4 Components of the Data Fluency Framework
Data alone isn’t valuable—it’s costly. Gathering, storing, and managing data all costs money. Data only becomes valuable when you start to get insights from it and apply those insights to actions. But how do you empower your organization to do that?
The answer is not simply a better dashboard or more carefully designed data visualizations. These are helpful, but small pieces.
The foundation of getting value from data depends on creating a data fluent culture in your organization. There are many benefits of having a data fluent culture, but what does it take to get there? Here’s the framework we first outlined in our book Data Fluency:
Data fluency is a web of connected elements. It requires (1) people who speak the language of data, (2) skilled producers of data products, (3) an organizational culture with the conditions to support data discussions, (4) and the systems, tools, and ecosystem to create and share data products. The Data Fluency framework explains the roles of individuals, the organization, and the systems necessary to achieve it.
(1) Data Consumers
The most fundamental element of your Data Fluency Framework is the individual or data consumer. Enabling these individuals to understand and draw deeper meaning from data is the fundamental condition for a data fluent organization. It takes more than a solitary listener to give meaning to data. When individuals are informed, they can participate in comprehensive dialogue around that data. This is the domain of the many data literacy training programs that have emerged (check out our partners). Becoming data literate boils down to being able to ask and answer three questions about data:
Where does the data come from? Not simply what database or system—rather, what real-life behavior does the data reflect? What is the scope and granularity of the data? And what do the data fields actually mean?
What can I learn from the data? People need to learn how to interpret charts, recognize the unexpected, and contextualize the data through comparison.
What can I do with it? The ability to take action on data requires both an understanding of the validity and reliability of the insights, and seeing how the insights connecting to the decisions available to you.
(2) Data Producers
Your organization’s data producers must work with your raw data and deliver the content in ways that are easy to understand and act on. Each data consumer comes to the information with different priorities, needs, and perspectives. As a producer of data products, your successful translation of data builds on this variety as an asset - everyone in the discussion adds to the overall understanding of the group and finds their own insights.
To bridge the gap between data and an audience, the data author has a complex job. The author must decide what data is most important to focus on to answer the questions at hand, and how to optimally harness and depict data to inform thinking and action. Effective communication with data is a rare skillset. Here are a few hats that data product authors must wear:
Salesman—Data product authors must know their audience. To do so, they must consider how to best capture their audience’s attention, how they might perceive the data, and what it may take to gain the audience’s buy-in. An effective data product needs to be enticing, clear, and convincing to lead an audience to action.
Therapist—Data product authors need empathy—the desire and ability to understand and share the feelings of others. By getting into the hearts and minds of the audience, they can find the questions that are most influential. What will motivate an audience to action? What is the audience afraid of? How can the data address these concerns?
Connoisseur—The best data authors are willing to make tough distinctions between data that is interesting and data that is important and actionable. It can go against our nature to put aside data that others might want to see. But the desire to deliver everything needs to be offset by an appreciation for an audience’s limited attention span.
Data analyst—Data authors can’t create great art if they don’t like working with their materials. Data authors need to be comfortable with core statistical concepts and comfortable with manipulating data. Getting involved with deep data analysis can reveal the important messages and accurate ways to convey them.
Ethnographer—Data product authors should have a perspective on how the data product will fit into the way people work within their organization. How will the data product get incorporated into your audience’s workflow? How does information travel throughout the organization? What do people care about and what do they ignore?
(3) Data Product Ecosystem
To enable the flow of information and the creation and sharing of data products, you need standards, tools, and processes in place. A good example is what Apple did with the App Store in creating a platform and standards by which apps are created, tested, distributed, and reviewed. Your data product ecosystem is no different, you must come up with those same standards, tools, and processes to facilitate the data environment. We’ve worked with large enterprises to establish these pieces:
Standards are the design patterns and style guidelines that make it easier for data producers to effectively communicate with the data.
Tools enable you to design and build data products and ensure they are discoverable for your target audience.
Processes encourage the sharing of insights and collaboration between producers and audiences, as well as ensure data hygiene and quality throughout.
(4) Data Fluent Culture
As your company develops more data consumers and producers, the data fluent culture will develop and flourish. Your company will develop your own unique dialect of data fluency through defining key terms, data collection, and interpretation. This leads your company to actions based on results and goals —and that is a culture everyone wants to have!
The ability of your organization to use data hinges on developing a team of people who share a common vocabulary and skillset to understand data. This culture often starts from the top. Leaders need to lead in three important ways:
Set and communicate expectations. Data fluent leaders must lead by example. They should express expectations for quality data products and then use data products to support the organization’s mission. To build a data fluent culture, leaders must communicate using data to support their decisions and organizational priorities. By doing so, they set the standard for quality data products and demonstrate their data literacy in public forums, modeling the expected behaviors of their team.
Celebrate effective data use and data products. Everyone watches leaders in an organization. What is appreciated, recognized, and celebrated by leaders signals the key values of the organization. In addition, these celebrations of work products provide an opportunity to demonstrate that high standards and performance are achievable.
Use data to inform decisions and actions. Leaders and employees in a data fluent culture bring well-understood key metrics into meetings, understand how to measure the performance of a new project or product, and include data fluency skills in the hiring and employee evaluation processes.
Building a Data Fluent Organization
This framework (and our book) reveal some of the important areas you can work on:
Evaluate strengths and weaknesses;
Define an organizational plan;
Define training and skills needed;
Data product inventory;
Set a technology roadmap.
Pick up our book, Data Fluency, to learn more about how to create a data-fluent culture within your organization our check out our product Juicebox to experience the easiest way for everyday information works to communicate with data.