7 Tech Experts Give us Their Big Data Predictions by 2021

Big data didn’t get so “big” overnight, and it most certainly won’t stop expanding anytime soon.

Today’s research anticipates the digital universe reaching an astonishing 163 zettabytes, or 163 trillion gigabytes of data by 2025. Curious about how large one zettabyte is? It could store roughly 2 billion years worth of music – just enough to get you through a long work week.

There are many concepts and ideas to apply all this data, like creating custom learning models for students or offering more personalized healthcare. There is, however, still a fair amount of uncertainty surrounding the deconstruction and analysis of big data.

Most of the uncertainty stems from the fact that 80 percent of big data is unstructured – like video, audio, social media, and more. Not only is big data difficult (and expensive) to process and analyze, but it’s being generated faster than we can keep up with.

Fortunately, the big data landscape won’t look the same over the next three years. Many businesses are devising plans to adopt big data for future success.

Big data will ultimately unveil new opportunities and efficiencies that could change our everyday lives – and it’s fair to expect some of these changes to break ground by 2021.

So, we asked seven tech experts what their three-year predictions were for big data. Here’s what they had to say:

Data Science on the Rise

Our first big data prediction comes from Harry Dewhirst, President at Blis, a mobile location data solutions provider.

“I recently read that the Harvard Business Review dubbed this role the ‘sexiest job of the 21st century.’ There is no denying that data is going to be the currency that powers our economy moving forward; we are already well down this road. Which means data scientists will continue to drive the future.

It’s critical businesses start planning for the integration of data scientists into their organizational structures now, and perhaps more so for colleges and other educators to provide more opportunities for future workers to explore this field. Data has staying power, it’s not going away any time soon.”

Harry is certainly right. Data science is one of the fastest growing fields today due to its important role in making sense of big data.

As a matter of fact, a report by IBM, titled the Quant Crunch, estimates that up to 2.72 million jobs that require data science skills will be posted by 2020.

Skipper Seabold, Co-Lead of Data Science R&D at Civis Analytics, went on to explain the anatomy of data science jobs by 2021.

“The role ‘data scientist’ will cease to be a specialized position that people hire for. The data science toolbox will become a set of skills that people in various functional roles within an organization are expected to have.

Most data scientists will no longer have to think about distributed systems – Hadoop, Spark, or HPCs. Old technologies, like traditional relational databases, will catch up in performance and capabilities to these technologies, and the need to think about and program for multiple machines connected over a network will be removed by tools available through the big cloud providers.”

Accessible Big Data

Our third big data prediction comes from Sam Underwood, VP of Business Strategy at Futurety, a data analytics and marketing agency.

“By 2021, big data will become much more accessible, and therefore much more useful. A key challenge for many enterprises today is unifying all of this data; by definition, this is a big job!

Building data lakes and other flexible storage environments is a major priority in 2018, and we predict that by 2021, much of this critical data will be housed in systems that are much more accessible by the tools that will use them (visualization, analysis, predictive modeling). This opens up limitless possibilities for every aspect of business operations to be purely data-driven.”

Sam’s insight is spot on. It won’t be enough to just gather and process big data. If data cannot be easily understood by business end-users and decision-makers within companies, it’ll be difficult to find value.

Jeff Houpt, President of DocInfusion and enterprise app developer of over 15 years, reinforces Sam’s sentiment with his own insight.

“I see the landscape for big data evolving from highly technical and expensive to more self-service and on-demand methods where the resources you need spin up automatically and you are only charged for what you use.

Really, in today’s landscape to analyze big data you need massive or expensive infrastructure to capture, catalog, and prepare the data for use. Then to query and analyze the data you need to have the skillset of a very technical programmer/mathematician or data scientist.

I think that there will be platforms and apps that continue to make these tasks easier and more intuitive, and within 3 years we are going to get to a point where you feed the data straight into a single application that will handle all of the remaining details for you – and do it at scale.”

I also think that through the use of artificial intelligence (AI) and machine learning concepts the applications will be able to automatically understand your goals by using knowledge obtained from past users who have done a similar task. This will allow the systems to optimize the data for specific purposes with very little feedback from the user.”

Natural Language Processing

Finding relevant data quickly may be made even quicker through something called natural language processing, a subset of AI which dissects human language in a way machines can understand.

Here’s what KG Charles-Harris, CEO of Quarrio – a conversational interface for enterprises – has to say:

“The most fundamental prediction for big data is that by 2021, information retrieval from big data repositories will be done using natural language and be instantaneous. People will just ask questions in normal language and the system will answer back in ordinary language, with auto-generated charts and graphs when applicable.”

Database as a Service

Ben Bromhead, CTO and Co-Founder of Instaclustr – an open-source big data technology solutions provider – talks about the relation of DBaaS and big data.

“We expect to see Database-as-a-Service (DBaaS) providers really embrace big data analytics solutions over the next three years, as they adapt to serve a fast-growing client need. Enterprise companies have been collecting and storing more and more data, and continue to seek ways to most efficiently sift through that data and make it work for them.

By integrating big data analytics solutions into their platforms, DBaaS providers will not just host and manage data, but also help enterprise clients to better harness it. For example, Elasticsearch is a powerful open source technology we’ve become quite familiar with that enables developers to search and analyze data in real-time.

Expect this and similar technologies that put developers in command of their data to become increasingly prominent within DBaaS repertoires.”

Clean Data

Our last big data prediction comes from Jomel Alos, Online PR Lead of Spiralytics Performance Marketing, a data science-backed marketing agency.

“One of the biggest issues right now for big data is the clutter and incorrect data. Most companies right now have their own cleansing framework or are still developing theirs. Eventually, cleansing and organizing will be automated with the help of various tools. Because big data is not static, these tools are also expected to automate the cleansing process on a regular basis.”

Jomel rounds out these big data predictions with a great point. For quick data-retrieval to occur, big data will need to be cleansed for quality and relevancy.

The U.S. lost an estimated $3.1 trillion due to poor data quality in 2016. This is why the practice of cleansing, or “scrubbing” through processed data is one that is gaining relevance in the world of big data.

Current processes of data cleansing aren’t exactly “time-sensitive.” They require an extraordinary amount of effort on behalf of data scientists. As a matter of fact, 60 percent of data scientists report spending most of their time cleansing data for quality.

Once these processes are able to be automated through the use of AI and machine learning, as mentioned by Jomel, real progress will be made.

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