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Data Quality Audits: How to Improve Your Data

As we see the rise of artificial intelligence (AI) and increasingly connected systems, businesses are turning to data for support. Collecting data on consumers, company successes, and financials lets businesses harness information to make better decisions. Data has never been more critical, but how can you be sure the data you’re using is any good?

With 90% of individuals predicting data’s increasing importance for their business, supporting data quality is critical. Data quality audits are a way to ensure your data is trustworthy. With effective audits, companies can take steps to boost data quality and improve their processes. Your operation needs to understand how conducting data quality audits works to stay ahead of the curve in a digital world.

What Is a Data Quality Audit?

A data quality audit is a review process that examines your data quality to find issues. It examines data quality dimensions (DQDs) like accuracy, consistency, and completeness for problems that could impact your business.

Data quality audits might reveal data issues that affect your compliance or efficiency. Without an audit, businesses have no idea if they can trust their data. Bad data can even affect your customer experience, reducing your reputation. Over time, data can see human error, system glitches, and business changes. Data quality and errors go hand in hand. A data audit uncovers errors so you can correct them and use the best data to make your business decisions.

What Makes Up a Data Quality Audit?

The audit process involves evaluating data sources, entry procedures, and management practices. Each data audit component helps ensure your business has reliable, up-to-date data for all its needs. Breaking down the audit structure lets you tailor the audit process to your organization’s needs.

1. Audit Objectives

Before starting an audit, you need to know your goals. Clear data quality objectives guide your audit, helping you to conduct it efficiently and accurately. Objectives might include identifying outdated or incomplete data, assessing data consistency, or ensuring compliance with data policies. With goals in mind, you’ll get relevant, helpful findings.

2. Types of Data Audits

Once you know what you’re auditing for, it’s time to pick an audit type. Your business can tailor data audits to different business areas, depending on where you think your biggest data challenges are. Some common data audit types include:

  • Customer data: These audits focus on customer records. Are they accurate and complete? Look at customer records to check information on purchase history, customer preferences, and contact details.
  • Financial data: Financial audits review your financial records’ accuracy. All invoices, transactions, and reports must be correct to ensure compliance and business standings.
  • Compliance: Compliance audits review your business’s regulatory compliance. You must follow relevant industry regulations around data or patient information — otherwise, you could face fines.
  • Operational data: These audits assess your operational data’s reliability and accuracy. Information used in daily business practices, like inventory tracking or supply chain management, should be correct to support productivity and success.

3. Key Metrics

Data quality audits only work effectively with the right information. You need to know what to measure and how to measure it. Data metrics or DQDs are specific figures that reflect your data’s quality. Common metrics include:

  • Accuracy: The correctness of your data
  • Consistency: How uniform your data is across your systems
  • Completeness: The proportion of data records with no missing information
  • Uniqueness: The number of non-duplicate records within systems
  • Timeliness: How current your data is

How to Conduct a Data Quality Audit

Now that you know what makes up a data audit, you’re ready to conduct one. Use a structured approach to make sure you get the best results from your audit.

1. Data Collection

The first step in any audit is getting all the data needed for analysis. Gather data from spreadsheets, databases, enterprise resource management, and relevant external sources. A comprehensive approach helps you avoid overlooking any datasets that could affect your results. Break down customer data, financial records, and other information based on the audit type. Set a good data audit example by making your investigations focused, regular, and detailed.

2. Identifying Data Quality Issues

Once you’ve collected and organized your data, it’s time for analysis. With all your anomalies flagged, you can start sorting them into categories. The better your analysis, the easier it is to see where the gaps are. Here are some common issues that pop up:

  • Inaccurate data
  • Duplicate records
  • Inconsistent formatting
  • Incomplete data

Once you know your major issues, your audit is complete. Take your data collection and analysis results and start planning your next steps.

3. Automated Data Auditing

Analyzing all this data manually is time-consuming and inefficient. Instead, use automated tools to scan your data for anomalies and flag issues. An automated, AI-powered system can analyze datasets faster than an employee without missing anything. These solutions use machine learning to detect patterns and highlight unexpected changes, giving you a better look at your data quality.

One of the best benefits of AI tools is their continuous nature. They’re regularly scanning data for issues, ensuring your team gets flagged as soon as the system spots a problem. Once AI-powered solutions flag an issue, they go one step further. These tools use root cause analysis to trace the issue back to its origin. They flag the problem and tell you what caused it, making it easier for your team to fix the error before it affects the business.

Post-Data Quality Audit Actions

Whether you use manual or automated methods, the real work happens after the audit. Knowing your growth areas is just the start — you need a plan to correct and improve them. Addressing the issues the audit finds is how you keep your data accurate, reliable, and valuable. Follow these steps to make your audit results effective:

  1. Rank issues: Categorize issues based on priority and impact. Critical errors like inconsistencies and missing values need attention first. Address the biggest issues before they can impact your business, and then work down the list to correct minor problems.
  2. Make improvements: After ranking and fixing immediate problems, focus on long-term improvements. You need ongoing data governance policies to protect your data. How are you going to prevent these issues and keep your data effective?
  3. Automate: Use automation to create better data systems. AI-driven tools can continuously monitor your data for problems, reducing the need for frequent audits. A proactive approach saves time and improves data quality faster.

Protect Data Quality With Anomalo

Data quality audits are essential, but manual processes take up valuable time and resources. Keep your business productive and your data effective with AI-driven data monitoring solutions from Anomalo. Our solution automatically detects anomalies, identifies root causes, and tracks data quality over time. Meanwhile, the no-code configuration makes it easy for everyone to use. Anomalo provides comprehensive, continuous auditing at scale for your business.

Enhance your data quality audits and leave false positives behind with Anomalo. Request a demo today to see how we can help you maintain reliable, accurate data.