Six Steps to Improving Data Quality in Your Organisation

In this article in our Data Driven Organisation Series, our Practice Manager, Steve Rose, outlines the six steps you should take to improve data quality in your organisation. 

In today's data-driven world, organisations of all sizes and industries rely on data to make critical decisions, drive innovation, and gain a competitive edge. However, data is only valuable when it is accurate, reliable, and consistent. Poor data quality can lead to inaccurate insights, decreased efficiency, and even legal and compliance risks. As a result, for many leading CIOs, CISOs, data analysts, and other data professionals, ensuring data quality is a top priority. In this article, we'll explore six essential steps to improving data quality in your organisation. 

Step 1: Determine Your Current Goals and Scope 

Before diving into a data quality initiative, it's crucial to define your goals and scope. Start by asking yourself what you are trying to achieve. Your initiative's purpose will directly impact the processes you put in place. 

Tactical vs. Strategic Approach 

There are two primary types of data quality frameworks: tactical and strategic. A tactical approach focuses on addressing specific issues, such as resolving customer complaints or improving campaign performance. In contrast, a strategic approach aims to establish enterprise-wide data quality standards and prevent ongoing deterioration. 

To stay focused, follow these steps: 

1. Identify the business process you want to improve
2. Determine the data sources contributing to that process.
3. Identify Critical Data Elements (CDEs) within those sources.
4. Define the scope of your initiative.
5. Focus on Critical Data Elements (CDEs) 

Whether you choose a tactical or strategic approach, it's essential to identify and prioritise your Critical Data Elements (CDEs). CDEs are the most valuable and essential data in your organisation. These could include customer names, email addresses, demographic attributes, or purchase history, depending on your specific needs. 

To identify CDEs: 

1. Check if CDEs have already been identified in your organisation.
2. Identify key business and reporting requirements.
3. Collaborate with data consumers and experts to determine essential data elements. 

Step 2: Profile Data 

Now that you've identified your CDEs, it's time to assess the quality of the data within these elements. Data profiling is a critical process that helps you gain insights into your data's characteristics, format, and potential issues. By profiling your data, you can identify: 

• Data domains (e.g., customer data). 
• Data format and patterns. 
• Data value distributions and abnormalities. 
• Completeness of the data. 
• Common data errors like duplicates and empty values. 
  

Benefits of Data Profiling 

Data profiling provides valuable insights into data quality issues. For example, if your marketing department's email campaign is underperforming, data profiling might reveal that a significant number of email addresses are in invalid formats or that a substantial portion of customer records lacks email addresses. This information can guide you in taking corrective actions. 

Step 3: Start a Data Quality Program 

Once you've profiled your data and identified quality issues, it's time to initiate a data quality program. This program involves two key actions: 

1. Fix the most urgent data quality issues immediately.
2. Establish metrics and methods for ongoing data quality monitoring. 

Addressing Urgent Data Quality Issues 

You can start by addressing the most critical data quality issues. This may involve data cleansing, transformation, or standardisation. While quick fixes using tools like SQL, Excel, or Python can be effective, consider implementing a robust data quality solution for long-term benefits. 

Modern data quality tools automate many tasks, from profiling data to preventing bad data from entering your systems. 

Create Data Quality Rules 

To maintain data quality over time, establish data quality rules. These rules define conditions that data must satisfy. For instance, you might create rules to check email address formats or validate data against predefined criteria. 

• Use insights from data profiling to inform rule creation. 
• Collaborate with data consumers and experts to define data quality rules. 

Monitoring Data Quality 

Once you have data quality rules in place, apply them to various data sources for continuous monitoring. This approach ensures ongoing data quality and allows you to track progress effectively. 

Step 4: Expand Your Data Quality Initiative 

After establishing initial data quality improvements, consider expanding your initiative. This expansion can lead to greater benefits and discoveries within your organisation. 

Expand to Other CDEs and Source Systems 

Leverage the features and processes developed for your Critical Data Elements (CDEs) to extend your data quality initiative across all data sets. By doing so, you may uncover valuable data sets that were previously untapped due to poor data quality. 

Create Automated Data Processes 

Automation is key to maintaining data quality in real-time. Implement processes for: 

• Data validation: Check incoming data against business rules. 
• Data cleansing and transformation: Standardise data formats and remove duplicates. 
• Data monitoring and reporting: Continuously measure and track data quality. 
• Issue remediation: Define processes for addressing data quality issues promptly. 
• Automating these processes ensures efficient data quality management and reliable data analysis. 

Step 5: Establish a Data-Driven Culture 

To ensure the long-term success of your data quality efforts, foster a data-driven culture within your organisation. This culture should encourage every team member to prioritise and contribute to data quality. 

• Develop a shared vision of data quality. 
• Encourage collaboration and engagement from all stakeholders. 
• Implement self-service Data Quality to empower data analysts and business users. 

Step 6: Monitor, Measure, and Communicate Data Quality Results 

Finally, continuously monitor and measure data quality results. Regularly communicate data quality issues, initiatives, and progress to keep all stakeholders engaged. 

• Document progress, actions, and results. 
• Use data quality reports to reinforce the importance of data quality. 

By following these six steps, you can establish a robust data quality framework that not only addresses immediate issues but also ensures ongoing data accuracy and reliability. In today's data-driven landscape, data quality is a critical foundation for success, and investing in it will yield significant returns for your organisation. 

Need help improving the data quality in your organisation? 

We have a proven approach to assessing an organisation’s data maturity and can provide practical recommendations on how to lift data quality, enabling you to embrace AI and realise the full potential of your data. Learn more here.