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How to improve data literacy

Every company thinks they're data driven these days slapping it across job descriptions, company websites, and shouting it at all hands like it's a secret sauce to success. But, when you ask a company how they know they’re data driven the answer is usually a blank stare followed by "Um, we have a data team" or "We look at dashboards every week."

The reason companies can never get to the level of data obsession that I talk about in this article

is because they’re fundamentally approaching data driven, obsession, literacy, etc. in the wrong way. It's about creating a culture where everyone understands, speaks, and breathes the same language.

What is data literacy?

Data literacy is the ability to read, understand, create, and communicate with data. It's like any other language and you need to align on the vocabulary, grammar, and syntax to effectively communicate.

It means:

1. Understanding what metrics matter

2. Knowing how to access those metrics

3. Being able to analyze what's happening

4. Communicating insights effectively

5. Taking action based on those insights

Here’s what it’s not:

• Everyone is a data scientist

• Everyone knows every metric

Why data literacy is important

According to research, companies with higher data literacy outperform their peers in:

• productivity and profitability

• spend less and drive more revenue

• identify opportunities faster and capitalize on them better.

Internally, data literacy creates alignment. When everyone speaks the same language, decisions become easier. No more "I think" vs "I feel" debates that waste time and lead to political decisions instead of fact-based ones.

Example of data literacy:

Team A: "I think we should put more budget into TikTok because it feels like it's working better."

Team B: "Our data shows TikTok has a 30% higher conversion rate but 2x the CAC of our Meta campaigns. Let's test increasing budget by 20% for 2 weeks to see if efficiency holds."

Which team would you trust with your marketing budget?

Perhaps the biggest benefit is autonomy. When your team is data literate, they don't need to wait for analysts to tell them what's happening. They can see it themselves, saving time and resources.

The goal of data literacy

The biggest misconception about data literacy is that everyone needs to become a statistician. Your copywriter doesn't need to understand Bayesian analysis, and your brand manager doesn't need to know SQL joins. What they need is curiosity.

Curiosity drives people to ask the right questions:

• "Why did last week's email campaign perform 20% better than the previous one?"

• "Is there a correlation between time spent on site and conversion rate?"

• "How does our Monday performance compare to our Friday performance?"

These questions don't require statistical mastery to ask. They require curiosity. This distinction is crucial because it makes data literacy feel achievable.

How to build it from scratch

If you're starting from zero, data literacy can feel overwhelming. A common question is “Okay, but how do I get buyin from everyone?”

1. Start with a data audit

   • What data do we need?

   • What data do we have?

   • Who has access?

2. Create a common language

   • Define key metrics clearly

   • Document these definitions somewhere accessible

   • Use these terms consistently

3. Build the infrastructure

   • Create dashboards that are accessible to everyone

   • Ensure data quality and reliability

4. Train your team

   • Start with the basics

   • Focus on practical applications

   • Customize based on roles

5. Lead by example

   • Make data part of every meeting

   • Ask data driven questions

   • Challenge assumptions with data

Here’s a few examples for step 4. Train your team

1. For a content marketer, that might be understanding how to read Google Analytics to see which content drives engagement.

2. A lunch and learn for paid acquisition manager to understand attribution models and incrementality..

3. As part of onboarding, everyone learns how to navigate dashboards and has a tutorial on key metrics

How to continuously nurture it

Building data literacy is not a one-time thing. It's an ongoing process that requires continuous nurturing. Here's how to keep the momentum going:

1. Create regular data discussions

   • Weekly team reviews of key metrics

   • Monthly deep dives into specific areas

   • Quarterly business reviews with data at the center

2. Recognize and reward data driven decision making

   • Highlight wins that came from data insights

   • Create a culture where "I don't know" is acceptable, but "I didn't look" is not (this one is my favorite)

3. Invest in ongoing education

   • Provide access to courses and resources

   • Bring in external experts

   • Create internal knowledge sharing sessions

4. Make data fun

   • Create friendly competitions

   • Gamify data exploration

   • Celebrate data-driven wins

Another one of my favorite approaches is the "data buddy" system, where you pair a data savvy team member with someone who's less comfortable with data. They meet regularly, with the less data savvy person bringing questions and the data buddy helping them find answers.

This creates a safe space for learning and builds relationships across skill sets. It also pressure tests how accessible your data is.

Traps and pitfalls

1. Analysis paralysis

   • When teams get so caught up in data that they stop taking action

   • Solution: Set time limits for analysis and force decisions

2. Data silos

   • When knowledge and access is concentrated in a few people

   • Solution: Democratize access and create shared resources

3. Perfectionism

   • Waiting for "perfect" data before making decisions

   • Solution: Embrace "good enough" data with clear confidence levels

4. Tool obsession

   • Focusing more on fancy tools than on actual insights

   • Solution: Start with simple tools that everyone can use

5. The "data team will handle it" trap

   • Outsourcing all data analysis to specialists

   • Solution: Make data everyone's responsibility

The biggest trap I see is what I call "dashboard theater" where teams look at dashboards, nod their heads, but don't actually understand or act on what they're seeing. They're going through the motions without deriving value.

Combat this by regularly asking team members to explain what they're seeing and what actions they would take based on the data. If they can't answer, you've identified a literacy gap.

Wrapping up

Remember these key points:

1. Data literacy is about building a common language and understanding

2. The goal is curiosity, not statistical mastery

3. Start with the basics and build progressively

4. Create systems that continuously nurture data literacy

5. Avoid common traps like analysis paralysis and dashboard theater

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Missed my last article?

Here it is: How "good enough" attribution is not enough