Written by: Abhishek Yadav Did you know that we generate around 2.5GBs of data daily? What is even more surprising is the fact that just the last couple of years are responsible for 90% of this data. As the world gets more interconnected due to the growing number people in possession of smart, electronic devices, this figure is only likely to soar higher in the years to come.If you’re surprised as to how you generate so much data, let’s take a quick recap of what seems like a redundant daily routine. A good morning text on WhatsApp wakes you up, you put on some music on your favorite online music streaming platform, and get ready for work. Then, using your preferred provider, you book a cab. On your way, you browse through your inbox to see if you’ve left any emails unattended. You log on to Netflix and catch the last few minutes of that episode you left incomplete the previous night. You get back from work and realize it’s a Friday – the end of a tiring week. What do you do next? Browse through the internet for the latest movies and their showtimes nearest to your place. Then, you go ahead to book your seats and get an e-ticket in your mail.All of these are chunks of data you have been generating for organizations round the clock. These organizations come back with the same data to provide you better and personalized treatment. If you’ve used Apple’s Siri or Google Maps, you know what we’re talking about. Both of these (and many other) applications get better with time and start coming up with much more sensible responses. Enter the era of Big Data powered Machine Learning.Big and small organizations alike are opening up to the importance of leveraging this Big Data and are therefore investing in much more sophisticated tools, technologies , and algorithms to help them gather much more insightful analysis. Right from managing and optimizing the supply chain to retaining customers by enhancing customer experience, these organizations are turning to Data for all their analytics needs.With so much said and done, it’s only fair to assume that the future of Big Data is not just undisputed but also extremely bright. Let’s look at the 5 predictions for the future of Big Data:
Prediction #1: Emergence of new job roles
Data Analysts, Data Architects, and Machine Learning Engineers are some of the most sought-after job roles you’ll find if you browse Indeed or any other job portal. These jobs were practically nonexistent just a few decades back. In future, too, we can expect some fresh job roles to spring up. One such is likely to be the “Chief Data Officer (CDO)”. According to forrester
, CDO will be one of the most prestigious positions in a very near-future. Although the appointment of a CDO will depend solely on the organization and their data needs, hiring a chief data officer will become a norm sooner than later.
Prediction #2: Even more demand for skilled data scientists
As of today, there is a shortage of around 2L skilled data professionals in the US, and the scenario isn’t any better in India. The data deluge surrounding our world today has been accompanied by a growing demand for skilled professional
. Because of high demand and low supply, the organizations are ready to shell out a handsome sum of money to the right candidate. This trend is only going to increase in the years to come. So, if you’ve been thinking all this while about trying your hands at Big Data, let’s tell you that this is just the right time. The sooner you get your hands dirty in the pile of Big Data, the better chance you have of enjoying a successful career. There are numerous Big Data certifications
available online which will not only equip you for the revolution that is Big Data but also make you a valuable asset to any organization. Related: What to Do If You're in a Digital Meltdown
Prediction #3: Organizations will switch from buying software to algorithms
A drastic shift can be expected in the way businesses approach software. With more sophisticated algorithm coming up to help these organizations deal better with their data, we can hope to see more and more organizations switching to purchasing algorithms as opposed to software. The pros of buying an algorithm against a software are that once you buy an algorithm, you can modify it with your data and make it work seamlessly. However, you cannot tweak a software according to your needs. In fact, you’ll need to adjust your business processes according to the software. But, all of this will change soon as algorithms selling services taking center stage.
Prediction #4: The term “Big Data” will get modified to “Actionable Data” or “Fast Data”
Big doesn’t necessarily mean better. Keeping this as the core ideology, organizations will soon replace the term “Big Data” with Actionable/Fast Data. Organizations use a minuscule portion of their overall data, and most of the data is useless. This hinders the whole analytics process of the organization. Actionable Data or Fast Data is likely to take charge and improve things on this front. Having tremendous amounts of data will not give an edge to any organization, but having actionable data will. Related: The Impact of Technology on Work as We Know It
Prediction #5: Investments in Big Data will skyrocket
According to the analysts at IDC
, the total revenues from Big Data and analytics is expected to rise from $122bn in 2015 to $187bn in 2019. Big Data analytics is still a field that is under extensive research, and there are sophisticated tools and algorithms developed fortnightly. With the future holding so much data, it’s only fair to assume that these organizations will shell out whatever money it takes to ease their analytics workload by using state-of-the-art tools. Investments in the development of these tools and algorithms are likely to increase with each passing day.What future holds is just for future to tell, and it’s likely that some of these predictions might merely pass into obscurity. However, we’ll not count on that. Looking at the present scenario, we hope and believe most of these predictions will come true in the years to come which will help broaden the overall scope of Big Data analytics.