Big data analytics is the next trillion-dollar market. The most amazing thing about data and analytics these days is the people who are involved in this. These people are always on the bleeding edge of technology, techniques, pushing all the boundaries to collect raw data, process them, analyse it and draw better insights.
As the companies are expanding and increasing their hold on big data trends, technologies should pace up at the same rate to strengthen their grip on the ever-changing business needs. Companies have been creating new data models to shatter the existing ones based on the more critical and sophisticated use of data.
So with this ever changing big data trends, I would like to bring to you the top six big data analytics predictions of the year 2016.
1. Embedded Big Data Analytics:
According to the prediction of IIA, computing will be severely microservice enabled where everything including the analytics will be connected via API. According to another prediction by the IDC, it reveals that by the year 2020 we will see that 50% of enterprise analytics software will have prescriptive analytics built on cognitive computing functionality. Cognitive Services will become an integral part of all the newly launched apps. It is roughly estimated that embedded big data analytics will provide the U.S. based enterprises around 60 billion dollar annual savings by 2020.
2. NoSQL is dominating:
NoSQL is gaining momentum. In this era of web, mobile and IoT the concept of NoSQL is gaining a lot of popularity. In 2016, we will definitely see some severe architectural change of the old legacy system where most of the companies would be switching to the NoSQL system. The global NoSQL market will reach to 4.2 billion dollars by the end of 2020, with a growth rate of 35.1 percent compound annual growth rate between 2014 and 2020.
The main reason for such a surge in demand of NoSQL being, companies these days are looking for such database technologies which are far more flexible, scalable and customizable. NoSQL is suited for the purpose of Big Data analytics and IoT and for sure the traditional methods can no longer cope up with the volume and variety of data. NoSQL will indeed go mainstream.
3. Yes to machine learning:
In 2016, most of the companies would want to get onto the machine learning bandwagon. With each passing day, it is becoming more and more strenuous to find data scientists who could dig the high paced growing volumes and varieties of data. So for business insights, companies will turn to autonomous services for machine learning. We are also likely to hear a lot of progress in the field of Artificial Intelligence, with many startups and large organizations making major decisions and investments in this particular domain.
4. Solid State Drive Storage:
The year 2016 foresees a huge scope for solid state drive (SSDs). The spinning disks help to scale the data growth but according to the pace of these days, it takes ‘too much’ time to take the data off the spinning element. On the other hand SSD very fruitfully solves the purpose of pace, i.e. it is a stagnant non-moving element much like being in a memory hence the speed of extracting the needed data is quite fast. The gilt-edge solution will ‘flash’ and ‘disk’ to support both fast and dense configurations.
5. Hadoop will take a break:
While Hadoop is a decade old open source software framework for collecting data and running applications on clusters of commodity hardware. But this year will definitely see a lot of alternative cropping up for Big Data processing. Moreover, tagging data has also become a major concept. A data realizes its actual value only if it is noted at which context it was collected. So data, in fact, tagged data, will snatch the importance.
6. Higher Risks of Data Leakage:
According to the Gartner, statistics states by 2018, 50% of business ethics violations will occur through the inappropriate use of big data analytics. So a lot of focus would be on the detection algorithms. The new data analytics system leads to a pool of data where phishing data becomes the most potential threat. Mostly systems detect a threat after 90% of the damage is already done, wasting valuable information, time and money. Thus, threat detection algorithms will play a major role to multiply the significance of Big Data.
The Big Data Revolution has happened so fast that the companies lacked preparedness to face the cultural shift to a data driven culture. Forrester declares that all companies are directly or indirectly in the data business now. IDC also states that the organizations with higher ability to collect and analyse data and to draw proper insights will be able to make 430 billion dollar bucks extra over their less analytically stronger peers.
What attracts my attention and excites me in the year 2016 and the coming year is how far the organizations wish to go to survive in this big data trends revolution. I have done cherry picking with this blog and I hope you have also picked up the appropriate one for your organization. So what are you picking up for your enterprise?? Happy 2016 analytics planning 🙂
References:
enterpriseappstoday,marketingland,data-informed,forbes
Ajeet Singh
