Data Analytics The Way Forward

“Data analytics is the future, and the future is NOW! Every mouse click, keyboard button press, swipe or tap is used to shape business decisions. Everything is about data these days. Data is information, and information is power.” ~ Radi, data analyst at CENTOGENE.

Data Analytics. We’ve all heard about the newfound adage’s growing popularity. Have you ever wondered why data is so important and how massive it really is, with the entire world clamouring for it and organisations all over the world bending over backwards to get as much data as they can?

Did you know that every day we generate 2.5 quintillion bytes of data? There’s a lot of data here. Simply said, if we had to duplicate this data to HD discs, we’d need around 10 million of them, and if we stacked them one on top of the other, the resulting skyscraper would be taller than four Eiffel buildings combined. And we’re talking about generating this volume of data on a daily basis.

However, when we need to make sense of huge datasets in a jiffy, this can be a barrier, as analysing data in real time to arrive at meaningful conclusions is important for staying ahead of the game.

Data analytics gives all of the solutions in this case. It is a process in which data is cleansed, analysed and modelled using cutting-edge technology, which subsequently aids in the extraction of actionable insights that may be used for decision-making.

But what is interesting is how the field of data analytics has evolved over the years. From collecting and analysing the limited amounts of data manually to inventing state-of-the-art and technologically advanced, sophisticated platforms and algorithms, it has undergone a sea change and evolved comprehensively.

Back in the days, when technology was not as advanced as what it is now, data collection and analysis was done manually. It was more like making a large number of excel sheets and trying to make sense of all the data in it and searching for the patterns which could help the organisations in arriving at actionable insights. But this process had limitations because of the limited data sources and the fact that it was time consuming and slow.

Eventually, a massive surge in data creation was witnessed in the whole world. Big data gained prominence and organisations looked for ways and means to handle the enormous amounts of data and arrive at meaningful insights in real time. As advanced technology came into the picture and concepts like data mining, neural networks, cohort analysis, etc. took analytics to a different level, and access to automated options for managing data became available, the data analysts could analyse a mountain of data in a few minutes and make sense of it. They could now analyse data, trends, etc. and come up with conclusions and suggestions in quick time.

Now, with the era of access of smartphones to a large number of people, connected devices and connected homes is becoming the norm, the creation of data has gone through the roof, thus paving the way for even more advancements in technologies to analyse these huge amounts of data. Organisations deploy complex algorithms to process data in real time, which helps in providing recommendations and suggestions derived from the analytics rooted in the data. Machine learning and AI are being used in addition to data mining and predictive analysis, which is making companies take quicker decisions based on sound data-driven insights. While data mining is used on an existing dataset to look for patterns, machine learning and AI, on the other hand, work on “training” datasets, which teach the computers how to make sense of data. When newer data is fed into the system, ML uses all its experience and training to analyse this data and come up with useful and meaningful recommendations. Big data technologies and cloud computing delivering predictive analytics are now being used by companies to stay ahead of the competition. With advanced cloud platforms, it is now possible to handle complex analytics to deliver precise predictions, thus helping companies to be proactive.

This has had a rub-off effect on the job market as it has led to a surge in demand for trained data scientists and data analysts, which have become very lucrative and in-demand profiles.

The future of data analytics certainly looks exciting as we can expect newer and advanced technologies to make it even more fast paced and ahead of the times.

Further reading