How to Create a Data Quality Management Plan

Low quality data can come at a high cost. According to a survey conducted by Gartner, poor data quality can cost organizations an average of $15 million annually.

To ensure the data you took the time to collect isn’t garbage, you need to create a data quality management plan.

What is Data Quality?

Data quality measures how accurate, complete, consistent, and reliable data is. High data quality ensures the information is reliable, actionable, and fit for its intended purpose. Ensuring good data quality involves identifying and correcting errors, removing duplicates, and properly formatting data.

This blog post will go over:

• What is Data Quality?
• What is a Data Quality Measure?
• How to Run a Data Quality Check
• How to Set Up a Data Quality Management Plan
• Create a Data Quality Management Plan

What is a Data Quality Measure?

Measuring data quality involves quantifying and evaluating data accuracy, completeness, consistency, and reliability. It enables you to assess the level of trustworthiness and usability of the data.

This process often includes defining relevant quality metrics, conducting data profiling and analysis, and using statistical techniques to identify and rectify any data quality issues. The key dimensions of data quality are:

• Completeness: The measure of how well data captures and represents all relevant information, without any missing or incomplete values.
• Uniqueness: The degree to which data is distinct and free from any duplicates, ensuring there is only one instance of each piece of information.
• Validity: How well data conforms to predefined rules, constraints, or standards, ensuring its reliability and compliance with specified requirements.
• Timeliness: The relevance and freshness of data, indicating how up-to-date and current it is.
• Accuracy: The level of correctness and precision of data, indicating how closely it reflects the true and intended values or characteristics it is meant to represent.
• Consistency: The degree of uniformity and coherence across different datasets or within a particular dataset, ensuring there is no conflicting or contradictory information.
• Fitness for purpose: The suitability and relevance of data for a specific intended use or purpose, ensuring it meets specific needs and requirements.

Measuring data quality helps you identify gaps, improve data governance, and make more informed decisions based on reliable and high-quality data.

How to Run a Data Quality Check

1. Define data quality criteria.

By clearly defining data quality criteria, you can set standards for what defines high-quality data. This step involves determining the specific factors of data quality that are relevant to your team, such as accuracy (how correct the data is), completeness (how whole the data is), consistency (how similar the data is across sources), and timeliness (how up-to-date the data is).

By defining these criteria, you establish a framework to assess the quality of your data.

2. Assess your data sources.

Evaluating the quality of data sources is important in helping you understand the reliability and validity of the information they provide. This step involves analyzing data collection methods, data capturing processes, data storage practices, and potential sources of errors or biases.

By assessing the data sources, you can identify any limitations or biases that may impact the quality of the data and take appropriate measures to address them.

3. Analyze data.

While the step above ensures you‘re pulling data from trustworthy and accurate places, dashboards, sites, or tools, data analysis allows you to gain deeper insights into the quality of your actual data. This step involves data profiling techniques to examine the data’s structure, distribution, and patterns.

Use data visualization tools and statistical analyses to identify outliers, missing values, duplicate records, or inconsistent data. By analyzing data, you can identify potential areas for improvement and make informed decisions about data quality.

4. Identify data quality issues.

Once you‘ve analyzed the data, it’s essential to identify specific data quality issues that need to be addressed or fixed. This step involves reviewing data analysis results to pinpoint discrepancies, anomalies, or inconsistencies in the data.

With this assessment, you should identify issues like incomplete records, inaccurate values, mismatched formats, or outdated information. By identifying these issues, you can focus on resolving them and improving the overall quality of your data.

5. Implement data cleansing.

Take action to clean and correct the identified data quality issues. Techniques like data deduplication, filling in missing values, normalizing formats, or correcting erroneous entries can be used.

Implement data validation rules and data cleansing processes to ensure that the data is accurate, consistent, and reliable. By cleansing and correcting the data, you enhance its quality and increase its usability for analysis and decision-making.

6. Monitor and maintain data quality.

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Monitoring and maintaining data quality is an ongoing process. Establish a system to regularly monitor the quality of your data. Set up data quality metrics and indicators to track data accuracy, completeness, consistency, and timeliness. You should also implement data governance practices to ensure adherence to data quality standards.

Afterwards, continuously review and improve data collection processes, data entry procedures, and data validation methods to maintain a high level of data quality over time. For example, HubSpot’s Data Quality Software can help you catch stale properties and integration bottlenecks.

Regular audits and periodic data quality checks will help you ensure that your data remains reliable and trustworthy.

How to Set Up a Data Quality Management Plan

1. Define goals and objectives.

To ensure you and your team stay motivated to keep data quality high, define “why” you need this quality data by aligning it to your company or team's goals and objectives. Then, clearly define the goals and objectives of your data quality management plan to your team, ensuring they are aligned with your overall business objectives.

You should also identify specific areas of data quality improvement that are essential for your organization's success. Consider factors such as accuracy, completeness, consistency, and timeliness, focusing on those that have the greatest impact on your business processes and decision-making.

2. Assess data quality

Conduct a comprehensive data audit and analysis to understand the current state of data quality in your organization. Identify data quality issues, such as missing or duplicate data, inconsistent data formats, and unreliable data sources.

Then, assess the impact these data quality issues have on business operations, decision-making, and customer satisfaction. Lastly, gather insights and metrics to establish a baseline for data quality improvement.

3. Develop data quality processes

Start with developing and documenting data quality processes and procedures that will be followed consistently across your organization. Next, define data validation rules, data cleansing protocols, data entry standards, and data governance policies.

Clearly outline roles and responsibilities related to data quality, ensuring clarity and accountability. Lastly, establish protocols for data management, storage, and access to maintain and improve data quality.

4. Implement data quality tools

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Identify and implement data quality tools and technologies that will support your data quality management efforts. Next, explore data profiling tools, data cleansing software, data quality dashboards, and other relevant solutions.

Integrate these tools into your data management systems and workflows to automate data checks, cleansing, and monitoring processes. Finally, leverage technology to streamline data quality efforts and enhance efficiency.

5. Monitor, measure, and improve data quality

Establish a system for ongoing monitoring and measurement of data quality, using defined metrics and key performance indicators. Regularly assess data quality, track progress, and identify new data quality issues or trends.

Implement regular data quality audits, reviews, and analysis to detect and address any gaps or weaknesses. Continuously improve data quality processes, procedures, and technology to adapt to changing needs and requirements and ensure ongoing data quality improvement.

Create a Data Quality Management Plan

Creating a data quality management plan is an essential step for any organization looking to leverage data for better decision-making and business outcomes. By following the steps outlined in this blog post, you can lay the groundwork for establishing a robust data quality management framework.

Leveraging data quality tools and technologies will enable you to automate processes, validate data, and identify and resolve issues promptly. Your analysis is as good as your data quality. Create a data quality management plan with the help of tools like HubSpot’s Data Quality Software.