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Data quality management: process, metrics, and best practices

Data quality management

Despite all the benefits of data for businesses, low-quality data sets bring real challenges to entrepreneurs. One example is lost invoices alongside unnecessary expenses. And here is where data quality management comes into play, helping companies and business leaders avoid inaccurate manual data reprocessing and achieve regulatory compliance. PNN Soft developers are skilled in assisting companies in establishing Data Quality support rules and organising data, thereby making sense of it. 

How to find out whether your company deals with poor data? Here are some examples of data sets that need improvements:

We will dwell on obtaining high-quality data – a robust foundation for acquiring business intelligence insights and enhanced analytics. 

Brief guide on how to measure data quality?

The crucial thing for data quality metrics is that they must be clearly defined and top-notch. It is not a one-time task, instead reqrers ongoing effort. Regularly measuring and improving data quality will enhance the reliability and usefulness of your data for better decision-making. What are metrics categories, then? Let us now consider what they imply. 

The criteria are based on the degrees to which information reflects the described object or event. 

The information should be enough comprehensive to fulfil the company’s needs. The data is complete if you can draw insightful and meaningful concussions out of it. 

Data sets from two sources do not contradict each other. However, the correctness of sets is to be checked separately as consistency does not always mean correct.

The criterium defines unintended mistakes and determines whether particular information corresponds to a relevant data type via structural data testing. 

 Measure data storage costs. Outdated data might lose relevance and value over time.

Data quality analysis also comprises additional metrics, such as the ratio of data sets to errors, the number of empty values indicating missing info or data within wrong fields. The latter indicator works this way: if data storage costs decrease, but the data operation level stays the same or slightly grows, data quality will likely be refined. Data time-to-value estimates of how long it takes to derive insights from information and data transformation error rate critical for analysing the probability of unsuccessful data transformation are no less important. 

Data quality control procedures

As we already mentioned, each step of dealing with data is to support its accuracy. The data management process contains multiple stages, enabling companies to acquire, implement, distribute and analyse information. Consequently, you achieve smarter and more grounded decision-making on product development and communication strategies in changing market conditions. 

Data quality tools provided by PNN Soft company include a range of functionality. The most common data management practices are the following:

Improving data quality with PNN Soft

Our team of developers has extensive experience in enhancing data quality. We have been utilising diverse metrics to handle data problems efficiently. 

We work with market leaders by analysing the individual needs of given companies and ensuring constant communication with partners with the help of Agile and Scrum methodologies. Contact our experts if you want to stay competitive in changing digital marketplace conditions and benefit from Business Intelligence and other data analysis tools. We will gladly help you create an individual and efficient data quality improvement strategy.