Data Literacy: An Essential Skill
In day-to-day life, we often make certain decisions and assumptions on the data we come across, but does this reflect the real facts? The answer to the question might be “yes” or “no”.
But the answer also explains the fact that “what we think we know, we may not know…”
Literacy is a term used to denote the “competence in a knowledge area”, and what do we call this literacy in Data interpretation? We call this Data Literacy.
As the amount of data being generated and collected increases at a very fast rate, these data should give actionable insights for better performance and benefits. This is where data literacy comes in. Data literacy is the ability to understand and contextualize data, having the knowledge to apply appropriate analytical techniques to the data and transform data into useful business value and insights.
How can we explain this with an example?
Shyam was the sales manager at a company named Kilezo. He was observing the sales data of his company’s product in relation to the number of customer coupons they were giving.
The graph is given below:
He observed that for an increase in the number of customer coupons, there was an increase in sales. Then, Shyam thought that “Okay, the reason sales are increasing is due to the higher number of coupons. So, let me increase the investment in the customer coupons strategy.”
Is this conclusion correct?
The answer is MAY NOT BE. Both the variables move together doesn’t mean that change in one causes the change in the other. Here the increase in sales might have been caused due to something else, say due to a change in the government’s economic policy or other promotions.
To introduce the more precise statement, correlation doesn’t imply causation.
How do we differentiate between correlation and causation?
· Correlation refers to the association or relationship between two variables. Here we see a linear association between sales and the number of customer coupons.
· Causation means a change in one variable causes a change in the other variable.
Now, if we come to the concept of data literacy:
We now realize that Mr. Ram was not data-literate. If he had been data-literate, he would take a better decision instead of wrongly increasing the investment in customer coupons without sufficient evidence.
Data literacy is the ability to work with data and derive meaningful information and insights from it. In today’s world, 2.5 × 1018 bytes of data are being produced every day, and not only this, the global data analytics market is estimated to reach $132.9 billion by 2026! And in such a scenario, it is sad to see that only 24% of business decision-makers are confident in their ability to use data. (According to a survey by Qlik)
Data literacy is required everywhere in business. Let’s see some more examples where data literacy comes into play:
· A marketing manager tracking and analyzing metrics like engagement, conversions, etc.
· A HR leader trying to find the causes of employee turnover and minimize it.
· An industry head looking to optimize manufacturing processes for reducing downtimes.
· A corporate executive aspiring to increase the company’s market share, hence aiming to understand and outflank competitors’ strategies.
And there are a lot of examples like this. Having got an idea of some real-world business scenarios, let’s address the main question:
Why is data literacy important in the present context?
· Improved business decision making
· Gain new opportunities and insights
· Boosts business value and performance
· Increase efficiency and optimize business processes
· And many more…
Do we know that large organizations with a high level of data literacy can improve their market value?
Actually, they experience up to 5% higher enterprise value ($320 to $534 million) compared to organizations with lower data literacy levels.
What is the conclusion?
It is clear that data literacy is the need of the hour. Data literacy is an essential skill without which one can neither succeed nor survive, for the future is going to be all about data, and it is imperative to leverage data to its fullest potential.
But the real world scenario is not ideal. According to a survey, 54% of youngsters (18–22 years) don’t consider themselves data literate. And the percentage is even higher for adults.
That is why, Alphaa AI is on a mission to create 1 billion Citizen Data Scientists. Through the Alphaa AI Citizen Data Scientist Fellowship, we educate people about data and aim to create a culture of data literacy, empowering business professionals and students to truly become data-literate.
Click the link to know more.
