How to Measure Data Quality

What it means, why it matters, and best practices. This article provides definitions and insights into measuring data quality.

Data quality measures

Data quality is key when it comes to data pipelines and customer satisfaction. When your company deals with large quantities of data, you need to ensure its quality. Many people will ask “What are the 6 dimensions of data quality?” While many resources will detail 6 dimensions, there can be some redundancy in the way they are presented. With 5 dimensions, the focus is streamlined. The 5 dimensions of data quality are as follows:

  • Accuracy
  • Completeness
  • Reliability
  • Relevance
  • Timeliness

These data quality characteristics determine the effectiveness of the data itself.  Googling a data quality dimensions PDF will provide an excellent foundation for the importance of the dimensions as well as a quality strategy for how the data is handled. But even with the dimensions laid out, you may still be asking, “What is data quality and why is it important?”

Data quality measures are very much in line with the dimensions listed above. Put plainly, they measure the quality of the data being reported. Any well-made data quality report example will make a point to highlight these measures. Data quality metrics come down to what you can control within the data delivery process.

These measures are in place to ensure that no duplicate data, poorly organized data, incomplete data, and so on. On the other side of that coin, complete, well organized, and unique data are all data quality examples that every organization should strive for. When quality data is handled correctly, business operations are more efficient and thorough.

Data quality standards

In the context of an interconnected performance management system, data quality is a key component. Data quality standards are the guidelines used to ensure consistent delivery. The standards fall in line with the data quality dimensions listeeed above as well. They can be monitored and met with the help of powerful data quality tools, like Acceldata.

Best practices: data quality standards

There are a few best practices to keep in mind when trying to align your data quality with the standards used industry-wide. These data quality standards best practices below will help you achieve consistent data quality.

  1. Make data an enterprise-wide priority.
  2. Establish data quality metrics.
  3. Investigate data quality failures.
  4. Regular internal training.
  5. Build data governance guidelines.
  6. Establish a data auditing process.

In order to effectively implement these data quality standards, examples may be a helpful tool to build your own methods around. The Federal Committee on Statistical Methodology offers a very thorough data quality standards PDF free to use for any organization looking to improve their data handling.

Data quality metrics template

Gauging the quality of your data consistently can be a tall order without a detailed framework to adhere to. It can make knowing how to measure data quality a very abstract ordeal. Employing a data quality metrics template can make all of the difference. Utilizing tools can be the key to understanding the metrics and making the most of them.

Data quality measurement tools are effective because of how they lay the information out for your team to understand. Key performance indicators (KPIs) are valuable in the tracking and quality monitoring of data. Finding a tool that offers a user-friendly data quality KPI dashboard will help your team assess and manage data efficiently. Data quality indicators will become a core focus for your team and the overall consistency of data quality will increase.

Data quality metrics scorecard

Tracking data quality metrics is one thing, but the process can be optimized by implementing a data quality metrics scorecard can help your team analyze the overall health of your data and build comparisons to past data. Scoring your data quality metrics and reporting those scores regularly will help the organization keep to a level of quality and immediately identify when they are falling short.

A data governance scorecard is slightly different as it grades the control and shared decision-making surrounding the data assets themselves. While data quality score calculation depends on the dimensions listed earlier, the governance score grades the overall handling and sharing of the data itself.

A data quality health score will provide insight into how well the standards are being met and how often. For building a data quality scorecard template, Excel spreadsheets are the most effective method. Template can be downloaded online, or you can find a data quality scorecard example online and build your own using it as a model. As long as it aligns with your operational model, the scorecard will be an effective tool for your team.

Data quality measurement framework

In terms of data quality measurement, framework implementation is a vital step. A book titled Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework is a great foundation for building and maintaining frameworks tailored to your data needs. This book functions as a data quality framework implementation guide, showing its readers not only the frameworks to use as well as the most effective ways to use them.

This book will help in the process of creating a data quality framework template that fits your specific operational strategy. Building a template from scratch allows for the freedom of customization. With this approach, you will have all of the assets on hand to more easily create a data quality framework PPT (PowerPoint Presentation) for company-wide training. The more informed your staff is, the better the framework can be used.

Data quality metrics dashboard

The best framework in the world is useless if team members can’t effectively interact with the data itself. Utilizing tools that offer a data quality metrics dashboard can get your entire team up to speed on the use and implementation of the data quality framework itself. A dashboard allows for the entire pipeline to be monitored and managed through every step of the process.

With unlimited visibility, companies can identify data flaws early and get things back on track efficiently. Acceldata’s services offer one of the most effective data quality dashboard examples available today. The Acceldata Pulse system offers industry-leading visibility and reporting. Contact Acceldata today to learn more.

Ready to start your data observability journey?

Request a demo and chat with one of our experts.