Data literacy has certainly been a bit of a buzz term over the last few years. A lot of organizations - especially large enterprises - are now putting in place data literacy programs. They’ve realized that in order to remain competitive, it’s now essential that each person in their organization has the basic skills needed to interpret, read and use data.
It doesn’t matter how strong their advanced data science teams are; they need the other 99% of their organization to have basic data fluency to make better, data-informed decisions.
Ultimately, being data literate allows people to make better decisions, by using relevant data, instead of just relying on gut feel and intuition.
Data literacy programs are often initiated as part of a data strategy, and for more traditional organizations, is part of a wider digital transformation project, where they’re seeking to throw off the shackles of their analog pasts and embrace the digital age.
This is a definitive guide to running a data literacy program in your organization. In this guide you will learn about:
You can understand your organization’s current data literacy capabilities with Alooba Growth. Looking for a blow-by-blow of Alooba Growth’s functionality? Check out a full rundown of the features and capabilities of Alooba’s various products here.
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Defining what data literacy is, is a good place to start the conversation. To be honest, there isn’t a universally accepted definition of ‘data literacy’ (what some also call ‘data fluency’). In general, data literacy is considered as the ability to use, read, interpret and analyze data correctly.
Some examples of what would be considered data literacy capabilities should shed some light on what data literacy is. Here’s some examples of things that you would be expected to understand as being data literate:
Armed with these examples, hopefully this gives you a good sense of what’s included in data literacy. These examples should make it clear how relevant basic data literacy is for almost anyone in any role.
No. MS Excel is a tool - an exceptionally popular, versatile and useful tool. In fact, it’s the most widely used analytics product in the world - even more than Python, R, or any visualization tool.
People might associate MS Excel with data literacy because MS Excel is typically an entry point for anyone who’s doing their first bit of analysis. Because of how universally available MS Excel is, and how easily you can get started doing some analysis, it’s no wonder it’s associated in some ways with data literacy.
No, data literacy is not AI (Artificial Intelligence). AI is an advanced topic within data science, and is not considered within the scope of data literacy. An analogy to regular language is that, everyone needs to be able to read & write, but not everyone needs to be Shakespeare.
Data literacy is not just another buzzword. It matters for the simple reason that improved data literacy can improve decision making. Each person in a business makes countless decisions per day, and the aggregated & compounded impact of improved decision making is hard to measure, but it’s easy to see its importance.
We have so much data now, it’s truly mind-boggling. For example, from 2012 to 2022, the estimated amount of data in the world increased by nearly 1400%!.
There is an expectation - and real need - that anyone, in any position, has the basic skills needed to interpret, integrate and analyze data. With less than 5% of an organization normally reserved for data professional roles (e.g. data scientists and data analysts), that leaves the 95% of the other positions in critical need of basic skills themselves to be self reliant.
Daniel Kahneman in his 2011 book Thinking Fast and Slow summarized how as humans, we have two parts to our brain. We have the ‘fast’ brain, which makes those automated decisions we take for granted each day - brushing our teeth, driving our car to work or swiping mindlessly on social media. In order to make these automated decisions, our brains rely on simple heuristic rules of thumb.
We then have our ‘slow’ brain, which is responsible for more deliberate, rational, complicated decisions such as doing our weekly budget, trying to set up our home internet or cooking a new recipe we’ve never made.
Kahneman notes that we get into real trouble when we use our ‘fast’ brain to make important decisions, that we really should use our ‘slow’ brain for. These biases prevent us from making the best decision - the bias clouds our better judgement. These are really common in hiring, especially when screening candidates and interviewing candidates, for example.
This is why data literacy is so important. With a basic understanding of data literacy, we can all see how our ‘fast’ brain tricks us into making biased decisions. Once we rationally look at the data, we can arrive at the correct decision. But for people to be able to arrive at that better conclusion, they need to be comfortable working with data; they need to be data literate.
A data literacy program is often part of an overarching data strategy, which itself is sometimes part of an overall digital transformation, especially in larger enterprises in more traditional (non-tech) industries.
A data literacy program normally involves:
For example, you might identify 4 different data ‘personas’ in your organization. Each of these personas might have different expectations regarding their own levels of data literacy. Then, for each role type in your organization, you’d map these to one of these 4 personas. Each person then has a clear understanding of the expectations of them when it comes to data literacy.
Each person then completes the relevant data literacy quizzes for their persona. These quizzes give insights into their relative strengths and weaknesses and whether or not they meet the relevant requirements of the persona.
With these strengths and weaknesses then measured, you can make a data-driven learning and development plan to plug the gaps identified.
Data literacy programs are best as an ongoing initiative, not a one-off project.
It’s easy to get caught up in hype, especially when it comes to buzzwords like data literacy.
We’d suggest being crystal clear about the goals of your data literacy program from the beginning. This will help you to keep everyone aligned and everything on track.
When we work with organizations, data literacy programs are typically trying to answer the following questions?
Identifying your own goals with your data literacy program will be essential to your program’s success.
Yep, you guessed it. It’s a great idea to go with a small scale pilot first. These pilots are normally either done with a small, broad-based group (10-20) people across numerous different functions, with varying levels of data literacy, or with a small group within a team.
A pilot (aka Proof of Concept or POC) has 3 big benefits of running a pilot program before a wider scale roll out:
Finally, we’ve noticed that in large organizations, if you label something a pilot, you can normally circumvent red tape and bureaucracy that springs up.
From our experience in helping organizations understand their data literacy capabilities, we’ve learned about the common mistakes that organizations make when running a data literacy program.
Here’s the most common issues that we see crop up:
Lack of buy in from the top
In some organizations, the data literacy programs are rolled out without senior executive sponsorship. Like with any internal project, this is going to cause problems. If the top executives aren’t participating, then it sends a message to others down the line that it’s not important. The buy-in can’t just be lip service. While it’s important that senior management is seen to be supportive, they also need to actually participate. Ultimately, if the CEO of an organization themselves lacks basic data literacy skills, that’s going to be a huge issue for the organization.
Neglecting the ‘so what’
Some organziations sometimes struggle to communicate the ‘so what’ of the analysis into their data literacy that they’ve done. There’s no point collecting data and then not doing anything with it. Having a very clearly communicated goal of the data literacy program will be essential to getting broad participation. This needs to be thoroughly and consistently communicated. You should keep the same messaging in your internal communications (email, town halls, Slack groups etc.) and in your Alooba quiz comms.
Not showing people what’s in it for them
As individuals, we are generally quite self-interested. As a result, the ‘what’s in it for me?’ should be very clearly articulated. Yes, you will be talking about benefits for the overall organization, but try to make it clear to people how they benefit from understanding their own data literacy strengths and weaknesses.
Not using data - how ironic!
What gets measured gets managed, as they say. Like with any other project, it’s important to quantify your goals so that they can be measured. Without this, how will you define success or not? This is why it’s important to measure your organization’s data literacy, and keep re-measuring as you put in place learning and development initiatives. Without this, you won’t know if your training is working or not.
Not sticking with it
Without sponsorship, enthusiasm and momentum, sometimes projects can run out of steam. Data literacy is not a one-off, set-and-forget type of thing. In large enterprises in particular, it’s very easy for the next shiny thing to distract a leader who then wants to shift focus. If data literacy is essential to your organization, then you need to dedicate consistent effort to the program
Reverting to status quo
Just like reading or writing, data literacy is a skill for life. Once people have learnt this new skillset, it’s important that there is then actual behavioral change. This means making decisions based on data more than just gut feel and intuition.
What are some common mistakes organizations make when trying to improve data literacy? There are some common, recurring mistakes that organizations make when it comes to improving their data literacy.
It might be tempting to create a SurveyMonkey or Google Form and ask your teams where they think their strengths and weaknesses are, and what training they think they need. Some organizations might want to try this approach, rather than actually validating peoples’ skills.
Unfortunately, this survey approach does not work well for a few reasons.
Additionally, there are a whole range of different cognitive biases that can be present when completing surveys. Here’s a good discussion of some of the more prominent ones.
It’s really essential to understand the true state of data literacy in your organization. This can only be achieved by actually measuring it. The truth will set you free, as they say.
Humans are change-averse. This is because it’s much easier to keep doing things the same way you always do them, rather than change. Any time a new technology or way of doing things comes along, there will always be resistance. For example, check out these - quite hilarious - articles about the internet from 1995, or indeed a little further back to the Industrial Revolution with the Luddites.
James Clear’s wildly popular 2018 book Atomic Habits did a great job at explaining why it’s so hard to change habits, how to get good habits and how to lose bad habits. We’d recommend trying to incorporate some of his learnings into your data literacy program.
Here’s our top three suggestions for how to overcome resistance:
Here’s a good run down of what’s included in the Alooba data literacy test. Looking for a demo of the test? Feel free to book a discovery call with us to learn more about how we assess data literacy in your organization.
When it comes to data literacy training, you’ve really got three options:
We tend to find that most organizations handpick & recommend various MOOCs to their team.
There’s some fairly common questions that participants have. You should include consistent answers to these in your internal communications.
Being able to compare your organization’s to other similar organizations in your industry is very powerful. This external lense gives you a glimpse into how you compare against similar organizations, and also gives you the ability to set goals for reaching the levels of organizations later on the data maturity curve.
Alooba has 50k+ data points of participants across various industries & geographies, which provides the basis for the Alooba benchmarks.
Yes, in addition to the Alooba proprietary content, you can also build out your own content. You can add questions via the Question Bank feature. You might like to add customized questions to make the experience hyper relevant for your teams. For example, you can include datasets from your organization and include your own business metrics.
This gives you the ability to understand your team’s data literacy as well as their fluency in your own systems, data & metrics.
Check out this free data literacy quiz to get a quick glimpse into your own data literacy.
After taking Alooba data literacy quizzes, you will be able to understand the strengths and weaknesses at an organization, geographical, business unit and individual level. You’ll also be able to benchmark yourself to other organizations. Armed with this knowledge, you can then put in place data-driven learning & development plans to plug the skills gaps.
This is a good question, and things are evolving quickly. This is quite organization-dependent, so we’d recommend checking with your manager. Organizations normally develop their data personas, and then map them to existing roles in an organization.
Sure, make sure you check off these 5 items and you’re ahead of the game.