The history of the emergence of the profession of data analyst. The concept of data, data analytics. Why data analytics appeared
The history of data analysis begins around the 70s of the last century, when the American mathematician and scientist John Tukey published his book “Exploratory Data Analysis” or “Intelligence Data Analysis”. In the book, Tukey writes that it is necessary to explore and analyze data to confirm or refute the hypotheses put forward.
Let’s think about what “Data” is and why it needs to be analyzed at all.
Data – this is the presentation of facts or information about something in a form that a person can understand and interpret, as well as convey this facts / information to others.
There are a lot of examples of data in our life: records of banking transactions, records from various sensors or video cameras, technical records of devices in factories and enterprises about the mode of operation, online surveys, a log of attendance at training or training classes, notes in a notebook, binary code from 0 and Data can also include media content in the form of music or video.
In the next section, we will analyze what types of data exist.
We realized that a lot of objects reproduce a lot of data around us.
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What to do next with this data?
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Analyze!
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Why analyze data?
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To get information and knowledge from this data!
Indeed, the data itself does not bring much benefit to a person. Real value can come from data that has been analyzed, or knowledge about the data.
In addition to data, it is customary to single out several more concepts: “Information” and “Knowledge”
Information is a set of processed data that has a specific meaning. Information should be relevant (necessary for a person), should have a specific purpose and context. Based on the information, a person can make decisions.
Knowledge is processed information that is used or has been used to make decisions.
In other words: knowledge is derived from information and information is derived from data
Let me give you an example from life:
Petya Petrov is going on a date and wants to book a table at a restaurant. He decides to find the opening hours of the restaurant, as wants to book a table for the evening (at 20:00). Petya found a restaurant website on the Internet with a picture of the work schedule, which says that the restaurant is open every day from 10 to 22:00. Petya reserves a table for Tuesday at 20:00 through the site.
After 15 minutes, the restaurant manager calls Petya and says that the restaurant’s working hours have recently changed and the restaurant is open until 20:00 on weekdays and the booking needs to be rescheduled. The site has an outdated schedule.
Disappointed, Petya decides to postpone the long-awaited date until Saturday for the evening, because his girlfriend said that on weekdays she cannot meet him before 20:00.
What in this story is data, information and knowledge?
Data – a picture with the restaurant’s schedule from the site (it has data on working hours, but this data turned out to be historical and irrelevant at the moment)
Information – a call from the restaurant manager and a conversation about the updated work schedule (the manager gave relevant and up-to-date information, in the right context for Petya)
Knowledge – Petya’s decision to reschedule the date and book a table for Saturday (Petya received information from the manager and, based on this information, decided to reschedule the date, while thinking about the convenience of the girl)
Data is collected and accumulated everywhere – in large corporations, enterprises, stores, shopping centers, on city streets …
So who can process this data to get the necessary and valuable information and knowledge from them to make the right decisions?
That’s right, Data Analyst!
data analyst – a specialist who works with data, collects, processes, studies and interprets. Thanks to his work, companies and other organizations can make decisions in their activities. In other words, the decision-making process based on the analysis and interpretation of data is called the data-driven approach.
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