Data Quality

The Data Consultant expert advice for business on analysing data quality audit - best tools for data analysis

Here at The Data Consultant, we understand the critical role that high-quality data plays in driving business success. Whether you’re a small start-up or a large corporation, ensuring the accuracy, consistency, and reliability of your data is essential for making informed decisions and staying ahead of the competition.

“Poor data quality is one of the primary reasons why data initiatives fail. Investing in data quality is not just a good practice; it’s imperative for business success.”

Thomas C. Redman, The Data Doc.

The Cost of Bad Data Quality:

  • Inefficient processes and decision-making lead to wasted time and resources. According to a study by Experian, poor data quality costs UK businesses an average of £23.9 million per year.
  • Decreased customer satisfaction and trust due to inaccurate information. Research conducted by the Data Management Association UK (DAMA UK) found that 89% of UK organisations suffer from data errors that impact their bottom line.

An article in the Harvard Business Review suggested that on average 47% of newly created data records have at least one critical error. On the other hand good data quality can be a competitive advantage and provides better leads, better understanding of customers and better customer relationships.

Your database will never be perfect, but we can help you put in place good data quality practices and that will benefit your commercial reporting, your sales and marketing activities and your performance management.

The key is to find the errors, fix them at source and most importantly prevent them from happening again in the future.

5 Steps to Data Quality

Define Data Quality Standards:

Clearly define what constitutes good quality data for your business. This involves setting specific, measurable standards for accuracy, completeness, consistency, and timeliness. Standards will vary depending on the type of data and its intended use.

Implement Data Entry Best Practices:

Train staff on the importance of data accuracy and establish clear guidelines for how data should be entered into systems. This could include using form validations, drop-down menus, and regular checks to minimise human errors during data entry.

Regular Data Audits:

Schedule periodic audits to check the quality of data across your systems. Use these audits to identify and rectify inconsistencies or errors. This helps in maintaining the reliability of the data and can uncover areas where data capture or entry processes might be improved.

Utilise Technology Solutions:

Invest in technology that can automate data quality checks and data cleansing processes. Software tools can help detect duplicates, correct errors, and fill in missing entries, ensuring the data remains clean and useful without requiring extensive manual oversight.

Create a Data Quality Management Culture:

Foster a culture where data quality is everyone’s responsibility, not just that of the IT department. Encourage employees to report discrepancies in data and suggest improvements to how data is handled. This collective approach can significantly elevate the quality of your data.