Data is at the heart of everything important for businesses these days. Whether it’s customer data, sales forecasts, supply chain scheduling, or any other critical process, data drives most business operations. That’s why the field of data quality management (DQM) has become so vital, especially in the booming era of big data. 

DQM has grown from being a tools-oriented set of activities for the IT department to a full spectrum of data management and governance best practices. It focuses on employing processes, methodologies, and advanced technologies to make sure data meets specific quality requirements, with the ultimate goal being trusted data delivered timely to business units. While many companies today employ systems architects to integrate applications, they often fail to recognize the importance of data architects who are fully prepared to understand and build powerful data models and interfaces. 

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Building a Data Quality Management Framework

Ensuring data quality doesn’t have to be the ad-hoc activity it is for many companies. The right framework can help any company wrap their arms around the complexity of the data problem and create a manageable process to safeguard the integrity of this most precious asset. The following overview gives a simple and workable Data Quality Management framework.

  • Create a Role-Based Organizational Structure

The first step for good DQM is to define the critical roles within the IT group. They include DQM program managers (focused on establishing data quality requirements, managing day-to-day data measurement tasks, team scheduling, and budget management); change managers (charged with managing shifting needs of data when it’s used across infrastructure and processes); data analysts (who interpret and report on the data); and data stewards (focused on turning data into a corporate asset). 

  •  Build a Data Quality Definition

Defining data quality rules is essential to squeeze the most value out of data. High-quality data should include details about data integrity (how it maps to quality standards), completeness (how much of the data is being acquired), validity (how it conforms to data set values), uniqueness (how often it appears in a data set), accuracy (for each need), and consistency (data holds the same value in different sets). 

  •  Profile and Audit Data

Data auditors look to validate data against metadata and other existing metrics, then report on data quality accordingly. Data profiling technology helps uncover data quality issues such as duplication and lack of consistency, accuracy, and completeness. 

  •  Data Monitoring, Reporting, and Error Correction

Finally, companies must monitor and report on data exceptions, which are usually captured in business intelligence software to ensure bad data is identified before it’s used. Once bad data is uncovered, it can be corrected or de-duplicated as needed. 

The Importance of Data Quality Management for Compliance

One of the most important uses of data quality management is for business compliance to government mandates. Many companies gather and process sensitive personal and customer data, as well as private third-party and IoT data. Data privacy regulations such as GDPR require that businesses correct inaccurate or incomplete personal data, making validation a critical process. 

Data quality management ensures that a company can identify, classify, and document internal and external personal information to meet GDPR compliance, and on a broader scale to measure the completeness, accuracy, and timeliness of data. Not meeting regulatory compliance can be costly for companies as well. According to a recent report from DLA Piper, GDPR fines increased by nearly 40 percent in 2020, showing the willingness that regulators have to use their powers of compliance enforcement.

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Common DQM Tools and Techniques

Once data quality rules and targets have been established, there are many tools available on the market that can help data architects and IT managers ensure a smooth DQM process. Some of the most important technology tools include: 

  • Data Scrubbing.

Used to fix data errors and enhance data sets by augmenting the data with missing values, more current information, or additional records. Results can then be measured against performance targets, and any shortcomings can provide a vital starting point for the next round of data quality improvements. 

  • Data Profiling.

As mentioned above, profiling tools help analyze data sources and collect metadata to identify the origin of data errors. They create data handling rules, data discovery relationships, and automated data maintenance and transformation. 

  • Collaboration Tools.

Provide a shared environment and workflow where data repositories can be analyzed by data quality managers, data stewards, and change managers. 

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Conclusion: Big Data and Data Quality Go Hand in Hand

Big data is a key driver of improving business operations in every facet of the business. Data quality management is becoming a vital tool as big data initiatives crop up around multiple business units and customer-facing activities. Accordingly, Big Data Engineers are vital players in using data to its fullest extent to supercharge their enterprises, and to meet the growing requirements of government regulators. 

About the Author

Stuart RauchStuart Rauch

Stuart Rauch is a 25-year product marketing veteran and president of ContentBox Marketing Inc. He has run marketing organizations at several enterprise software companies, including NetSuite, Oracle, PeopleSoft, EVault and Secure Computing. Stuart is a specialist in content development and brings a unique blend of creativity, linguistic acumen and product knowledge to his clients in the technology space.

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