Modern organizations rely on data daily to inform business strategies and make decisions. The large volume of data collected can be incredibly useful, but only if it is good-quality data. Without having data quality measures in place, you may quickly see an increase in data issues. These data issues can have a significant impact on critical decisions and your data quality score. In this blog post, we’ll be talking about common data quality issues, best practices for identifying them in your own data processes and how data governance can help mitigate data issues. Read on to learn how to make your data more accurate and trustworthy.

A Brief Overview

Data quality issues are a common challenge in the modern data-driven business environment. Errors, inconsistencies and inaccuracies can easily lead to wasted resources and missed opportunities. Naturally, businesses want to make sure that data quality issues are kept to a minimum.

Fortunately, there are tools and strategies to prevent data quality issues from becoming a big problem. First, let’s start by diving into some of the most common data quality issues your organization may experience.

What Are Data Quality Issues?

Data quality issues are any errors or inaccuracies in the data your organization collects and analyzes. It’s important to keep in mind that these issues can pop up at any stage of the data pipeline, which is why it’s important to have high visibility into your data environment. 

Some of the most common data quality issues include:

• Missing data
• Duplicate entries
• Stale data
• Formatting errors
• Incomplete data
• Hidden data
• Inaccurate data

These data quality issues may not cause too many problems on a small scale, but consistent data errors will inevitably lead to negative outcomes. It’s important to address data issues as they are identified. So, what are the signs that you’re dealing with data quality issues?

Look Out for the Signs

If you know what to look for, you can catch data quality issues before they become a bigger problem. These signs can help you catch potential problems early on and prevent them from affecting your business outcomes:

• Inconsistent data — If your team is seeing inconsistent or contradictory information across data sources, then there are data quality issues somewhere in your processes. There shouldn’t be numerous discrepancies between data sets.
• Missing or incomplete data—— If your team is consistently getting data with important fields left empty or left incomplete, then you’re likely having a problem with data quality.
• Imprecise data—— You may notice that the data you’re collecting doesn’t fall within typical ranges. While outliers are certainly possible, data that consistently falls outside the expected or standard ranges may be imprecise and inaccurate.

If your team is seeing data quality issues, it should be reported as soon as possible to ensure the issues can be addressed. Of course, it’s better to be proactive rather than reactive when it comes to data quality. Let’s talk about some ways you can actively assess the quality of your data.

Data Quality Assessment Techniques

To ensure data accuracy, it’s always a good idea to have some processes in place for data quality assessment. Here are some techniques to consider employing in your organization:

• Data profiling—— Data profiling is a technique that involves analyzing data sets and finding anomalies and patterns that are causing data quality issues. Data profiling can give you a better idea of your overall data quality and help you identify critical issues that should be addressed as soon as possible. 
• Data cleansing—— Data cleansing is another technique that should be part of your data processes. When data is ingested, it should be cleansed to remove and correct any errors, inconsistencies or inaccuracies. This can be done through manual or automated processes and tools.
• Data validation—— Data validation is a technique that involves verifying the accuracy and completeness of data. This can be done by comparing your data against predefined benchmarks. This helps to ensure that your data meets your data quality criteria and that it is trustworthy to use in decision-making processes.
• Data auditing—— Data auditing should be done regularly to ensure data is accurate and that your processes are updated and sufficient for maintaining data quality.

Tools for Identifying Data Quality Issues

When it comes to identifying data quality issues, having the right tools in your stack can make a world of difference. Fortunately, there are a variety of tools available, including Secoda. Secoda is a comprehensive data management tool that can help ensure data quality through data monitoring, data lineage and more.

Platforms like Secoda can often act as an all-in-one solution for identifying data quality issues. If you are building your stacka la carte, make sure to incorporate data profiling tools, data cleansing tools and data validation tools to ensure your data is as accurate and error-free as possible. By leveraging the right tools, you can both identify and resolve your data quality issues and ensure your data remains consistently accurate as your business grows.

The Importance of Data Governance

Data governance is one aspect of data quality management you should never neglect. Data governance not only plays a significant role in ensuring the accuracy and security of data, but it also ensures you’re compliant with industry regulations. Without effective data governance policies in place, you open your organization up to negative consequences like data breaches, cyberattacks, regulatory penalties and loss of customer trust. On top of that, poor data governance can lead to inaccurate and inconsistent data.

Data governance helps to establish a framework for data management through clear guidelines, processes and responsibilities for managing and protecting data assets. You should make sure to outline your data quality standards and identify your data stewards in your organization. You should also make sure you’re adhering to all relevant data privacy regulations and industry compliance standards. With proper data governance, not only will everyone in the organization understand their role in relation to your data, but you will also have processes in place to ensure your data is secure and accurate.<p>

Best Practices To Follow

If you want to maintain data quality and minimize data quality issues, it’s essential to incorporate certain best practices throughout your organization. Here are some of the best practices you will want to implement:

• Establish data quality standards—— Make sure to establish clear data quality standards and guidelines. These standards and guidelines should be made available to every stakeholder in the company. They should also outline data-related policies such as data ownership, roles, responsibilities and data quality criteria. You should also have clear processes for data collection, cleansing and validation.
• Conduct regular audits—— As mentioned earlier, you will need to make sure to conduct regular data audits to maintain data quality over time. Define your auditing process and make sure to monitor changes and key performance indicators on an ongoing basis to ensure your data processes are meeting or exceeding standards.
• Train and onboard team members—— Make sure your team is on board with your data quality processes. Provide training and education to enhance their data management skills and improve overall data literacy. 
• Leverage data quality tools—— Remember to leverage data quality tools to streamline processes and make catching data quality issues easier and more efficient.

Try Secoda for Free

If you’re looking for a comprehensive data management solution, Secoda is here to help. Secoda is the first AI-powered data catalog platform that enables data search, catalog, lineage and monitoring throughout your organization. With Secoda, you can easily centralize your data and use monitoring tools to alert you to any issues or errors. Ready to learn more about Secoda and how it can help your organization?