With the emergence of data analysis, artificial intelligence and machine learning as the new pillars of e-commerce, the ability of creators and consumers of solutions based on these elements to “talk about data” in a common way is a real issue.
Just like literacy and the ability to write, read and think critically, Data Literacy has become essential in everyday life.Being data-literate means being able to read, use, analyze and communicate with data and think about data critically.
Whether it is an individual concerned with the use of his personal data or a professional with a need to make informed decisions, data culture has become necessary and sometimes even essential.
So how to become data literate?
First of all, don't worry, becoming data literate is important, but the required level is not necessarily that of a guru. In a professional environment, it will depend in particular on the role of the employee and his exposure to data. The expectation will not be the same between a machine learning engineer and a project owner, for example.
As with learning any new language, becoming data literate starts with understanding basic terms and key concepts.
In the case of data, the three fundamental elements to learn about data are as follows:
#1 Data management
For more on this topic see my articles: data manager what is my job and Data Management, what is it all about?
#2 Data analysis
Without going into too much detail, the field of data analysis contains four major types of analysis (descriptive, characteristic, predictive and prescriptive). Each has its own specificity, function and value in the company.
#3 The implementation of the valued data in a context
Here we are talking about harnessing the value of data to meet an economic need and/or provide an informed solution to a problem.
Data science, the foundation of data literacy
Data science is an interdisciplinary field that uses scientific method, processes, algorithms, and systems to extract knowledge from data in various forms.
Data science is not to be confused with artificial intelligence which can be described as a huge collection of tools that give machines smarter behaviors. Data science is also not machine learning. Machine learning can be defined as a sub-domain of artificial intelligence and aims to give algorithms the ability to “learn” without having been explicitly programmed.
Data science in practice makes it possible to discover elements of information within data. By delving into this information, the data scientist's goal is to identify and understand complex trends and patterns. The next step is to bring that information to the surface and help the company to make more informed decisions.
Finally, the implementation of data science and the data culture in a company is based on concepts similar to Maslov's pyramid of needs.
At the bottom, the collection of data, which supports the movement and storage which in turn supports the exploration and transformation of data. If these three basic elements are strong and each one supports the following, good fundamentals are in place to go further in the usage of the data. Aggregation and categorization follow, and only then will we begin to be able to use machine learning techniques and the optimization of the models.
This data science hierarchy of needs is a reminder that without a solid foundation, it will be increasingly complex to move up to the next level and attempt to produce quality results.
To conclude, becoming data literate is an adventure and mastering this new form of communication requires awareness and continuous learning to stay in touch with the rapid developments and evolution in the field. I hope to have contributed to it at my scale with these few elements and help you to start “talking data”.
