Data Quality Management Tools

Data quality management tools are software systems used to ensure users are always working with the highest quality, most up-to-date data available. Organizations rely heavily on these systems for functions such as:

• Data cleansing—detecting and removing data containing typos, formatting errors, or other issues
• Data deduplication—removing duplicate copies of data
• Bulk updates and automation—updating or deleting multiple records at once, transforming data, finding and replacing, and automating repetitive, time-consuming tasks
• Data validation—ensuring that data is accurate and up-to-date
• Importing and exporting data—updating record data by importing from a spreadsheet and finding certain data to export to a spreadsheet
• Integration—connecting to other software systems to combine disparate data sources

Who uses data quality management tools?

In the time of Big Data, more and more organizations are adopting data quality management tools to keep their data error-free and up-to-date. Since many organizations now work with vast amounts of data, many database management systems cannot process the kind of sweeping actions and complex processes that database administrators (DBAs) and data analysts need to perform. Using a data quality management tool shifts processing to a different system to improve functionality.

Plus, there are real consequences of using outdated, incorrect, or even partially correct data. Some of the most valuable companies in the world (Alphabet, Facebook, Tencent) deal heavily—if not almost exclusively—with data. High quality data is crucial for these companies to generate revenue.

But organizations of all sizes use data quality management tools. For example, many organizations use customer relationship management (CRM) software to store client contact information, job titles, interaction and purchase history, and data on how they interact with an organization’s website or emails. Storing incorrect or incomplete data on a client can lead to an awkward phone call at best and significant lost revenue at worst. Every organization works with data to some degree, and it’s important to make sure it’s reliable.