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Data has been a hidden jewel for several decades. Back in the days in the early 90s data was considered to be a static element. Most use cases were just storing information and retrieving it at a later date.
In the late 90s science started to realize that something can be done with this. Labs started playing with the content, primarily numeric data and see how the trends are, later translating the same to Data Mining. Once industry started to realize the potential on how to look at the data, it started benefiting from the hidden insights. These were no secret treasure, but value of a feature you discovered late. I discovered cooling compartment in my glove box after 5 years of driving my car!
Data Visualization started to evolve with tools like Cognos, Crystal Reports, Power BI and now Tableau. In the last decade, these data visualization techniques and tools have prompted scientists to do more mature analytics, leading to the birth of Data Analytics for the common masses. Many techniques started coming up.
Beginning of this decade, we started to hear about Data Science. Interesting things started happening with the maturity of tools like Python, R, Hadoop, Big Data and many more. This is the most powerful transformation period. When every little drop of data is considered precious and industry started investing a lot in storing information in active stores rather than tape drives as of 2 decades ago.