Future of Big Data in 2020

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Want to become an expert in Big data analytics, or a Data Scientist, or Big Data architect and don’t know about the future of big data, well you have landed on the right page.

IT industry has seen an immense growth and impact of Big data and its capabilities. The coming years are yet to see a huge advancement and revolution in the field of data science and analytics. With the upcoming tends and application of Big Data the prediction of its future speaks for itself.

Weshall now discuss in details some of the latest predictions made by topindustry experts about Big Data.

#First Prediction – Needfor Real-Time Analytics

Inthe earlier times organizations focussed on generating relevant data either weekly,monthly or annually but with rising competition and ever-changing stream ofdata, organizations are required to prioritize real-time data streaming inevitabilityto step above the competition in 2019. The live data streaming capabilities helpsthe organizations to analyse the data once it becomes accessible. Real timeanalytics considerably improves the entire process of data analysis and permitsmajor data science industries to know about the data trends the moment theyhappen. Real time analytics and live data streaming technology helps greatly withinthe field of Artificial Intelligence and Machine Learning by accuratelyupdating the user with accurate predictions about the market by efficiently andinstantly analysing any frequent changing patterns within the real-time datastream.

#Second Prediction – Sourceof Innovative Revenue Stream

Sinceindustries are giving a lot of significance to mature and reliable data sets, availabilityand accuracy of data has successively set its roots as a crucial factor for marketprediction. It has also been predicted that in the coming years data will beresponsible for generating revenue by offering data as a service. In which casethe data sets will be used to derive insights and generating recommendationsfor third parties.

#Third Prediction – Increasedrequirement of Cloud and Big Data

Industries are experiencing ever-growing collection of data sets, with the hope to reduce cost and increase efficiency within the data analytics process. Leading to which combination of big Data and cloud Computing have become a perfect combination for the businesses over the industry. It has also been predicted that the global spending on big data solutions via the cloud will grow up to 7.5 times faster than on-premise solutions as it is cost-effectiveness, together with wide availability, and easy accessibility

#Fourth Prediction –Growth in Artificial Intelligence

With the growth and transformation of Artificial Intelligence, IT industry has nearly transformed inside out ever since the commencement of the concept. Industries are adapting and implementing artificial intelligence and its enhancement through big data at a growing rate, which might be inevitable transformation to lay down its initial roots. Also, the growing need of big data for making decisions, Artificial Intelligence as service vendors will be able to provide efficient, low-cost AI-powered tools to specific businesses that have already built out their data sets.

#Fifth Prediction – Emergenceof Ethical Intelligence

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Thereis a heft of data across industries leads to moral questions such as itscapability to take critical decisions where data is not the only elementrequired for concluding.

Thereis a niche for a machine to respond ethically and base its decisions on ethicalbackground, by looking the data with a new perspective. This would require theemergence of Ethical Intelligence implemented within the AI-powered machines tosimulate a closer human-like thinking network. Industries face the rising needfor Ethical Intelligence as a field due to the rapid growth of AI technology.

#Sixth Prediction – Risein Demand for Data Scientists

Since the major players in the technology-based industry have got onto the Artificial Intelligence and Machine Learning movement, there would be a huge requirement for big data to properly utilize said technologies is more of necessity now. This would ultimately lead to a rise in the demand for Data scientist in the coming years.

#Seventh Prediction – Growthin Edge Computing

The growing data collection in organizations, leads to immediate requirement to reduce the lag between the processing and the collection process. This would further emphasise the need for edge computing in the coming years. Edge computing is one of the methods of cloud computing where the servers are brought to the “edge” of the cloud computing barrier. Edge Computing brings the servers that process and collects the said data as close as possible to the users to minimize the lag duration. Edge Computing, can process and collect the data themselves by accessing the closest node to the edge computing that enabled server located nearby ensuring better performance and reducing cloud computing costs.

#Eighth Prediction – Risein Natural Language Processing

Thereis an urgent requirement to retrieve data quickly and efficiently. NaturalLanguage Processing (NLP) is a sub-section of Artificial Intelligence field thathas been used to interpret verbal human language and convert it into amachine-understandable language. NLP permits to skip writing complex commandsthat require technical understanding as a prerequisite and just communicate withthe machine normally. This integration of NLP within big data will permit easyand faster data retrieval speeds.

#Ninth Prediction – Emphasison Protecting Personal and Private

Oftenconsumers are hesitant to share their personal while using an applicationprovided by an organization this ultimately puts the focus of the organizationson protecting user’s data on priority. Given the various incidences of datahacking and court hearings for major technology giants with reference to privacyand security has made privacy mainstream.

#Tenth Prediction –Effort to maintain Clean Data

Theincreasing need for maintaining and collecting data by industries there alsoarises the requirement for data cleaning i.e., generating uncluttered andreliable data. Data scientist spend most of their time cleaning data even beforestarting to build reliable data sets and designing relevant algorithms. Theincrease in the size of clean data there would be a need for new and morereliable cleaning algorithms to build an efficient process. Coming years will experiencedata cleaning as a priority in a quick and efficient manner

BigData market is experiencing a considerable shift in trend with the merger oftwo big data giants – Cloudera and Hortonworks and the coming years are boundto have a upsurge for data scientist and big data professionals.

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