Data quality management: process, metrics, and best practices

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:

• inconsistent data
• incomplete data
• poorly defined data
• poor data security
• poorly organised or duplicated data.

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. 

• Accuracy

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

• Completeness

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. 

• Consistency

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.

• Integrity

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

• Timeliness

 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 Outdated data might lose relevance and value over time.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:

• Cleansing. Aims at enhancing substandard data representations, reformatting data and deleting duplicated records. In addition, this tool is indispensable for customising data in line with individual companies’ requirements and standardisation rules. 
• Monitoring. Dwell on implementing automated instruments for checking the quality of data. Usually, companies create individual metrics and KPIs (key performance indicators), so the further goal lies in evaluating them against configured data quality baselines. In cases of non-meeting quality thresholds, the system immediately notifies administrators about this. 
• Profiling. Implies both monitoring and cleansing instruments. The objective is threefold: first, to establish data relationships; second, to verify information against descriptions; and finally, to evaluate data in line with a standard statistical baseline. Thanks to profiling, companies can uncover any inconsistencies in information promptly. 
• Parsing. Support automatic recognition, which is crucial for improving the data quality process. For instance, it defines if information conforms to recognisable patterns, such as the name/surname of a person or the geographic code of a telephone number. 
• Matching. Analyses all the information duplicated within the single data source, including approximate and exact matches. 
• Standardisation. Repairs acronyms, inconsistent or wrong capitalisation and punctuation if placed in the wrong field. In other words, the approach helps ensure the consistency of data format. 
• Data enrichment. Focuses on adding necessary information to prevent incomplete data sets. It requests missing data parts from another source to supplement your current solution (typically used during migrations). 

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.