Data Literacy: why training is important — and not enough to stay relevant

My maternal grandfather was born right after World War I in a small, backwater village in The Netherlands. His family could just make ends meet and only provide for basic education. But my grandfather was determined to make somebody of himself, and moved to the big city of Rotterdam to join its police force. Unfortunately, his basic education and strong dialect made him subject of discrimination and sometimes ridicule.

He did not accept this and knew what to do: catch up and educate himself.

In the hours that he was not on duty, that is.

Why Data Literacy Is Important

For us human beings, language is vital for our social interactions. We judge and are judged by our ability to express properly and convincingly. Most of us can go to school, so the importance of written language has increased tremendously. As my grandfather found out the hard way: in our modern society it is next to impossible to succeed if your skills to read, write or build convincing arguments are lacking.

Like he, we need to work hard to become and stay relevant.

Modern, 21st century organisations thrive through emails, reports and presentations that try to inform and convince. Even our personal lives are filled with status updates, tweets, blogs, books, podcasts, adverts that fill our head with other people’s ideas and convictions. We use different media to support what we want to express (language, video, voice, sound), but below the surface there is a narrative that we need to express. So, it is more important than ever to be able to write properly and effectively.

But there is something odd with numbers and, by extension, data.

Even though organisations become more and more dependent on them, the ability to communicate and deal effectively with data is lacking. As a result, organisations lack the ability to get information across that is supported by data. Let alone the ability to use data to distill information, insights and value from data. It is not entirely clear why that is the case. One reason could be that numbers and data are, for many, not on the same level as spoken or written language. When talking with intelligent, educated people, you can sometimes hear them say (with some pride, even) that they "are not good with numbers". That they were glad to get rid of having to deal with them once they progressed their education. In contrast, those same people would never admit (in the same tone of voice) that they were "no good with words and phrases".

Being illiterate is a taboo among successful professionals.

Being data illiterate apparently is not.

Such an attitude does not make any sense this day and age where data is everywhere. Data has been fundamental to the success of tech giants and their incumbents. The same will become true for most organisations in the very near future. So the inability to make the most of data is a recipe for disaster.

To become data literate is essential for survival: both for professionals and for organisations.

What Is Data Literacy?

On the Wikipedia page, Data Literacy is defined as the ability to read, understand, create and communicate data as information.

But that definition is incomplete, because it only applies to an individual’s competency with data. Added to that, organisations need to have this ability too. Reports, emails and presentations are acceptable means to inform and support decision making. It totally makes sense to send an email with a powerpoint filled with arguments to support your point of view. People will be able to understand, challenge or accept what you wrote down.

Similarly, organisations need to allow information and arguments built from data to do the same. Even more, they need to be able to understand, challenge and enhance such information to create sustainable value. That is why it is not enough to only train your people or to hire data professionals: an organisation needs to enable these professionals to create value from data and judge how they are doing.

To understand why, you can use the analogy of a restaurant. In order to be a successful restaurant, you need professional cooks, customers, waiters and managers to work together. It is not enough to hire cooks or teach some colleagues how to cook (a.k.a become data literate) if the rest of the organisation does not know how to create value from their cooking. Or whether the food is any good before it is served to the customers.

To create value means that people understand how to work together and do work that aligns with the organisation’s strategy. Only if you are able to understand how data fits into the core of your business, you can successfully optimise and innovate with data. Tools such as the value chain for data can help you with this, to brainstorm, validate, assess value and prioritise based on alignment with the overall mission.

At the same time you need to be able to validate value. That means that you are able to work your way back from what people express, explain or create with data. You do that through a series of logical steps, just like you would when validating arguments that were spoken or written down. It is like a chef who tastes and judges the food in the kitchen before it is being served to the customers: salt, sweetness, bitterness need to be in the right combination for the dish. Data can be twisted and turned in many ways, just like words, and support many views. Moreover, the investment required to take data products into production is a heavy. So, it is vital you can understand how people have used data and judge potential risks or fallacies in how they approach this.

Because the saying "there are lies, damned lies and statistics" exists for a reason.

How To Become Data Literate

For an individual the way to become literate in anything is through training. So, as an organisation you will need to invest in training your people, and we are happy to help you with that. For an organisation on the other hand, you need to invest in creating a structure that enables your data literate professionals to work together effectively.

The type and depth of training depends on the role a person has in an organisation. A basic, yet essential, level of training is what we call an awareness training. This training teaches the general concepts of, for example, machine learning, experimentation, cloud, data-driven product development, or basic statistics. It is part inspiration and part teaching of concepts. We usually provide this to people with the highest level of responsibility: instead of teaching operational skills, we teach how to keep informed. This type of training gives the ability to assess (at a strategic, conceptual level) the success, risk or failure of developments around data and AI.

Other people need for more in-depth skills training, preferably (but not necessarily) building upon those skills they already have. For example, a data analyst who wants to become an analytics engineer or data scientist; a statistical analyst who wants to become a machine learning expert; or a business analyst who wants to become an analytics translator.

Now you cannot train an organisation like you can train a person. Instead, an organisation needs to develop the structures to make data professionals effective. One way to start with this is to assess where your organisation currently stands. You need to ask questions such as: is your current workforce of data professionals mostly external or do you already have a strong internal presence? Is there a vision to attract and educate talent? Do your data professionals share knowledge and best practices? Are you dependent on external suppliers for your core data infrastructure, or do you have a self-maintained, modern data platform for that purpose? And how resilient is your current setup? Is it a monolithic, brittle structure or can you easily adapt, change or even repair?

And so on.

If you want, feel free to take our free self-assessment and take it from there.

If you go through these questions systematically, you will get a picture of the current level of data literacy of your organisation. Based on that, you will start to see in which areas you still need to invest in. Combine that with your level of ambition towards becoming a data literate organisation, and you have the start of a data strategy.

When not To Invest in Data Literacy

Now, there are organisations that should avoid investing in data literacy.

For example, if your organisation happens to be run in an autocratic way that does not allow for data-supported arguments to influence decision making. Or if your organisation happens to like that data can be interpreted and bent in many directions to support whatever the most dominant argument supports.

Such organisations should not invest in data literacy, because they will not see any return other than frustrated data professionals leaving the organisation because they cannot go anywhere with their skills.

How to stay relevant

To make your people and organisation more data literate puts you on a path to becoming data driven. But we live in a fast paced world where data and AI develops at a lightning speed.

What is relevant today might be irrelevant tomorrow.

The key to stay relevant is through the people in your organisation. If you support them in what they like do, enable them in their role and keep investing in their education, you will automatically keep up. To enable this even further you should support this in your organisational structure, by setting up communities of practice for your data professionals. In that way, people can build the network to keep up, but also have an informal way of peer review to make people keep up.

Such a community will be able to more or less autonomously hire, train and enable the professionals that are able to create value. They will develop golden paths, training programs, and professional journeys in order to keep up. The only thing the rest of the organisation needs to do is two things. First, to trust that those professionals are doing the right thing. Second, to challenge them in how you can deliver sustainable value together.

It’s a journey without an end, so you better enjoy the beautiful scenery that will pass by and make you a more complete being.

Final words

My grandfather worked hard to prove himself in a metropolis run by harbour barons, criminals and other hard working professionals. The world had just gone into the Great Depression and was quickly heading into the World War II. Society did not make it easy for him. But he kept educating himself, improved how he interacted with people, spoke with them, and (eventually) commanded and led them. He slowly worked his way through the system and landed several promotions. He made himself a success by becoming a literate person, able to write and judge reports that put people in jail and helped keep the people of Rotterdam safe.

Way to go for a person who just went to elementary school.

Just imagine where you can bring your organisation when you make it data literate!