Data literacy | Numeracy | Core competency for 2019

Data Literacy: core competency number 1 for 2019

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Data literacy becoming crucial

As we recently pointed out, data literacy is a hot-button issue. Only 24% of the decision-makers in international business spheres indicate that they have complete confidence in their own ability to read data, work with it, analyze it, and discuss it. This alarming conclusion was drawn by the worldwide Data Literacy Project. Spoiler alert: 2019 could be the year of data literacy.

The concept of literacy is a lot broader than most people think, according to Wikipedia. It’s the ability to work with information, understand it, and use it with purpose. In the Netherlands, Princess Laurentien has been working tirelessly to promote literacy since 2001. At her behest, the Foundation for Reading & Writing was founded in 2004. Although the foundation achieved successes in its fight against illiteracy, the war is far from won.

Numeracy

According to National Numeracy, a UK organization aimed at reducing rates of adult innumeracy, nearly 80% of the adult population have numeracy levels below the equivalent of a GCSE ā€˜Cā€™ grade and 49% are at a level expected of children at primary school. Innumeracy (sometimes unflatteringly called data blindness) is becoming a big problem in workplaces, both for individuals and the organization. As society becomes more and more digital, this issue is only going to grow in scope and importance.

Data literacy is an essential skill in the fourth industrial revolution. Data literacy enables people to ask critical questions about data and machines, to gain knowledge, make decisions, and share the meaning with others.

Shaky sample

Organizations that want to automate their processes and cash in on their big data will have to make significant investments in educating and training their employees in order to improve their data literacy. Striving for data literacy in practice also means fighting against bad test samples and non-existent causality. This was written about as early as 1954 by Darrell Huff in How to Lie with Statistics.

Dangerous cocktail

Numbers and data can mislead us as easily as they can lead us to new insights. Scientific journalism in combination with health factoids is perhaps the most dangerous cocktail. Mistakes are often made early in the process, when planning the research. Researchers can make six crucial mistakes when determining sample sizes. The conditions or the questions can be wrong, the research might exclude certain groups, or the sample size is too small. If not enough people want to participate, the non-response rate is too high. The uncertainty margins could be overlooked, or the researcher has a vested interest in achieving a certain outcome. Scientific research is, as you might expect, an exact science!

Correlation does not imply causation

It’s easy to make mistakes when interpreting data, too. One of the most common mistakes is reverse causality. The problem with correlations that if you look hard enough, you’ll always find one. But that correlation could just as easily be coincidence. The so-called p-factor (probability) can be the decisive factor. Other factors can also influence both the cause and the result: a missing factor. How can you make all this clear to the public at large and the people in the workplace who never studied statistics?

Use the data opportunities

The founders of the Data Literacy Project don’t want to leave anything to chance. Education and training are the only “cure” for data illiteracy. This is the age of Data Opportunity, according to Qlik. The data analytics firm is taking charge, along with founding partners Accenture, Cognizant, Experian, Pluralsight, the Chartered Institute of Marketing, and Data to the People. Their goal is to encourage discussion and develop the necessary tools to strive for a more data-literate society. Of course they also want to make money, but there’s nothing wrong with that per se. The Data Literacy Project has great social value, after all.

Make every colleague into a confident data aristocrat

The thing that’s often still missing is self-confidence. In other words: how can we turn a data doubter into a confident data aristocrat? By delegating as many decisions as possible to data-proficient employees, according to Quooker, the successful Dutch inventor and maker of the boiling water faucet.

Social relevance

“The rise of automation, robotics, and artificial intelligence is increasing the importance of our data skills,” according to Mike Capone, Qlik’s CEO. Data skills are essential in the current digital revolution, according to him. The participants of the Data Literacy Project provide individuals and companies with training materials, interactive assessment tools, and access to a network of experts. This network doesn’t just support the development of personal skills, but also works on increasing awareness about the importance of a data-driven company culture. “Individuals have to be able to trust the integrity of their data and analytics findings,” according to Anjeev Vohra, Accenture’s global technology officer. Data literacy, according to him, is an essential skill that enables us to be critical about information before making crucial decisions.

In short: data literacy is the weapon of choice against bad decisions, detours, and other mistakes. Education institutions around the world should therefore add data literacy to the standard curriculum.

Data Literacy Index keeps finger on the pulse

While some people are sitting in classrooms taking data literacy classes, the Data Literacy Index tracks how organizations and countries score when it comes to data literacy. That’s the sum of all human competencies, the amount of data-driven decisions, and the degree of data saturation within organizations. Singapore scored the highest on this index worldwide, with a median of 84.1. Europe’s no slouch either, with England leading the pack there with 81.3.

We know that there is a correlation between data-skilled businesses and their results. Front-runners in the field of data literacy have a three to five percent higher market capitalization. Data literacy also have a positive effect on performance indicators, like the margins before tax, return on assets, return on equity, and return on sales. Nevertheless, only a third of the businesses surveyed offer data literacy training. A meager 17% of companies stimulates its people to become more skilled with data.

Four factors

Can people read data? Are they capable of applying analytics in their daily work? How many data illiterates are you actually working with in your team? Insights like these allow your people to use data more efficiently and become more data literate. Four factors are crucially important: access to data, attitude towards data, skills, and critical thinking. These aspects can be scored on the Likert scale, from 1 to 5. The total scores indicate the general degree of maturity when it comes to data literacy.

Bridge the competency gap

The total scores could end up being disappointing. There is definitely a competency gap. Gartner, in the report “Build a data-driven enterprise”, therefore predicts that 80% of all organizations will start developing data skills and competencies, the goal being to bridge this gap.

We’re nominating data literacy for word of the year 2019. Data to the people!

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