Knowing how to interpret the data we generate creates value
Although we talk about digitalisation, innovation and artificial intelligence, we first of all need to learn about datafication and data literacy.
Datafication is the process of generating data. In our personal and professional lives, we are constantly generating data. We generate data with our mobile phones (when we move around and open apps), our watches (health data, step counts, the amount of hours we sleep, etc.) and even our domestic appliances: speakers, televisions, thermostats, refrigerators, washing machines and other sensor-equipped devices also generate data. When we browse the Internet and interact on social media, we generate data in real time.
Although this can have negative implications (facilitating an invasion of privacy), we need to use datafication in a way that benefits us. For companies, datafication and making good use of data is the key to standing out in the global market.
Thanks to technology, we have terrestrial and wireless networks that can transmit data at speeds that were previously thought impossible, and which provide access in real time. We must also remember that low storage costs allow us to keep data in different cloud storage systems, free of charge (depending on the amount of data in question).
Data plays a key role in accelerating a company’s digitalisation and enabling innovation. Without data, there would be no artificial intelligence.
When we talk about digitalising a company, we mean optimising and automating its processes, while innovation means creating new products and services that make the company stand out. The use of artificial intelligence by companies enables us to identify patterns of behaviour on the part of our customers. In turn, this allows us to offer personalised products, understand what we need to do in order to achieve our expected results in the future, and predict the outcome of certain courses of action. In short, digitalisation, innovation and the use of artificial intelligence make it possible to identify new business opportunities.
Once we have learnt about datafication, the next step is to make sure we know how to work with data. This leads us to our second subject: data literacy.
Data literacy is the process of reading, working with and knowing how to communicate data. It is a bit like speaking another language. If we learn it and use it, that language can become an additional professional skill that helps us in our personal and professional lives. Data literacy therefore allows us to develop great analytical capability.
The first stage in data literacy is the ability to read data. When presented with a data visualisation, whether in the form of a table or graph, we need to ask three questions: are the data representative? Do they meet the criteria for good representation? Lastly, is the interpretation of the data distorted in any way? When we look at a graph, we need to keep in mind that whoever created it did so with a particular intention, with the aim of conveying a particular message. Two different visualisations of the same data can show different realities. Let’s look at an example:
Which graph do you think a human resources department would present to a company’s shareholders if it wanted to increase employees’ salaries?
Secondly, we need to work with data, which involves creating a hypothesis, searching for and collecting data, storing them securely (very important), cleaning and refining them, analysing them and updating them (in the knowledge that data can become out of date).
Lastly, the third stage in the data literacy process is learning how to communicate data. To this end, we need to ask ourselves: what is the message we wish to convey? To whom? Via which medium? Last but not least: what type of reaction are we looking for?
When a company’s employees have integrated datafication and data literacy into their workflow, can we say that the company is now data-driven? The answer is: datafication and data literacy are only the start. But that is a subject for another article.
In conclusion, learning that we are all immersed in the process of datafication is a positive step. Applying data literacy is an analytical skill that will give us a personal and professional advantage in our workplace and enable us to stand out. It is, however, up to us to make the most of it. Shall we begin?
Published in Diari d’Andorra 21.09.23