Imagine flying a plane with your eyes half-closed. You might make it to your destination, but your success is mostly dependent on luck. That’s what decision-making without data looks like—a stab in the dark that might succeed but probably won’t.

A data-driven decision-making process reduces risk and improves outcomes, and yet only 21% of employees are confident with their data literacy skills. While data improves decision-making, wrongly interpreted data can hurt your team more than help it. As your company’s data manager, you need to lead and help your team understand company data and how they can use it.

What is data literacy?

Data literacy is the ability to go beyond merely reading data. Data literate individuals can read, query, and explain data in context. They also know how to experiment with their projects, which metrics they need to track, interpret the data from their experiments, and identify what success looks like.

Professionals who know how to use data often take data literacy for granted. However, seeing data in a dashboard or database can appear overwhelming to a complete novice. In fact, Gartner reported that data illiteracy across teams is the second-biggest challenge of data teams and chief data officers. 

Why is data literacy across teams so important?

Every team in your company needs data. From marketers to product managers, everyone needs to know which key metrics to look at, what those metrics mean, and how to explain their findings to peers and superiors. With data, making decisions around which features to build and which customers are at risk all become easier.

Data literacy is important for two reasons:

1. Poor data literacy leads to wasted time and money. When your team is data illiterate, they ask a lot of the same questions. Automate data discovery so that your team doesn’t ask the same questions twice.
   

2. Data literate employees can better explain the value they add. Externally, properly presented data relays the value your company adds to your customers’ and clients’ businesses or lives. Internally, it’s easier to ask your superiors for a higher budget if you can say, “In the span of X months, we’ve increased this key metric by X%. We believe that we can increase it even more with a higher budget.”

If you're struggling to share data across the different teams at your company, read on for some tips.

7 tips to improve your team’s data information literacy

As the head of data, data engineer, or data product manager at your company, it’s your job to spearhead the data literacy movement. Your expertise lies in the understanding of data, so you need to share this knowledge with others.

1. Take the lead to foster a collaborative data-first culture

A collaborative data-first culture allows all members of your company to participate in collecting and presenting data. Your company will keep more robust records and make better decisions if you make data a team sport.

To create a collaborative, data-first culture, you must let people know that you are willing to assist. Your team should know who to contact for specific data points by having a champion for each department. Additionally, you should assist your teammates whenever possible by answering their questions, organizing workshops, and encouraging everyone to record their insights.

Read more about fostering a collaborative data-first culture.

2. Invest in a stack that makes information easily accessible

With a modern data stack, you can democratize data so that everyone has access to important information. Modern data tools can also help your team access, read, and interpret data, no matter their level of data information literacy.

A modern data stack includes a cloud data warehouse, an ETL tool, a transformation tool, a BI/analytics tool, a reverse ETL tool, and a data discovery tool.

Read more about modern data stacks and our tool recommendations.

3. Create one data dictionary

A data dictionary contains key metrics and how you define them across your entire company. Different teams could have different meanings for a single metric. For example, a “conversion” for the marketing team could mean someone booking a call, whereas a “conversion” for your sales team means someone who booked a call and made the decision to purchase.

A data dictionary removes those discrepancies in definitions and keeps your data both clear and accurate. You can create a data dictionary easily using specialized tools or manually with a spreadsheet (take note, though, this can get messy).

Read more about how to create a data dictionary.

4. Keep all your data in one place that’s easy to search through

Information overload kills productivity, and having large amounts of data in different places is both overwhelming and stressful—especially to someone who's not so good with data to begin with. Having easy-to-access and easy-to-read data records is a necessity for anyone on the data team or working with data.

Read more about keeping all your data in one place.

5. Communicate and answer questions

The key to fostering a whole new culture is communication. You can help your team achieve data literacy if you act as their guide and help them understand the data.

Make sure you manage communication between the data team and other teams, like the data and product teams, for example. We suggested earlier that each team should have a champion who can address questions and offer solutions. Invest in the right tools and processes to make communication easier and the data quicker to access and understand.

Data teams should also take on data requests. When your teammates ask for help pulling up a certain statistic or gathering quantitative data about a certain project, we recommend you help and even walk your teammates through the data collection process.

Read more about communication with data teams and taking on data requests.

6. Help your team develop data literacy skills

There are four important data literacy skills that your team needs to learn:

• Data analysis: The act of reading data and interpreting to draw insights and conclusions.
• Data wrangling: The process of “cleaning” data to reduce duplicates, inconsistencies, and errors.
• Data visualization: The process of presenting data using a visual aid like a graph or chart.
• Data governance: The process of managing data so that it remains “accurate, secure, and complete.”

You can create webinars and walk-throughs to help your team develop the relevant skills. You can also hold workshops every now and then for everyone interested.

7. Check if your efforts are effective through your net promoter score

It's important to evaluate whether you are doing enough to develop a culture of data literacy at the end of the day. One way you can check whether your team is doing well is through a data net promoter score (dNPS)—a survey that asks your team to rank their own data literacy from one to 10.

Regularly send your team a dNPS survey asking the question, “How easily, independently, and confidently can you answer your questions using company data?” The average result of your survey should give you an idea of whether your data processes and tools help your team or if you need to do more:

• 9 to 10: Your users are promoters—they are confident in their data literacy skills.
• 7 to 8: Your users are passives—they can use the data you provide, but aren’t as confident as promoters.
• 0 to 6: Your users are detractors—they don’t quite understand the data, so they won’t use it to make decisions.
  

As you grow, learn, and improve, so should your NPS. Read more on the ROI of data discovery.

You can also conduct quick data literacy assessments before and after starting your data literacy initiative to see if your teams have improved.

Take the first step to becoming data literate with the right tools

The biggest hurdle to overcome when it comes to data literacy is that many people are intimidated by data. You can help your team get past that hurdle by making it as easy as possible to access the data they need when they need it.

Check out Secoda for an easy to use data discovery tool that sorts your dashboards, metadata, analyses, and more all in one place.