Leveraging data for your company’s specific needs has become necessary in today’s business world. Businesses use numerous tools and resources that produce huge volumes of data for their operations. So, it’s only right to put that data to efficient use, leveraging it for better business decisions, competitive advantage perks, and optimum value. However, the type of data you depend on can affect your business outcomes. You need to go the extra mile to ensure that your data is without inconsistencies or data quality issues. While at it, it’s essential to have key performance indicators (KPIs) to measure the effectiveness of your efforts, but what are data quality KPIs? We’ve listed a few below.
Accuracy
Accuracy is one of the most important data quality KPIs and the first component in the data quality framework. It involves all efforts to rid your database of errors, including typos and incorrect data values. Data accuracy is a valuable data attribute for all data professionals, and businesses must endeavor to see that their data conforms to their related objects and the appropriate real-world context. It’s a must because inaccurate data can have several implications for businesses, including higher operational costs and reduced confidence in your business’s insights.
Completeness
The completeness of data relates to its wholeness, so your business’s data sets should have no missing data values. Ultimately, data completeness is the percentage of missing data entries in an entire database. For instance, your data completeness degree stands at 80 percent if a column of 300 entries has 100 missing fields.
Despite the need for data completeness, humans aren’t perfect, and as long as your data management processes rely on human efforts, data quality problems are bound to happen. That’s why automation has become an attractive option for many data organizations. Automated platforms help data professionals perform repetitive tasks with more precision.
That being said, incomplete data can still be usable, but it can lead to costly mistakes and misinformed conclusions. So, establishing standards for data management efforts can be a great way to reduce the occurrence of incomplete data.
Validity
Data errors can happen in several ways. For instance, capturing dates in the “mm dd yy” format when your database is programmed with “yy dd mm” settings can affect your data’s validity. It’s essential to subject your business processes to the required value attributes established by your policies. A key metric of data validity can be the percentage change of data that has values within the domain of acceptable values.
Timeliness
The number of times you can access data whenever you need it is crucial in generating efficient business outcomes. Data timelines measure the difference between the expected time for information delivery and the actual time the information becomes available for use. Business analysts and decision-makers prioritize the timeliness aspect of data quality to enhance their competitiveness in their industries.
Suppose you’re competing with another company to gain the largest market share of your industry. Industry growth focuses on different factors, and changes happen frequently. You’ll need effective software systems to help you understand the changes as they occur. Your competitor gaining real-time insights faster can tailor their strategies to enjoy competitive advantage benefits over your business. For this reason, many businesses today invest in business intelligence systems, increasing their access to effective data analytics.
Consistency
Data consistency is the process of keeping information uniform as your data moves between different points. Ultimately, data consistency ensures different sources keep the same data without any mismatched values. Businesses can measure data consistency in three categories: point-in-time, transaction, and application consistency. You can use online utilities like CHECK DAT and CHECK INDEX to improve the effectiveness of your efforts.
All in all, these key performance indicators can be a great way to ensure data quality and integrity.
