Article written by Ben Herzberg, Chief Scientist of Satori

We live in a data-driven world. From the first moment we learn to speak, we are inundated with data: numbers, measurements, and labels. When we are in need of assistance, numbers guide us to the proper resources. When we want to purchase something new, numbers tell us what we need. In every step of our journey, data is there to help us make decisions.

Today’s corporations rely on data to make critical business decisions—decisions that affect their bottom line and can influence whether they will be successful tomorrow. Data is the lifeblood of a company, and to make informed decisions, business leaders need to be data fluent.

So, what is data fluency? And how can you empower your business to become data fluent? This article will explore the definition of data fluency and its key components, along with some tips on how to improve your company’s data fluency.

What is Data Fluency?

Data fluency is the ability to understand, interpret, and act on data. It’s being able to ask the right questions of data and use that information to make sound decisions. Data fluency is also about communication—the ability to take data and turn it into a story that everyone can understand.

Data fluency in a business setting links users from various teams via a common set of standards, procedures, technology, and lingo. Data fluent users may turn raw data into usable insights since they understand how to read it, know the data that is and isn’t accessible, as well as how to make effective use of it.

The idea that only a few people are gatekeepers of data is rejected. Instead, information is spread, data access across an organization is increased, and decision-making for everyone improves as a result of data fluency.

Key Components of Data Fluency

There are three key components of data fluency:

  • Data literacy: the ability to read and understand data
  • Data interpretation: the ability to make sense of data and see patterns in it
  • Data actionability: the ability to use data to make decisions

All three of these components are essential for making informed decisions. So, let’s take a closer look at each one.

Data Literacy

Data literacy is the ability to read and understand data. To be literate in data, you need to be able to extract information from data sets and understand what it means. This includes being able to identify the different types of data (numeric, text, categorical, etc.), and knowing how to handle them accordingly.

Data Interpretation

Data interpretation is the ability to make sense of data and see patterns in it. What do your numbers really mean? What insights can you find within a dataset, and what decisions will they help inform? This component also includes being able to tell a compelling story with data (e.g. data storytelling).

Data Actionability

Data actionability is the ability to use data to make decisions. What questions can you answer with your data? What hypotheses can you test? What actions can you take based on what you’ve learned? This component is essential for turning data into knowledge and using that knowledge to improve business outcomes.

All three components of data fluency are essential for making knowledgeable judgments, and all three are critical to the successful operation of an organization.

Why is Data Fluency Important for Data Democratization?

When it comes to data democratization, data fluency is an essential component. Data democratization refers to the process of breaking down data silos and moving valuable business analytics from a departmental level to an enterprise-wide level.

The key requirements of data democratization are that users will have access to data (making data accessible), and to help make users more data fluent, so they know how to use this data.

Data democratization provides people with access to relevant data so they can make better data-driven decisions. And in order for this system to work, everyone in the organization needs to be data fluent. That’s why data fluency is so essential for data democratization.

Improving Your Data Fluency

So, with all of the above in mind, how can you improve your data fluency? Well, there are several ways. Let’s take a look at just some of them next.


It’s important to learn the basics of data literacy and interpretation so that you can understand and make use of the data that’s available to you. Moreover, you can use data to drive insights and tell a story with it. Data literacy is essential in the digital economy we live in today, and being educated in knowing how to read and understand data is a critical skill.

There are many different ways to get educated in data, and it’s crucial to find one that works best for you. There are online courses, boot camps, and certificate programs available, as well as traditional educational institutions that offer degrees in data science and analytics. The important thing is to start somewhere and make sure you’re constantly learning and keeping up with the latest data trends.

Data Sharing

Data sharing also plays a crucial role when it comes to your data fluency. When you share data with others, you open yourself up to new insights and perspectives. The value of data is amplified when it’s shared and used collaboratively.

There are many different ways to share data, but the most effective approach depends on the type of data you’re sharing and the people with whom you’re sharing it. The key is to find the right balance between security and accessibility when sharing data and to make sure you’re all on the same page when it comes to how and why data is being shared.

Tools to Process Data

Data communication and processing tools are other helpful resources for improving your data fluency. But, as we’ve already discussed, it’s not enough to just have access to data—you need to be able to understand and interpret what you see as well.

Data visualization and BI tools can help with that by allowing users the ability to explore insights in a more interactive way that helps them better understand what they’re viewing. So if you’re looking to tell a story with the value of data, data visualization is a powerful way to do that.

When your users learn how to create informative visualizations easily, they can simplify the communication of the data-driven insights to those who need it most.

Simplified Access Control

Lastly, simplified access control can help improve your data fluency. If your users have to jump through hoops to get access to data, this will be a barrier to data fluency and data democratization. This includes the application of dynamic fine-grained access control to enable access to data that may contain sensitive information. An example of this is applying dynamic data masking on data, to enable access to the data without expositive sensitive information to those unauthorized to view it.

The Conclusion

When it comes to data fluency, there are several different ways to improve your users’ skills. Education is essential, as is data sharing and the use of tools to help you process data. Simplified data access control is critical to driving data in a secure way fast enough for data consumers. 

When users in your organization are data fluent, they can make better decisions and use data to drive more business value. Improving your data fluency is essential for the success of your business—and for the success of data democracy in the digital age.

Ben is the Chief Scientist of Satori, the DataSecOps platform, simplifying access to sensitive data. 

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