Is my data reliable? 4 ways to check

Data quality in business determines the accuracy, completeness, consistency and reliability of any information used for decision-making and operations. High-quality reliable data is important for effective business strategies and customer satisfaction, while poor data quality can lead to bad business decisions, inefficiencies and lost opportunities.

This article will go through four ways of checking the reliability of the data in your business.

Data Validation

Data validation involves checking your data against predefined rules or criteria to find errors, inconsistencies or missing information. It can be done manually or through automated systems that flag potential issues.

For example, you might want to check that all your customer phone numbers follow a standard format or that product prices fall within an expected range. If you validate your data, you can catch and correct errors before they impact your business decisions.

Cross-referencing Data

Cross-referencing is when you compare your data to external databases, industry benchmarks or historical records to identify any discrepancies. This process can help you spot outliers or inconsistencies that may identify data quality issues.

Also, if you compare data across different departments or systems within your organisation, it can show inconsistencies and make sure that information is aligned across your business. A data collection company, such as shepper.com/, can be of great help in analysing your data.

Regular Audits and Monitoring

Having a system of regular audits and monitoring is important for making sure your data is reliable. Auditing can involve reviewing your data quality processes, making spot checks and analysing key metrics related to data accuracy. These processes can also be completed by AI to save time

Data Profiling

Data profiling is when you examine and analyse your data to try and get a better understanding of its structure, content and quality. This process involves analysing data patterns, distributions and relationships to identify any issues or anomalies. It can uncover hidden problems such as duplicate records, outliers or inconsistent formatting, gain insights into the overall health of your data and identify areas that may require further investigation or cleaning.