What is Data Usability? Definition, Examples, and Best Practices

Your company has a vast trove of customer data, but you're constantly grappling with the problem of data usability. Even though your team has access to all this data, they often struggle to turn it into insights that can help drive business decisions. How can you improve the usability of your data to make it more valuable to your team?

What is Data Usability?

Put simply, data usability refers to the ease with which data can be accessed, understood, and used to inform business decisions. It's one of ten dimensions of data quality, along with elements like accuracy, validity, completeness, and consistency.

It's important to note that while data usability is related to these other dimensions of data quality, it has distinct differences. For example, data accuracy refers to the correctness of data, while data usability is more focused on how easy it is for users to understand and use that data.

Examples of Unusable Data

To help us understand why data usability is so important, let's take a look at some examples of how unusable data can impact business analytics.

‍

• Inconsistent data formats: If your data is not standardized and consistently formatted, it can be difficult for your team to understand and use. For instance, if you have one table that uses international date format (DD/MM/YYYY) and another that uses US date format (MM/DD/YYYY), it can be challenging to run queries across both tables.
• Inadequate data documentation: If your team doesn't have access to clear and organized documentation on how data is collected, or what specific variables mean, it can be hard to parse and use this data in a meaningful way. This can lead to misunderstandings and misinterpretations of data.
• Poor quality data: If your data is rife with errors, your team can quickly lose confidence in its accuracy. This can lead to them discounting data altogether, and turning instead to gut instincts or guesswork when making decisions.

How Do You Measure Data Usability?

Measuring data usability involves a range of real-world metrics and best practices that allow your team to assess how easy it is to access, understand, and use your data. Here are some examples of metrics and strategies that can help measure data usability:

• Table documentation: A uniform and organized documentation of data schema that can help identify what table the data is associated with and what the data means.
• Monitoring query successes: By tracking dataset activity closely with tools like Metaplane, you can quickly identify datasets with low activity or datasets that have undergone significant changes in recent times. This can help you avoid analyzing data that is no longer relevant, or that may be unreliable.
• User Survey: Use surveys to measure user satisfaction with data analytics tools, such as self-grading whether they're able to easily use a dashboard. Gathering user feedback enables you to identify areas for improvement in your data management system, including data usability.

How to Ensure Data Usability

To ensure data usability, it's necessary to actively maintain data quality using real-world metrics. Here is an example of a best practices for ensuring your data stays usable:

• Standardizing data formats: By establishing clear templates for datasets and standardizing data formats, your team can more easily access, understand, and use your data.
• Implementing an anomaly detection system: This can help you identify potential issues with data quality before they negatively impact your data. Data observability enhances data usability initiatives by providing anomaly detection for key objects and related queries, to help you find datasets that appear unusable.
• Documentation: Simply documenting field defintions either in transformations models or business intelligence tools can mitigate user confusion when seeing results.

Summary

In summary, data usability is essential to making sure your business runs efficiently and effectively. Usable data has a direct impact on operational and decision-making purposes. Without it, your business can quickly run into problems. Consistent data formats, proper documentation and quality data can make all the difference in turning a messy dataset with no discernible insights into a tool that can guide your business.  Overlaying a data observability tool on top of your data stack can help you with understanding data usage today to inform your usability initiatives.