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What is Data Quality Management?

What it means, why it matters, and how it works. This article provides definitions and practical advice to help you understand and gain insight into data quality management.

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What is data quality management?

Research has repeatedly demonstrated that poor data quality management has cost businesses millions of dollars. In 2016, IBM released a report that estimated the cost of bad data for U.S. businesses was around $3.1 trillion dollars every single year. Over time, as businesses continue to manage increasing volumes of data, that number is only likely to go up. The question is, can we solve the problem?

In order to properly manage data quality, we need to understand the complete set of data quality goals and objectives.  Our founder and CEO, Rohit Choudhary, in a blog post, described the six dimensions of data quality as:

  1. Accuracy
  2. Completeness
  3. Consistency
  4. Freshness
  5. Validity
  6. Uniqueness

The way to ensure that these six elements are maintained in your data is to utilize a data quality management plan. This plan needs to consist of more than just data quality monitoring tools. Although they may sound like a possible solution, these kinds of tools are no longer effective in the age of cloud-centric data infrastructures with complicated data pipelines. Due to fundamental flaws in their design, it is often difficult to use them to identify the root causes of issues, and their scalability is limited. Furthermore, based on the six dimensions above, data quality is more than just errors. A dataset could be completely correct and accurate but be six years old.

Instead of just monitoring for errors, a great data quality plan example establishes checkpoints along your entire pipeline and ensures that all six dimensions are held constant for all your data. Context is key to being able to identify the root cause of problems. The way to get that context when it comes to big data is through a data observability platform like Acceldata. With Acceldata, you can better manage your data and maintain its quality.

Data quality management best practices

One way to learn more about best practices when it comes to data quality management tools and techniques is to download a data quality management PDF. Acceldata provides several different data quality guides and eBooks that can help you uncover more about how observability is integral to building your data quality framework. Because of the diversity of sources and teams, data quality management is impossible without an organization-wide strategy that ensures everyone is on the same page about how data should be used. In today’s highly competitive business environment, knowing how to get the most out of your data is crucial. However, making a decision based on poor quality data can lead to massive problems. The reality is that big data is called that for a reason. Most enterprises have to manage thousands of different sources and hundreds of thousands of data points that all need to be correctly organized and monitored to ensure their reliability and quality. You cannot maintain the quality of something you cannot see. That’s why data observability is so crucial to data quality management. However, observability is a very difficult thing to achieve. Sometimes, organizations will spend thousands of dollars hiring engineers to manually build checkpoints at every step of the data pipeline. Other times, businesses will just hope that nothing goes wrong. Fortunately, there’s a better way. Acceldata’s Torch data reliability and governance solution shines a light on your data and automates the observability approach so that you can achieve strong data quality management.

Although big data, complex data pipelines, and cloud-based infrastructure are all fairly new, data quality itself is not new. For years organizations have been developing rules and frameworks to help ensure that they are only using the best data to make decisions. It’s important to know the data quality management best practices if you are to implement a successful strategy. As we’ve already mentioned, one of the most vital best practices in this space is to get buy-in from top leadership and the company as a whole. Everyone responsible for any aspect of data management needs to understand and implement data quality improvement techniques. Another best practice is to identify the measurable factors that you can use to quantify the quality of your data. Finally, a third best practice is to thoroughly investigate the causes of data quality problems. All of the strategies that make up the data quality management process are easier and more effective when done through the lens of data observability. A platform like Acceldata could be one of the best data quality management tools in your organization because of its ability to simplify and streamline a significant number of data management tasks. The right data quality tools give you the visibility you need to understand where your data is coming from, where it’s going, and how it’s going to get there.

Data quality management framework

An effective data quality management framework is comprehensive and covers all of the six main pillars of data quality we have already mentioned. It should cover the entire data cycle from the source to the analysis by end-users and everything in between. This includes establishing policies that manage the handling of data. Over 70% of employees currently have access to data they should not be able to see. Solving problems like this is impossible without having solid data observability. For example, Acceldata Torch gives you the ability to automate much of your data quality management with an AI system that is able to improve dynamic data handling and detect invisible errors such as schema and data drift. Referring to a data quality framework implementation guide is often a great way to learn more about the particular checks you should have in place to ensure that your data quality is effectively managed. Examples are always a great way to learn something, which is why a data quality framework template is another tool that can help you build out your framework. It shouldn’t be too hard to find a data quality management framework PDF download that can help walk you through the various tasks you need to complete in order to build a strong data quality management process. Just as there are tools for data storage, manipulation, and analysis, there are also several data quality management tools available on the market today. The key is understanding the difference between a tool that’s going to help you achieve your goals versus a passive tool that’s no longer big data compatible.

Data quality management examples

We’ve discussed much of the theory surrounding good data quality management. However, from a practical perspective, what does this process actually look like? What are some data quality management examples? One great example of data quality management is data profiling. Data profiling is the process of monitoring and cleaning up data to enable organizations to make better, insights-driven decisions. Traditionally, data profiling is a complex, time-consuming task requiring hours of manual labor. However, with Acceldata, you can introduce automation into the process, freeing up your teams to focus on analysis and decision-making instead of cleaning and organizing. Other data quality examples include smart data handling policies and data validation tasks. Data validation is another area in which a data reliability solution such as Torch from Acceldata can be very useful. Torch enables you to get the context you need with machine-learning-based classification, clustering, and association. Plus, our platform also offers a dynamic recommendations feature that can help you rapidly improve data quality, accuracy, coverage, and efficiency.

Good data quality starts with a data quality management definition. This definition should have buy-in from your company’s top leadership. The framework you build and implement needs to clearly define the data quality management roles and responsibilities as well. Furthermore, your plan should also describe the kinds of data quality tools that are going to be used.


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