Data has a tremendous impact on the trajectory of an organisation as it relates to predicting customer behaviours and buying patterns, assisting with effective product management, providing organisations with competitor information and more.

However, if your data is not accurate, complete and consistent it can lead to major missteps when making business decisions. Gartner estimates the average financial impact of poor data quality on businesses at $15 million annually, which means you cannot afford to not make data quality management a priority especially now that the General Data Protection Regulation (GDPR) standards have been rolled out. With GDPR, the way in which organisations can use their data now comes with restrictions. To ensure compliance with GDPR, data quality management must be implemented in organisations to operate correctly and use data in line with regulations.

What is Data Quality Management?

Data quality management (DQM) refers to a business principle that requires a combination of the right people, processes, and technologies with the common goal of improving the quality measurements that matter most to organisations. The ultimate purpose of DQM is not just to improve data quality for the sake of having high-quality data, but rather to achieve the business outcomes that depend upon high-quality data.

Effective DQM requires a structural core that can support data operations. To have a strong organisational structure, IT leadership should create specific roles when implementing DQM practices across the enterprise such as a DQM Program Manager, Organisation Change Manager, and Data Analyst. These roles set the tone for data quality and help establish data quality requirements.

Defining data quality can help give your organisation a standard to uphold. Critical points of defining data quality may vary across industries and from organisation to organisation. Defining these rules is essential to successfully use business intelligence software. Main characteristics to consider in regards to quality data could be integrity, completeness, validity, uniqueness, accuracy and consistency.

To ensure these characteristics are satisfied each time, experts in data protection recommend following guiding governance principles when implementing a DQM strategy. These principles should include accountability, transparency, protection, and compliance. By adding in measures such as data profiling and data reporting, enterprises will automatically be able to better fulfil GDPR requirements through compliance.

Once potentially bad or incomplete data has been sorted out, it’s time to make appropriate data corrections such as completing the data, removing duplicates or addressing some other data issue.

Five Best Practices for Data Quality Management

For businesses starting to implement data quality management processes, there are practices that can lead to successful implementation.

First, when starting with DQM, do an audit of your current data. This involves taking inventory of inconsistencies, errors, duplicates, and recording and correcting any problems you come across to make sure the data that goes into your infrastructure is as high-quality as it can be.

Second, create Data Quality Firewalls. A firewall is an automated process that prevents and blocks a figurative fire. In this case, the fire is bad data. Putting up a firewall to protect your organisation against bad data will help keep the system clear of error. User-error is easy, and firewalls help prevent it by blocking bad data at the point of entry. The number of people allowed to feed data into an infrastructure affects the quality of data. But in many large organisations, it’s imperative to have multiple entry points. A firewall helps data stay error-free even when there are a number of people entering data.

Third, no enterprise business can justify the resources required to comb each and every data record for accuracy all the time. But integrating the DQM process with BI software can help to automate it. Based on predetermined parameters, certain datasets can be isolated for review. For instance, new data sets that are likely to be accessed often can be audited as part of the DQM cycle.

Fourth, as described above, there are several positions within your organisation that have accountability over the data quality process. Ensuring these positions are seated and dedicated to the job, means ensuring governance standards can be met consistently.

Lastly, creating a data governance board helps protect businesses from the risk of making data-driven decisions. The panel should consist of business and IT users and executives. The group will set the policies and standards that become the cornerstone of data governance. In addition, the data governance board should meet periodically to set new data quality goals and monitor the success of the board initiatives DQM across the various lines of business. This is where developing an objective measurement scale comes in handy since in order to improve data quality, there must be a way to measure it.

Data Quality Management is a Marathon, Not a Sprint

Big data is an important component of doing business in today’s digital world. It is now even more important with the implementation of GDPR. It offers customer and competitor insights that can’t be achieved with any other tools or resources, and if done correctly, it can aid compliance with GDPR regulation.

Because of its high velocity, big data is accessible to business leaders who can use it to make valuable decisions in real-time. But for that reason, it’s also associated with business risks that need to be managed properly, and DQM is one effective tool for achieving just that.Overall, DQM offers many benefits to your organisation because it can help you to get the right data the first time, you have a better view of what’s going on with your stakeholders (customers, vendors, marketers, etc.) and DQM helps to drive more informed business decisions. With these considerations in mind, it is also important to remember that DQM is an ongoing process that requires continuous data monitoring and reporting. Spring cleans are always hard but always worth the effort, and now it’s time for organisations to do the same and implement DQM to help effectively follow GDPR.

By Paul Cant, Vice President EMEA, BMC Software