24/22

Data Migration to SAP S/4HANA ERP - The Fast and Safe Approach with MDM

2/17/22

The Best Data Governance Tools You Need to Know About

2/17/22

Top 5 Most Common Data Quality Issues

2/14/22

What Is Synthetic Data and Why It Needs Master Data Management

2/10/22

What is Cloud Master Data Management?

2/8/22

How to Implement Data Governance

2/7/22

Build vs. Buy Master Data Management Software

1/28/22

Why is Data Governance Important?

1/27/22

Five Reasons Your Data Governance Initiative Could Fail

1/24/22

How to Turn Your Data Silos Into Zones of Insight

1/21/22

How to Improve Supplier Experience Management

1/16/22

​​How to Improve Supplier Onboarding

1/16/22

How to Enable a Single Source of Truth with Master Data Management

1/13/22

What is a Data Quality Framework?

1/11/22

How to Measure the ROI of Master Data Management

1/11/22

What is Manufacturing-as-a-Service (MaaS)?

1/7/22

The Ultimate Guide to Building a Data Governance Framework

1/4/22

Introducing the Master Data Management Maturity Model

1/3/22

Master Data Management Tools - and Why You Need Them

12/20/21

The Dynamic Duo of Data Security and Data Governance

12/20/21

How to Choose the Right Supplier Management Solution

12/20/21

How Data Transparency Enables Sustainable Retailing

12/6/21

What is Supplier Performance Management?

12/1/21

What is Party Data? All You Need to Know About Party Data Management

11/28/21

What is Data Compliance? An Introductory Guide

11/18/21

How to Create a Marketing Center of Excellence

11/14/21

The Complete Guide: How to Get a 360° Customer View

11/7/21

What is the Difference Between Master Data and Metadata?

11/1/21

How Location Data Adds Value to Master Data Projects

10/29/21

How Marketers Should Prepare for the 2023 Holiday Shopping Season

10/26/21

What is Supplier Lifecycle Management?

10/19/21

What is a Data Mesh? A Simple Introduction

10/15/21

How to Build a Master Data Management Strategy

9/26/21

10 Signs You Need a Master Data Management Platform

9/2/21

What Vendor Data Is and Why It Matters to Manufacturers

8/31/21

3 Reasons High-Quality Supplier Data Can Benefit Any Organization

8/25/21

4 Trends in the Automotive Industry

8/11/21

What is Reference Data and Reference Data Management?

8/9/21

What Obstacles Are Impacting the Global Retail Recovery?

8/2/21

GDPR as a Catalyst for Effective Data Governance

7/25/21

All You Need to Know About Supplier Information Management

7/21/21

5 Tips for Driving a Centralized Data Management Strategy

7/3/21

Data Governance and Data Protection, a Match Made in Heaven?

6/29/21

Welcome to the Decade of Transparency

5/26/21

How to Become a Customer-Obsessed Brand

5/12/21

How to Create a Master Data Management Roadmap in Five Steps

4/27/21

What is a Data Catalog? Definition and Benefits

4/13/21

How to Improve the Retail Customer Experience with Data Management

4/8/21

How to Improve Your Data Management

3/31/21

How to Choose the Right Master Data Management Solution

3/29/21

Business Intelligence and Analytics: What's the Difference?

3/25/21

Spending too much on Big Data? Try Small Data and MDM

3/24/21

What is a Data Lake? Everything You Need to Know

3/21/21

How to Extract More Value from Your Data

3/17/21

Are you making decisions based on bad HCO/HCP information?

2/24/21

Why Master Data Cleansing is Important to CPG Brands

1/20/21

CRM 2.0 – It All Starts With Master Data Management

12/19/20

5 Trends in Telecom that Rely on Transparency of Master Data

12/15/20

10 Data Management Trends in Financial Services

11/19/20

Seasonal Marketing Campaigns: What Is It and Why Is It Important?

11/8/20

What Is a Data Fabric and Why Do You Need It?

10/29/20

Transparent Product Information in Pharmaceutical Manufacturing

10/14/20

How to Improve Back-End Systems Using Master Data Management

9/19/20

8 Benefits of Transparent Product Information for Medical Devices

9/1/20

How Retailers Can Increase Online Sales in 2023

8/23/20

Master Data Management (MDM) & Big Data

8/14/20

Key Benefits of Knowing Your Customers

8/9/20

Women in Master Data: Kelly Amavisca, Ferguson

8/5/20

Customer Data in Corporate Banking Reveal New Opportunities

7/21/20

How to Analyze Customer Data With Customer Master Data Management

7/21/20

How to Improve Your 2023 Black Friday Sales in 5 Steps

7/18/20

4 Ways Product Information Management (PIM) Improves the Customer Experience

7/18/20

How to Estimate the ROI of Your Customer Data

7/1/20

Women in Master Data: Rebecca Chamberlain, M&S

6/24/20

How to Personalise Insurance Solutions with MDM

6/17/20

How to Democratize Your Data

6/3/20

How to Get Buy-In for a Master Data Management Solution

5/25/20

How CPG Brands Manage the Impact of Covid-19 in a Post-Pandemic World

5/18/20

5 Steps to Improve Your Data Syndication

5/7/20

Marketing Data Quality: Why Is It Important and How to Get Started

3/26/20

Panic Buying: Navigating Long-term Implications and Uncertainty

3/24/20

Women in Master Data: Ditte Brix, IMPACT

2/20/20

Get More Value From Your CRM With Customer Master Data Management

2/17/20

Women in Master Data: Nagashree Devadas, Stibo Systems

2/4/20

How to Create Direct-to-Consumer (D2C) Success for CPG Brands

1/3/20

Women in Master Data: Anna Schéle, Ahlsell

10/25/19

Women in Master Data: Morgan Lawrence, Infoverity

9/26/19

Women in Master Data: Sara Friberg, Acando (Part of CGI)

9/13/19

Improving Product Setup Processes Enhances Superior Experiences

8/21/19

How to Improve Your Product's Time to Market With PDX Syndication

7/18/19

8 Tips For Pricing Automation In The Aftermarket

6/1/19

How to Drive Innovation With Master Data Management

3/15/19

Discover PDX Syndication to Launch New Products with Speed

2/27/19

How to Benefit from Product Data Management

2/20/19

What is a Product Backlog and How to Avoid It

2/13/19

How to Get Rid of Customer Duplicates

2/7/19

4 Types of IT Systems That Should Be Sunsetted

1/3/19

How to Use Customer Data Modeling

11/15/18

How to Reduce Time-to-Market with Master Data Management

10/28/18

How to Start Taking Advantage of Your Data

9/12/18

6 Signs You Have a Potential GDPR Problem

8/16/18

GDPR: The DOs and DON’Ts of Personal Data

6/13/18

How Master Data Management Supports Data Security

6/7/18

Frequently Asked Questions (FAQ) About the GDPR

5/30/18

Understanding the Role of a Chief Data Officer

4/26/18

3 Steps: How to Plan, Execute and Evaluate Any IoT Initiative

2/20/18

How to Benefit From Customer-Centric Data Management

9/7/17

3 Ways to Faster Innovation with Multidomain Master Data Management

6/7/17

Product Information Management Trends to Consider

5/25/17

4 Major GDPR Challenges and How to Solve Them

5/12/17

How to Prepare for GDPR in Five Steps

2/21/17

How Data Can Help Fight Counterfeit Pharmaceuticals

1/24/17

Create the Best Customer Experience with a Customer Data Platform

1/11/17
Did you like this blog post?

Sign up to get the latest blog content in your inbox.