“Big Data” has come a long way since it was dubbed a mere buzzword. Not using the wealth of social, mobile, CRM, internal, and other data warehouses will be slowly but surely putting enterprises out of business in 2016.
But now that big data analytics have become mainstream, what does the future hold? Based off our own feedback combined with other expert opinions like HP and Cisco, here are our 5 predictions.
1) Cloud storage designed for data processing
After analytics and cloud storage going hand in hand for so many years, cloud storage databases are beginning to be designed for analytics itself. The storing, processing, and viewability of data in the cloud is rapidly advancing.
Because of this, we foresee cloud providers offering “data processing” or “storage analytics” services as premium value added bonuses to a basic package. And with these packages, the choice of what technology computes the data now falls onto the shoulder of the cloud provider instead of the enterprise.
2) Shortage of data scientists
Now, anyone in an enterprise can make decisions based off complex statistics like a data scientist. While this is generally a good thing, the demand for experienced data scientists to create the systems that output these statistics is higher than ever.
Because big data is filled with countless variables, the ability to distinguish the important insights from the unimportant is a required skill, which too few people in the US currently have. Even two years from now it is predicted that there will be a 1.5 million shortage of qualified data scientists for companies that need them.
3) NoSQL, Spark, and Hadoop
A somewhat opinionated topic is that of which big data framework will rise and which will fall. What we do know is that NoSQL databases will likely expand in popularity while they slowly replace outdated relational frameworks.
Apache Spark is also seen as a system that will grow in 2016 and will likely replace Map Reduce while working with its more complex counterpart, Hadoop. Built to process data quickly and efficiently, Apache Spark is supposedly able to tap about 80% of the unstructured data within enterprise data stores.
4) Advancement of machine learning in Big Data
Generally speaking, the number of events that require human analysis in Big Data is not realistic. This year, many new technologies like natural language processing API’s will be applied to big data analytics. The purpose of this is to sift through the event and find the most mission critical to be presented for human analysis.
This point is in support of the need for more data scientists. Machine learning capabilities will need to be assisted by data scientists to be truly efficient. With large-scale machine analytics, company efficiency can be elevated to whole new levels.
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