What Is Data Quality Management and Why Is It Important?
The Data Age is a new era in the world. The Data Age means that today’s data is more abundant than ever before. This amounts to 2.5 quintillion bytes per day. Data is created every time someone sends an e-mail or text, downloads an application, or sends any number of seemingly insignificant things, and this has led to a massive explosion in data. Your organization can be more data-driven than it is overwhelmed by data. Data-driven companies share a common trait: they have a data quality management program in place to ensure that they work with the best data.
Why Organizations Need Data Quality Management
Organizations are increasingly aware of the importance and necessity of data quality management, from the top down. Common threads are driving the need to ensure data quality. These include the integration of new data sources, especially unstructured, with existing systems, the financial investment required, and the competitive pressure to maximize all enterprise data. Also, the difficulty in extracting data from its silos. Harvard Business School published a study that found 47% of new data records contained at least one error. MIT Sloan’s astonishing study found that bad data can result in a loss of 15-25% of total revenue.
Bad data does not have to be costly for your company. A strong data quality management program will ensure data integrity. All data and are available to anyone who requires them in a safe and controlled manner. Data quality management is about finding the right combination of people with the right tools and the right approach.
Also read: Why You Need to Perform a Data Quality Audit
A Collaborative Path to Data Quality
A small IT team, or just a few data professionals, should not be able to manage data quality management initiatives. Data is a team sport. Everyone from IT to data scientists to business analysts to application integrators should be able to participate in the extraction of valuable insights from continuously available, high-quality data.
It is important to work together with data when you embark on a data quality management program. Otherwise, you might become overwhelmed by how much work it takes to validate trusted data. A Wikipedia-like approach to data curation allows everyone to participate. This will allow the business to get involved in the transformation of raw data into something trusted, documented, and shared.
IT and other support agencies such as the CDO office need to set the rules and provide an authoritative approach to governance when required (for instance, for data privacy or compliance).
It is important to foster a collaborative approach so that content providers and curators can be made from the most competent business users. Utilizing smart, workflow-driven self-service tools with embedded quality analysis controls. You can create a trust system that is scalable.
A Unified Data Quality Management Platform
Many tools for data preparation or stewardship offer many benefits in fighting bad data. Only a handful of these tools cover data quality. These standalone, specialized data quality management tools often have complex user interfaces that require deep expertise to be deployed. These tools are powerful but you’ll miss your deadline if you don’t have any short-term data quality priorities.
You might also find simple, but often powerful apps that are too isolated to be integrated into a comprehensive data quality system. Even if they focus on business people, even if they have a simple interface, they will overlook the important part of collaborative data management. That’s exactly the problem. The tools and capabilities are only part of success. It’s also about their ability to communicate with each other. Platform-based solutions must be able to share, operate, and transfer data, actions, or models together.
Also read: 5 Best Data Quality Issues and How to Fix Them
Multiple use cases will arise where it may not be possible for one person or group to successfully manage your data. Collaboration with business users and empowering them in the data lifecycle will give your team and your superpowers to overcome common obstacles like cleaning, reconciling, or matching your data, or resolving it. These are some ways data quality tools can help your data-driven company:
- Analyze your data environment: Data profiling The process of assessing the condition and character of data stored in different forms throughout an enterprise is often recognized as a crucial first step towards gaining control of organizational data.
- Share quality data safely: You can share only production-quality data on the premises or in cloud-based apps without exposing Personally Identifiable Information to unauthorized persons.
- Manage the data lifecycle: Data stewardship is the process of the creation and maintenance of data models, documentation, cleaning, cleansing, and policy definitions. This allows the execution of well-defined processes that include monitoring, reconciliation, and refining, deduplication, cleansing, and aggregation. It also helps deliver high-quality data to end users and applications.
- Prepare and share data quickly: Many people still spend too much time in Excel crunching numbers or expecting colleagues to do the same. Data preparation tools let anyone access data sets and then clean, standardize, transform or enrich them. This shared ownership eventually drives collaboration between IT and business.
Bad data quality can lead to lost opportunities and bad decisions. It also costs time and money to find, clean up, and correct errors. The best way to ensure data quality is through a solid data quality management program that includes the right combination of technology and people.