5 Ways to Improve Data Quality
What is Data Quality?
Data Quality refers to the level to which a company’s data assets satisfy various metrics, most notably, completeness, accuracy, timeliness, consistency, and reliability. Following these metrics, companies can measure how well the data serves their needs.
The relevance of data in the business world has grown over the last decade or so. Companies, nowadays, can access swathes of big data, which means that the influence it has on all aspects of a company’s operations is growing.
It can, therefore, come as no surprise that companies place great importance on data quality. Given that certain data will be the source of some potentially significant decision-making, the value in its quality is clear as day. But how exactly does a company ensure elevated levels of data quality within its organization? Let us look at six strategies you can follow to improve data quality.
5 Ways to Improve Data Quality
1. Push for an enterprise-wide understanding of what data quality is
To maintain data consistency, one of the critical representations of data quality, we first need to instill a mutual understanding of data quality. From directors to developers, everyone within an organization will have a varying degree of knowledge about data quality and, more importantly, differing interpretations. It is, therefore, critical to ensure everyone understands it and that their perception of what constitutes ‘good’ quality data aligns with that of the company.
2. Establish robust data governance policies
Now that your workforce is familiar with data quality, the next step is to emphasize how important it is to achieve institutional goals. Nowadays, becoming a data-driven organization can only partially depend on technological resources. There also needs to be a culture of data acknowledgment. An effective way of doing this is to invest considerably in data governance practices. Data governance is a set of standards, processes, and policies to ensure the proper management of data. Effective data governance strategies will ensure that an organization’s employees adhere to predefined data quality standards.
3. Employ the appropriate personnel
Of course, we all know the role data analysts play. But who will ensure that analysts are provided with high-quality data? That is where data stewards come in. Data stewards often work closely with analysts and manage data quality. Typically, they utilize various tools and software to ensure quality standards are maintained and will identify and fix any problems that may arise.
4. Get to the root of the error
When we encounter something wrong, our first reaction is to get rid of it by any means possible. However, the same cannot be said of data. We must still use even low-quality data, albeit for different purposes. Rather than simply locating it and wiping it from the system, a more effective action plan would be to determine why and how it came to be.
Much like criminal profiling, it’s not enough to know of a crime; we must investigate and ultimately identify the guilty party. However, most companies have hundreds of thousands, if not millions, of data assets, making human profiling like finding a needle in a haystack. Finding a software solution that incorporates profiling tools to identify the root cause of the issue is a great way to automate this process. Such devices can investigate different elements (columns, patterns, duplicates, etc.) and often include customizable features that the data steward can use to their advantage.
5. Prevent future errors from occurring
Okay, so we’ve identified the source of the error and weeded it out, but it’s equally necessary to put measures in place to stop any recurrences of similar mistakes. While the data profiling deployed in step 4 would have identified the source of the problem, tangible action still needs to be taken to prevent such a problem from regularly corrupting enterprise data.
Follow these steps to achieve data quality excellence
According to Gartner, on average, lousy data costs companies 15% of their total revenue every year. Maintaining high levels of data quality remains a challenge to all. But we hope that, with the steps outlined on this page, you will feel you have learned five new ways to improve your data quality management strategy.