Data literacy refers to the ability of non-data specialists to read, write, and communicate using data. This includes a basic understanding of data sources, organization, and constructs. It also includes being able to understand the context in which data is collected and being able to communicate this context. Data literacy is becoming more and more important especially in a B2B context. Teams outside of the data organization itself must be able to make data-backed decisions and communicate to others why they're doing so.

An improvement in data literacy across your organization benefits everyone, especially those apart of the data team itself. This is because an increase in data literacy results in less questions and hand-holding from the data team, easier data discovery, and better data hygiene.

Educate everyone on the business value of data.

Every employee is a potential data consumer. Here's how to start the conversation about why data literacy matters, and how you can use it to make more effective business decisions.

Data is an asset that every company has at their disposal, but too often it goes unused because employees don't know where to find it or what questions to ask of it. A lack of data literacy means missed opportunities for better decision making and efficiency across all departments—from helping sales teams identify new prospects, to giving marketing a clearer picture of customer behavior, and even helping HR understand employee performance.

Inform your team about the benefits of using data so that people can see the value in becoming more literate. If you have already launched a BI solution, make sure people know why you chose that solution, what specific features will help them do their jobs better, and how they can get started with self-service analytics. It's helpful if there are success stories from other departments so they can see real examples of the impact this will have on business goals.

Emphasize the importance of data documentation.

High-quality data documentation is an essential part of your organization's culture, and it's crucial to ensure that all employees are aware of its importance. You can do this in a number of ways.

Make data documentation a mandatory part of your projects. If you're accountable for a project and don't have time to document your data, then you should either get help or delegate the project to someone else. No one can be in such high demand that they can’t fit documentation into their schedule. If this is the case, then it's imperative that you address the situation as soon as possible; otherwise you risk having a backlog of work being created and left undocumented by overworked employees.

Document every process step by step with clear explanations and examples. The best way to teach people how to do something is to show them how it's done—and explain why every step is important along the way. This will give everyone who works on your team clarity about what their job entails and where any problems might lie in order for them to be able to take action against these issues before they become overwhelming obstacles that affect productivity levels negatively.

Set expectations for data literacy that show you are valuing it as a skill.

Here are some tips for how to set expectations for data literacy that will show your company is serious about its importance.

Clearly define what data literacy means for your team and/or company. This is necessary so that everyone understands what skills they will be expected to learn and use on the job, based on the definitions that are most relevant to their positions.

Determine when people need to be trained in those skills, so they have time to hone them. Set aside periods of “ramp-up” time in which new employees must begin picking up these abilities, as well as development opportunities throughout their careers in which there can be a focus on specific types of data literacy or “soft” skills such as communication and creative thinking—skills that can help people work with data better even if they don't touch it directly themselves.

Make sure you're giving people incentives to invest in building these skills: If there aren't concrete steps people can take at work toward improving their use of data, whether it's through professional development activities or by getting direct feedback from more senior colleagues during their course of day-to-day tasks, then you've got a problem with follow-through and buy-in from employees who may fear racking up more work or making mistakes because they didn't understand the details of an assignment right away.

Of course, there's no way around the fact that learning anything new takes time, but setting clear expectations helps you get out ahead of any potential problems with employee buy-in or frustration before they arise.

Track and share your team's accomplishments with data.

Organizations often find it difficult to track and share their accomplishments.  Fortunately, there are several techniques that you can use to collect and showcase the important metrics that measure your success.

Start by determining which achievements are worth tracking. A good rule of thumb is that anything that makes the business more efficient or profitable should be included on a list of top accomplishments. For example, if your Data Science team was able to cut costs by creating an algorithm to automate manual processes, this would be worth noting.

Then decide how you want to share those milestones with your team or company at large. Some teams opt for a monthly newsletter or data bulletin board so everyone can stay up-to-date about what their peers are working on at all times. You could even throw an annual Data Science Awards event—complete with trophies for each category—to encourage friendly competition between departments and recognize individual accomplishments too!

Encourage failure, but reward the learnings.

Encouraging failure in a safe environment is the heart of learning. If you can foster an environment where failure is allowed and learning from it is rewarded, your team will feel empowered to take risks. To encourage this type of culture, create a place where success stories of failures can be showcased and successes that came from those failures celebrated. By demonstrating that you value learning over getting it right every time, your team will be more proactive about taking risks.

Offer free learning opportunities to your team members.

Give your team members multiple opportunities to learn about data science and related fields in ways that work for them. They might prefer online or in-person courses, short or long ones. Free or paid ones, with or without certificates. You can even offer mentorship programs for those who want a more immersive experience. Another option is to create a library of free books available for check out by anyone who wants them.

The key here is to give your team members the freedom to choose how they want to learn, whether it's at their own pace or during breaks from their day-to-day responsibilities. These options will ensure that you're giving everyone a fair chance at improving their understanding of data, while acknowledging that they all have different needs and preferences when it comes to learning new things on the job.

Improving company wide data literacy will help team members understand their goals and make better decisions with their data.

Data literacy is a term that has been used since the 1990s, but it still doesn't have a universal definition. Data literacy refers to the ability to read and understand data, as well as knowing how to use data for analysis and communication.

Improving data literacy in your organization can help team members understand their goals, make better decisions with their data and may even lead to more effective business operations. Let's look at how you can improve company-wide data literacy, including ways you can get started on improving your own skills immediately.