The more data, the better. Today’s organizations are collecting a vast amount of data from all areas of the business. Data is captured through transactions, website navigation, product development, and way more—and that’s just touching the surface. Although, what value are all these insights providing your business if they are inaccurate?
The answer to inaccurate data is proper data quality management. A good management strategy is the cornerstone to managing multiple streams of data while ensuring high-quality insights are being leveraged for the most important business initiatives.
What is data quality management?
Data management is the process of acquiring, validating, storing, protecting, and processing data. While a data management program is crucial to streamlining business insights, data quality becomes its right-hand—this is where data quality management matters. Data quality management ensures that this data remains accurate and reliable throughout each of these steps.
As you start thinking about data quality management, it’s crucial to review how accurate your information is in each stage of the data management process and how you intend to keep it that way. Is your data accurate at the point of collection? Are you confident in leveraging the insights found in your current database?
An effective data management strategy is necessary for organizations looking to stay one step ahead of their competition. Proper data quality management strategies include
When you lay a foundation of data quality, you can empower your business to make better and faster decisions. More importantly, you can leverage that competitive edge as you shift with the ever-changing market.
What is data quality and why does it matter?
Data quality refers to the accuracy and usability of the consumer data that you collect. This data typically includes addresses, email addresses, and phone numbers. High-quality data should be free from typos, formatted properly, and without any incomplete information.
Companies can lose millions of dollars in revenue each year due to just poor data quality alone. This revenue loss can come from failed mailing deliveries, return fees, and lost opportunities. Fortunately, data quality can help. Characteristics of data quality control include:
- An improved customer experience.
- Better insight for decision-making.
- A greater chance for innovation.
- A higher ROI
Data quality control is a fundamental component to the success of a data management program. Think about it—how can you or your team effectively manage data that could be inaccurate and untrustworthy? Our research has found that 50 percent of organizations say their data is inaccurate due to human error.
With the right tools and data management program, your business can reduce errors in data for improved data quality, communication, and overall success. A complete and trustworthy database gives you confidence that you are leveraging accurate customer insights to drive business opportunity.
Through data validation and data enrichment capabilities, you can rest assured that your data-driven decisions are based on the right data.
Learn more about data quality solutions
Best practices for data quality management
It’s no secret data quality management is a key driver when it comes to successful business outcomes. However, to maintain high-quality data, it is crucial that your business has an effective plan and roadmap. As your organization begins to adopt data management practices, it’s important to remember that managing data should be an ongoing discipline. Let’s dive into the best practices:
Prioritizing data quality strategy
The first step to good quality is having a strategy for how data quality will be maintained and used by your organization. For data quality management to work, it is also vital that everyone on the team is on the same page and understands the benefit of data quality.
Mapping out a rigid strategy as a team will help ensure every function runs smoothly. A commitment to high-quality data and data-driven decision-making will be the difference between an effective data quality management strategy and a half-hearted one.
Creating roles
Once the team develops, and commits to, an effective data quality strategy, there should be clear roles established. Common roles include a dedicated department for managing data and a Chief Data Officer. These roles allow a clear and specific focus on data quality monitoring to provide the greatest success.
The entire team should be included in the data-quality process, as data-driven decision-making requires everyone to understand how data is measured and used. Part of this process includes democratizing data so everyone can access it and teaching data skills so everyone knows how.
Cleansing data
Data cleansing is one of the most critical parts of a data quality monitoring strategy. There is a wide range of common data quality issues that can affect your database. With the right tools, you can ensure clean data enters your database and that your database is consistently refreshed. Data cleansing targets duplicate data, formatting issues, and typos to keep data accurate.
With a strong data governance strategy, your team can continuously ensure that data is clean and high-quality as it is stored and transferred.
Profiling data
With clear roles and high-quality data, the team can then focus on preparing data for effective use. Profiling data involves examining, summarizing, and analyzing it all, making it easier for everyone to understand and interpret.
Empower your business to adopt data quality management through these best practices. Before you know it, your data will be used across the entire enterprise to make better decisions, streamline operations, and improve customer experience, all while watching your bottom-line growth take off.
Data quality monitoring solutions
It’s no secret data quality management is a key driver when it comes to successful business outcomes. However, to maintain high-quality data, it is crucial that your business has an effective plan and roadmap. As your organization begins to adopt data management practices, it’s important to remember that managing data should be an ongoing discipline. Let’s dive into the best practices:
Prioritizing data quality strategy
The first step to good quality is having a strategy for how data quality will be maintained and used by your organization. For data quality management to work, it is also vital that everyone on the team is on the same page and understands the benefit of data quality.
Mapping out a rigid strategy as a team will help ensure every function runs smoothly. A commitment to high-quality data and data-driven decision-making will be the difference between an effective data quality management strategy and a half-hearted one.
Creating roles
Once the team develops, and commits to, an effective data quality strategy, there should be clear roles established. Common roles include a dedicated department for managing data and a Chief Data Officer. These roles allow a clear and specific focus on data quality monitoring to provide the greatest success.
The entire team should be included in the data-quality process, as data-driven decision-making requires everyone to understand how data is measured and used. Part of this process includes democratizing data so everyone can access it and teaching data skills so everyone knows how.
Cleansing data
Data cleansing is one of the most critical parts of a data quality monitoring strategy. There is a wide range of common data quality issues that can affect your database. With the right tools, you can ensure clean data enters your database and that your database is consistently refreshed. Data cleansing targets duplicate data, formatting issues, and typos to keep data accurate.
With a strong data governance strategy, your team can continuously ensure that data is clean and high-quality as it is stored and transferred.
Profiling data
With clear roles and high-quality data, the team can then focus on preparing data for effective use. Profiling data involves examining, summarizing, and analyzing it all, making it easier for everyone to understand and interpret.
Empower your business to adopt data quality management through these best practices. Before you know it, your data will be used across the entire enterprise to make better decisions, streamline operations, and improve customer experience, all while watching your bottom-line growth take off.
Explore data quality management tools for your business
Data quality management can be a very involved and seemingly tedious process, but it is well worth it. By prioritizing data quality monitoring, you can improve customer communication, increase conversions, and boost revenue.
Experian can help you take your data quality to the next level with our best-in-class data quality management solution. From our email verification tools to our address verification tools, we have a full suite of tools ready to assist you with your data management strategies. Contact Experian today to learn more about your options.
