According to a 2026 report by DataCamp and YouGov, 60% of organizations report a significant internal skills gap in data and AI literacy.The Data Literacy Index – commissioned by Qlik and conducted by IHS Markit and a professor from the Wharton School – found that “more data literate firms have a greater enterprise value of between 3-5%. This represents US $320-$534 million of the total market value of each business.” This data supports our contention that data literacy is essential to achieving a data-driven culture and business growth. That’s why we have written this article.

Raul Bhargava and Catherine D’ignazio from MIT define “data literacy as the ability to read, work with, analyze, and argue with data. It’s a skill that empowers all levels of workers to ask the right questions of data and machines, build knowledge, make decisions, and communicate meaning to others.” Data is, especially for Marketing, your growth machine. Almost every aspect of Marketing, from developing campaigns to launching new products and services to identifying new markets and segments to pursue, requires data.

Keeping your data literacy skills in tip-top shape is essential. There is a difference between having data and knowing how to use it. Your ability to compete depends on your company being able to utilize data. The Corporate Data Literacy study found that “even companies that have data-literate employees across every business unit are not likely to be turning data into usable information as effectively as they could.”

How do you know if you have data literacy? Consider the six capabilities below and the degree of proficiency in each for every person within the Marketing organization. How many members of your team have mastered or are proficient in each of them? How many of your people are competent? If the answer is only one or a few compared to most or all, then it may be time to address the data literacy of your team.

Most members of the Marketing team can:

• Conduct and interpret statistical analysis, such as averages, standard deviation, and correlations
• Acquire and analyze the data necessary to construct a business case
• Explain the impact of Marketing with specific quantifiable measures and metrics
• Employ algorithms needed to construct models (journey maps, segmentation, adoption rate, etc.)
• Understand whether the data is valid, why the data is important and in what context, and where it comes from
• Recognize relevant patterns in the data and what action to take

How to Tune Up Your Data Literacy

Based on your responses above, if you need to build some additional data literacy proficiency for all the people on your team. Here are five ways any organization can fine-tune its data literacy.

1. Brush up on statistics. Statistics are the base language for understanding data and gaining value from analytics. If it’s been a while or you have folks who shied away from math and statistics, take a course as a group. Bring someone in or sign up for an online program.
2. Clarify what you want to learn or what decision needs to be made. While data for the sake of data may be interesting, the ultimate goal is being able to use the data to the benefit of the business. Therefore, make it a habit to write out a statement about what you want to learn or what decision you want to make before you go diving into data and end up being sucked into what we call the data vortex.
3. Aim for comprehension and manipulation. When we all first learned to read and write, a key test of progress was comprehension. That is, did we understand the words we were reading? Over time, we learned how to use the words to create and communicate. The same applies to data literacy. Learn how to read data and demonstrate that you understand what the data you are reading means. Analytics is what enables us to manipulate the data. Start with simple analytics to derive useful insights. If necessary, reach out to data scientists inside or outside your organization for help.
4. Immerse yourself and everyone in data. Like any language, the more you immerse yourself in it, the more fluent you become. Immersion takes access. Make sure everyone has access to data. Part of enhancing your data literacy is to actually use data. Take the time to work with data and use it to inform decisions. It may seem like it is faster to trust your experience or instincts. And you may come to the same conclusion using the data as you would have trusting your gut, but your gut in not always right. Practice is a key aspect of becoming more literate. The more you practice, the better you’ll become.
5. Set up performance targets for literacy. How will you measure your team’s data literacy? You can apply a rating scale to the capabilities above as a starting point, with the goal that some percentage of your team will achieve some performance target for all six capabilities. Setting, monitoring, and reporting on performance targets signals to the team that these capabilities are important.

To stay competitive in a data-rich world, every part of the organization, including Marketing, must be prepared to leverage data to inform its business practices and decisions. Without data literacy, it will be extremely difficult for your organization to determine the right data, acquire and analyze it, and use it to derive insights that inform critical decisions. People on your team who are data literate have the skills to create models, apply a critical eye to insights derived from data, and communicate these insights in a way that is relevant to key decisions. In our data-intensive world, everyone, especially everyone in Marketing, needs to be data literate. Whether through self-learning or expert consulting, achieving business success depends on solid data literacy. Until you have these capabilities nailed or when you have more complex questions, consider hiring and collaborating with external experts to serve as trusted advisors.

FAQ:

• Conduct and interpret basic statistical analysis (averages, standard deviation, correlations)
• Acquire and analyze data to construct a business case
• Explain Marketing’s impact using quantifiable measures and metrics
• Use algorithms to build models (journey maps, segmentation, adoption rate, etc.)
• Evaluate data validity, context, and provenance (where it comes from and why it matters)
• Recognize patterns and translate them into action

1. Brush up on statistics as a shared foundation language.
2. Clarify the decision or learning objective first to avoid the “data vortex.”
3. Aim for comprehension and manipulation—learn to read data, then apply simple analytics to extract insight.
4. Immerse the team in data by ensuring access and building practice into daily work.
5. Set performance targets for literacy using a rating scale across the six capabilities, then monitor and report progress.