Data literacy: the importance in a bird’s eye view

► PART I: THE IMPORTANCE IF DATA LITERACY IN A BIRD’S EYE VIEW

•   PART II: HOW TO DEVELOP A DATA LITERATE ORGANIZATION

•   PART III: HOW TOOLS HELP ORGANIZATIONS DEVELOP DATA LITERACY

Author: Lohic Beneyzet

Lately the term “Data literacy” pops up more and more. In short, it is the ability to read, understand, create, and communicate data as information. Different articles, like ‘10 Ways CDOs Can Succeed in Forging a Data-Driven Organization (Gartner)‘ or ‘Boost Your Team’s Data Literacy‘, call it one of the most important skills for the 21^st century. Let’s have a look at what these skills entail and why they are so important for businesses in a volatile era as this one.

What is data literacy?

To fall back at the short description above, that is quoted from the data literacy description on Wikipedia, let’s dig a bit into the full definition formulated there:

Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data.

To grasp this definition better it is important that we first get a uniform understanding of the word ‘data’. It is nothing more than a collective name for all types and bits of information ‘flying around’. In this digital era data are nowadays all around us; loads of information is constantly processed in very different formats. Businesses collect these data in a continuous flow, often without even realizing it. Very unfortunate. Data provide the inspiration and ingredients of a recipe, the composition for articles in a journal, the likely success factors for a documentary for Netflix or a decision-making platform for c-suites.

Types of data

As an example, let’s take a news message about COVID from the Dutch National Institute for Public Health and the Environment (RIVM) on April 6^th 2021:

“In the week of 31 March to 6 April, 48,186 people in the Netherlands received a positive test result for COVID-19. That is a decrease of 7% compared to the week before. The number of tests decreased by 10% in the past week compared to the week before… The reproduction number is now just above 1. The number of new patients admitted to ICU with COVID-19 in the past week increased by nearly 20% in the past week. The number of new COVID-19 patients admitted to hospital was about the same as in the week before (-3%).”

This news message contains the two data types: quantitative and qualitative.

Quantitative data in this message are i.e. the “48.186”, the “7% decrease” and the reproduction number of “1”. Quantitative data are numbers. The “number of people infected” is considered as discrete quantitative data: it is a finite set of numbers. On the other hand, the reproduction number is considered as continuous quantitative data: it exists of an infinite set of numbers.

The qualitative data in this message are i.e. “the week of 31 March to 6 April”, “new patients admitted to ICU with COVID-19” or “new COVID-19 patients admitted to hospital”. Those are non-numerical and describe qualities or characteristics. In the case above, the total number of new patients can be divided into two categories: the ones admitted to ICU and the ones admitted to other hospital departments.

Although data can be presented in raw format, without manipulations, most of the time it has been ‘processed’ before presented. Here, the percentage variance of people receiving a positive test has been calculated based on two other quantitative data: the number of people that were tested positive last week vs people that were tested in the previous week. The data processing is needed to “create data as information”.

After having created information, the next step is to communicate data as information. The intention of this communication is to simplify decision-making while informing, conveying idea’s and/or convincing people. Data communication can be done through different formats: it can be presented in form of text, like in our example, but also through graphs and maps. It is common to see those formats used together to help the storytelling.

This is where the “literacy” comes into play. Data literacy is about understanding data. It is an ability that implies three different ways of thinking:

• Analytical thinking: break large and complex data sets into small understandable bite-sized chunks to make more sense out of insights and simplify decision-making. In the mentioned case, the reproduction number helps people to understand the contamination rate of the virus more easily.

• Critical thinking: form an approach based on previous knowledge, on ruling out (own) bias, and look beyond the standard solutions; In our example, we could ask ourselves whether the source is reliable, if the numbers make sense. There is a decrease of positive tests, but at the same time there is a significant decrease in the number of tests. This might suggest that the number of positive cases reported might be lower than it in fact is.

• Ethical thinking: evaluating data against public regulations, but also privacy and other bias. The pandemic situation has raised many ethical questions. Can we share the names of the people tested positive with others? Imagine that the RIVM published a detailed list of the 48.186 tested positive…

Why is data literacy important?

The amount of data is more and more increasing. Businesses process data at every moment of the day and in very different formats. Information can be extracted from data that can help us to make decisions, act and anticipate. Next to the COVID-19 case, the most common examples are the weather forecast or sales strategies: “Using this product will reduce hair loss by 30%”. Another way of data usage seen in practice is influencing behavioural change: “If the whole world was eating fewer meats, greenhouse emissions declines would be around 70%.”.

With data widely available and more and more tools in the market that help organizations to read, process and interpret those data, the ability to create maximum value for customers and employees rises. In different researches, it has been revealed that businesses that rely on data management tools to make decisions are significantly more likely to beat their revenue goals than non-data driven companies. But to be able to draw insights, ask the right questions, take decisions and create true value from data, people need to be ‘data literate’.

Data is the new oil. Big business. Organizations need people that are able to refine data and make it valuable. Therefore people need to be able to make sense of the data, to examine correlations, to depict bias and to communicate using those data.

It’s a skill that empowers all levels of workers to ask the right questions to data, build knowledge, make decisions, and communicate meaning to others.

In my next article I will elaborate more on how organizations can develop data literacy within their workforce.