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Critical steps for improving your data quality

It’s no secret that the quality of your data matters. Your organization’s data is not some mysterious entity that exists only in the realm of technology and analytics, but is in fact a competitive differentiator, increasingly used to influence broader business decisions around things like operations and marketing.

But what happens when you have poor data quality across your systems? Or, worse yet, you’re not even sure how bad the problem is? Data-driven decisions are not a fleeting business trend but rather a new reality, and without complete, accurate data you can rely on, the choices you make for your business could be at risk.Get started on the path to data quality improvement with our action plan that you can begin implementing today.        

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Determine what you want from your data and how to evaluate quality

Data quality means something different across different organizations. For some, it’s ensuring that customer contact data is accurate so that shipments are received in a timely manner. For others, it could be complete prospect profiles that help with marketing segmentation efforts. At its heart, data quality is about being fit for a desired purpose.

Your first step is determining the business purpose for using your data and aligning your definition of quality around that. From there, you can develop specific KPIs that are relevant for your organization so you can track data quality improvement efforts. Having metrics in place will not only help you assess the effectiveness of your program, but will also help you define a clear ROI for data quality improvement plans, helping you get buy in (and resources like money and people) for your initiatives.

Assess where your efforts stand today

Before implementing any data quality improvement plan, you need to understand where you stand today. Using the data quality sophistication curve, you can determine where you are related to data management efforts and what your next steps should be. There are four stages of sophistication your organization can fall into:


Data quality sophistication curve

Almost half of organizations today fall into the reactive or unware stage, meaning there is a lot of room for data quality improvement.

Hire the right people and centralize ownership

According to our Data Quality Benchmark report, just 20 percent of organizations have a centralized data quality strategy under one owner. This means most companies see a lot of different, departmental and disconnected strategies, resulting in less effective data quality efforts. 

By assigning a single data owner (including considering hiring for the relatively new title of chief data officer), you benefit from having one person taking the responsibility for the quality and standards around your data assets. In fact, this centralization even correlates with company profits: more companies who manage their data quality with one single owner have enjoyed a significant increase in profits in the last 12 months.

This central data owner should also be supported by a team of data professionals—data stewards, data service officers, analytics professionals, data scientists, etc.—who can help enforce policies and promote the use of data to drive insights across the business.

Implement proactive processes

After getting the right people in place, data quality improvement relies on proactive processes being put into place, making quality control efforts a part of day-to-day activities across the organization. Otherwise, issues will only be found reactively, when they negatively impact the business. Best case, you scramble to fix it; worst case, you impact customers, brand perception and revenue.

Data management practices need to be developed from a business lens, not just a technical perspective, with the primary data stakeholder having a firm grasp of the purpose of data within the organization and the plans to use it. From there, best practices for data quality management and improvement can be developed to fit those needs.

Take advantage of technology

Considering the volume of data organizations are gathering today, it’s not surprising that data quality technology is an essential piece of most data improvement plans. Manual efforts won’t cut it when an organization looks to move up through levels of data quality sophistication. Most organizations are already using technology today for data quality and plan to continue their investment. But the types of tools in place vary greatly, and can be inconsistent, even across the same company.

Implementation of data quality technology solutions across a company can streamline and unify data improvement efforts. This will result in the enforcement of consistent data standards across the organization, which means your data can be more easily used for advanced, comprehensive decision making. 

Whatever your business purpose is for your data, effective decision making starts with good data quality. Learn more about how you can improve your data quality by downloading our paper on creating an ideal data quality strategy.

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