What is Data Quality Management?

In today's digital age, data has become a critical asset that guides the direction of businesses of all sizes. Organizations rely on data to gain insights into customer behavior, improve operational efficiency, and make informed business decisions. However, the value of data is only as good as its quality and reliability. 

Poor data quality and reliability can lead to incorrect or incomplete analysis, which can have serious consequences for the business. Continue reading the article to explore the data quality management framework and best practices that businesses can use to improve the quality and reliability of their data.

What is Data Quality Management?

Data Quality Management includes a specific set of practices that can enhance the quality of data being utilized by businesses. 

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:

• Accuracy
• Completeness
• Consistency
• Freshness
• Validity
• 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. It 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.

Best Practices for Data Quality Management ‍

The increase in the demand for data in the contemporary digital age has led to a remarkable challenge - a data crisis. A data crisis results from poor-quality data which makes it unusual or unreliable for businesses. Data Quality Management prevents these crises from occurring by providing a quality-focused foundation for an enterprise’s data. 

Data Quality Management has become an essential process that helps businesses to make sense out of data. It helps to ensure that the data present in systems is appropriate to serve the purpose. Here are the best practices for data quality management.  

Assessing Data Quality and Reliability

The first step towards improving data quality and reliability is the assessment of the current state of your data. You can start by identifying the sources of their data, which will include both internal and external sources, such as customer data, sales data, and third-party data. Once you identify your data sources, you can evaluate your data for completeness, accuracy, consistency, timeliness, and validity. This will help you identify areas for improvement and develop a plan for data cleansing and normalization.

Data Governance

Data governance plays a critical role in maintaining data quality and data reliability. It involves establishing a data governance program to ensure proper management, security, and quality of data. This includes developing policies and procedures for data management, assigning roles and responsibilities, and ensuring compliance with relevant regulations and standards.

Data Cleansing and Normalization

One of the most effective ways to improve data quality and reliability is through data cleansing and normalization. This involves identifying and correcting errors and redundancies in data, standardizing and normalizing data formats and structures, and removing duplicates. Data cleansing and normalization can improve data accuracy, consistency, and completeness, which are essential for reliable data analysis.

Data Integration and Migration

Integrating data from multiple sources and migrating it from legacy systems can be a complex process. However, it's essential to ensure data quality during integration and migration to avoid data inconsistencies and errors. This involves selecting appropriate tools and technologies for data integration and migration, testing and validating data before and after migration, and ensuring data security during the process.

Training and Education

Finally, training and education play a crucial role in maintaining data quality and reliability. It's important to train employees on data management and governance best practices and provide ongoing education to keep them up-to-date with new technologies and regulations. Sharing best practices for maintaining data quality and reliability can also help organizations avoid common pitfalls and ensure maximum value from their data assets.

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.

Ensure Data Quality Management with Acceldata 

Acceldata provides several 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 spend thousands of dollars hiring engineers to manually build checkpoints at every step of the data pipeline. Other times, businesses 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 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.

Final Words

Improving data quality and reliability requires a comprehensive approach that involves assessing data quality, establishing data governance, cleaning and normalizing data, integrating and migrating data, and providing training and education. By following these methods, businesses can maximize the value of their data assets and make informed business decisions. It's important to remember that maintaining data quality and reliability is an ongoing process that requires continuous monitoring and maintenance.

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