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5 Essential Best Practices to Ensure Quality Data for Your Non-Profit

If you’re responsible for managing data, you know it’s important to ensure that the data is high quality. Because, in data management, poor-quality data leads to incorrect decision-making, wasted resources, and even legal problems.

To help ensure you’re using quality data, this post provides five essential best practices to follow, including:

  • Understanding the basics of quality data.
  • Finding and selecting appropriate data sources.
  • Automating the verification of data accuracy.
  • Monitoring data quality over time.
  • Taking action to improve data quality.

By following these best practices, you can help ensure that your organization’s data is of the highest possible quality to make it valuable to use to analyze and visualize your impact.

Understand the Basics of Bad Data 

First, it’s important to understand what makes quality data, well, quality.

Quality data is defined as accurate and consistent data that’s are used for a purpose. To ensure quality data, non-profits must implement processes and controls to manage the data throughout its lifecycle. 

What Factors Impact Quality Data for Non-Profits?

Several factors can impact the quality of data, including:

  • Inaccurate or incomplete data: Inaccurate or incomplete data can lead to incorrect decision-making. It can also cost a ton in legal fees!
  • Inconsistent data: Inconsistent data can be difficult to interpret and lead to confusion.
  • Outdated data: Outdated data can be misleading and result in decision-makers using old (and irrelevant) information.
  • Poorly structured data: Poorly structured data can be hard to work with and make extracting meaning from the data even more difficult.

Find and Select Appropriate Data Sources for Your Non-Profit

Many different types of data sources are used to support decision-making. 

So, it’s essential to consider the strengths and weaknesses of each type of data source when selecting which sources to use for a particular purpose. 

Some typical data sources for non-profits include surveys, administrative data, transactions data (like donations), social media data, and web analytics data.

Evaluate Data Source Quality

Once you have identified potential data sources, evaluating the quality of those sources before using them is crucial. Several factors can impact data quality, including accuracy, timeliness, completeness, and consistency. It’s important to consider all of these factors when evaluating the sources in which you obtain your data. 

Automate the Verification of Data Accuracy

It’s vital to have a framework in place to automate the verification of data accuracy. 

This framework should include guidelines and procedures for ensuring data quality and tools and technologies for supporting these activities. 

Consistency is key when it comes to frameworks, especially if you have a team of professionals working with the data. 

Utilize Data Validation Tools

Many different data validation tools are available, both open-source and commercial. These tools can be used to verify the accuracy of data and identify errors and inconsistencies.

Monitor Data Quality Over Time

The first step in monitoring data quality over time is establishing quality metrics. These metrics are one of your organization’s most important factors because they are typically in direct correlation with your non-profit’s goals. 

There are various ways to measure data quality, but some common metrics include accuracy, completeness, timeliness, and consistency.

It’s important to choose metrics that are achievable and that you can actually improve upon. 

For example, suppose one of your non-profit’s goals is to increase customer satisfaction. In that case, you might want to track the percentage of customer service inquiries resolved within 24 hours.

Track Performance

Once you’ve established your quality metrics, it’s time to start tracking performance against them. Doing so helps you identify trends and areas where improvement is needed. 

There are a number of different tools and techniques useful for tracking data quality, such as data audits, surveys, and analytics tools.

Data audits involve manually reviewing a sample of data to check for compliance with quality standards. 

Surveys are used to collect feedback from customers or employees about their experiences with data quality. 

And analytics tools are used to monitor changes in data over time and identify patterns or anomalies.

Monitoring data quality is an ongoing process, and it’s essential to regularly review your metrics and track performance against them. 

By doing so, you can identify issues early and take action to improve data quality before it becomes a problem. In other words, the sooner you implement a process, the better. 

Take Action and Improve Data Quality

An organization’s data governance program is essential to ensuring its data quality. 

Data governance includes the processes, policies, and people responsible for managing data. 

By investing in data governance, organizations can ensure that their data is accurate, consistent, and compliant with regulations.

Implement Data Quality Promotion

Data quality promotion is the process of improving the quality of data within an organization. Which can be accomplished through various methods, such as training employees on best practices for data entry and validation, implementing quality control measures, and establishing standards for data accuracy.

The quality of your data is essential to the success of your non-profit. 

By following these five best practices, you can ensure that your data is accurate and reliable. And you can make better decisions, improve operational efficiency, and confidently drive your mission’s growth.

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Meet the Blogger

Hi! I’m Sarah, the human behind Impact Aligned.

After 13 years of strategy and technology consulting for the private sector, I decided it was time to use my expertise to empower non-profits to grow their impact using the same methods as for-profit companies. I’ve helped organizations utilize their own data in order to help save them boatloads of money and build profitable growth.

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