Data is a valuable resource in today’s digital world, especially as the internet-of-things (IoT) technologies and devices become more common. The more devices your business uses, the more data volumes you collect. Often, managing huge volumes of data from different sources can lead to several quality problems. Data quality measures your data set against specific data standards and established data quality metrics. In this article, we talk about common data quality KPIs and how to determine if your company’s data is high quality.
Many people have different opinions and standards for measuring data quality. So, a quick query on Google for “what is data quality” can lead to ambiguous definitions since the final call rests in every data organization’s hands. However, effective data quality management revolves around generally accepted key performance indicators, including accuracy. Data accuracy is one of the main data quality metrics organizations use in checking the overall data quality of an organization’s data.
Your company’s data can be considered high quality and accurate if the data professionals can represent the data values in a consistent and unambiguous form. Accurate data connotes error-free and correct data regardless of the data volumes in your database. Data accuracy is a non-negotiable attribute. Inaccurate data can have several implications for businesses, including inflating operational costs, faulty predictions, and uninformed decision-making. The more accurate your data is, the more confidence you can have in your data analytics and decision-making process.
Data can have missing values, especially when you use manual processes for data entry and collection. Any human error from your data teams can lead to missing records that undermine data completeness.
Data completeness is a dicey metric. Sometimes, incomplete data is still usable, and often it’s tempting to overlook data completeness in such cases. But using data with missing values can lead to costly mistakes and untrue conclusions.
The best way to ensure data completeness is to have standards for your data entry or a scale to measure what level of missing values can be tolerated. It’s also essential to use the right tools like a people search engine to collect data, especially when performing a background check on a specific group or individual. A people search engine connects to public and private sources of information, bringing up arrest records, court records, and the information you need to complete your data entry.
The concept of data quality can also apply to how timely you can access your company’s data. Data timeliness focuses on how up-to-date your data is, and you can measure this metric by assessing the expected time for information delivery and the actual time the information becomes available for use. Data timeliness is an essential dimension of data quality for business analysts and decision-makers. For this reason, modern organizations can invest good money into good software to ensure they can always access data in real time.
Using this scenario better explains the importance of timeliness. Suppose you’re closely leading an industry with a competitor eager to establish its dominance in your industry. That means every change in your customer records or target audience behaviors demands quick changes to your marketing strategies. Whoever understands these changes first and tailors their strategies accordingly wins the market.
Data users produce over a trillion megabytes of data each day. This number is always expected to surge, mirroring the world’s fast digital transformation rate, especially with mobile phone and internet proliferation rates increasing globally. However, generating large amounts of data is one thing, and extracting relevant data and insights is another. Often, the latter is challenging because businesses gather data from disparate sources, including cloud systems and legacy applications. Relevant data always meets the requirements for its intended use, and the easiest way to measure data relevance is to assess the relationship between your data and your business objective or organizational needs.
All in all, these metrics can attest to your data’s quality and can improve decision-making and business intelligence efforts.