How has data analytics evolved since its inception?

Data analytics is a continually growing field, and it’s changed dramatically over the past few years – with the healthcare sector being no exception. In this blog post, we’ll look at the ways in which this field has developed since its inception, and how it’s changing the future of patient care for the better.

Data analytics in itself is a relatively simple concept: to help individuals and organizations make sense of data. It’s the science of studying and analyzing raw data in order to make conclusions about the information in question, which can then be used in a number of ways, from affecting public policy to changes within the healthcare sector. There are endless possibilities when it comes to data analytics, but in order to look to the future, we need to understand where data analytics came from and how it’s evolved over time.

Analytics itself is based on statistics, which has obviously existed for centuries. However, the first tangible use of data analytics for business purposes goes as far back as the 19th century, when the U.S. Census Bureau began processing citizens’ information; however, it took over seven years to sort through these mountains of data for one census alone. The need to better understand this sensitive and valuable information led to the invention of the tabulating machine – an electromechanical device designed to assist in summarizing information stored on punched cards.

In terms of significant developments, these machines stood as the status quo until the mid-1970s, when the invention of regional databases allowed users to write in sequel and retrieve data from their database. These two concepts quickly became popular, as they gave the analyst the opportunity to analyze data on demand. That being said, they were not designed to translate unstructured data, which gave analysts yet another problem to solve. Additionally, with the internet growing in popularity over the course of the 1990s, analysts found that regional databases were unable to keep up with the demands of this enormous flow of new information. As a result, non-relational databases came to fruition, which lacked the rigidity of regional databases and enabled analysts to translate data using different language and formats quickly, replacing organized storage with greater flexibility.

Fortunately, we’ve come a long way since then, with the vast majority of data analytics now operating in the digital sphere. The amount of actual data being collected grew substantially - along with the introduction of hard drives and the low costs associated with them - leading to the development of the first data warehouses, which were optimized leading to a quick response time to queries. Around this time, data mining first made its appearance, and the combination of these two concepts led to something entirely new: big data.

A wide variety of software frameworks evolved during this time, along with new paradigms such as the cloud and modern predictive, augmented, and cognitive analytics – leading us to the present day. A lot has changed in the field of data analytics since its inception over a century ago, but the core principles are still very much the same.

As with any technological progress, there’s no telling exactly what the next evolution of data analytics will look like. However, one thing that we can be sure of is that the future of many businesses will either depend or rely on this concept, particular in the healthcare sector. Data analytics is now an integral part of countless organizations, with the conclusions drawn from it providing key players in the industry with invaluable insights on just about everything – from patient-reported data to the entire research and development process. Understanding how to safely, responsibly, and ethically harness the power of this technology is crucial in improving the current state of affairs within this sphere – something a growing number of healthcare providers are starting to realize.

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