From the course: Introduction to Analytics Engineering

How the field of data has changed in the last couple of years

From the course: Introduction to Analytics Engineering

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How the field of data has changed in the last couple of years

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- [Instructor] How has the field of data changed in the last couple of years? Most data titles that are normalized now have only been around for a few decades, whereas data collection/analysis has been a function in society for centuries. The term data analysis as used today, is credited to have come from the '60s. The term data science came about a few decades later, in 1985, as another way to describe statistics. The modern database as we know it today started in the 1970s with the invention of the relational database by Edgar F. Codd. SQL then became popular in the '80s. That's not that long ago, yet in the decade since, the field of data has dramatically changed. As our need for data exponentially grew, so did our technology. Businesses have become data driven, a term that we hear quite often now but did not exist until recently. With data becoming more accessible, our need to store it more efficiently became evident and that's when the idea of data warehouses became introduced. Before data warehouses, many large businesses would store the same data in different environments for different users. That meant there was a lot of redundancy cost in storing and querying data. Now, people are starting to view data not only as a business cost, but a cost that they can look to cut down on like any other aspect of the business. This is how data warehouses came about. It introduced the idea of a centralized system for data and data aggregations that can be most cost efficient and easily accessible for decision makers, but it's not just data science that's exploding in demand, other data roles are too. Because of the growing tech industry and the demand for data growing from the '80s to now, we have many different types of roles within data. There are the more familiar terms such as data scientists, data analysts, and analysts in more specific fields, and data engineers. There are newer titles such as data science product managers, machine learning engineers, and data strategists. In 2018, McKinsey called the analytics translator, "The new mus-have role", which I'm not sure about you, but this was a new job title to me. There are so many different functions within the data realm, from technical to non-technical. We need people who are able to make the data accessible, comprehend the data, and manage all the data our company may have.

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