Three Big Data Trends Analysts Can Use in 2016 and Beyond

One of the byproducts of technology’s continued expansion is a high volume of data generated by the web, mobile devices, cloud computing and the Internet of Things (IoT). Converting this “big data” into usable information has created its own side industry, one that businesses can use to drive strategy and better understand customer behavior.

The big data industry requires analysts to stay up to date with the machinery, tools and concepts associated with big data, and how each can be used to grow the field. Let’s explore three trends currently shaping the future of the big data industry:

Big Data Analytics Degrees

Mostly due to lack of know-how, businesses aren’t tapping into the full potential of big data. In fact, most companies only analyze about 12 percent of the emails, text messages, social media, documents or other data-collecting channels available to them (Forrester). Many universities now offer programs for big data analytics degrees to directly acknowledge this skills gap. The programs are designed to administer analytical talent, train and teach the skillsets – such as programming language proficiency, quantitative analysis tool expertise and statistical knowledge – needed to interpret big data. Analysts predict the demand for industry education will only grow, making it essential for universities to adopt analytics-based degree programs.

Predicting Consumer Behaviors

Big data allows businesses to access and extract key insights about their consumer’s behavior. Predictive analytics challenges businesses to take data interpretation a step further by not only looking for patterns and trends, but using them to predict future purchasing habits or actions. In essence, predictive analytics, which is a branch of big data and data mining, allows businesses to make more data-based predictions, optimize processes for better business outcomes and anticipate potential risk.

Another benefit of predictive analytics is the impact it will have on industries such as health informatics. Health informatics uses electronic health record (EHR) systems to solve problems in healthcare such as effectively tracking a patient’s medical history. By documenting records in electronic format, doctors can easily track and assess a patient’s medical history from any certified access port. This allows doctors to make assumptions about a patient’s health using predictive analytics based on documented results.

Cognitive Machine Improvements

A key trend evolving in 2016 is cognitive improvement in machinery. As humans, we crave relationship and identify with brands, ideas and concepts that are relatable and easy to use. We expect technology will adapt to this need by “humanizing” the way machines retain memories and interpret and process information.

Cognitive improvement aims to solve computing errors, yet still predict and improve outcomes as humans would. It also looks to solve human mistakes, such as medical errors or miscalculated analytics reports. A great example of cognitive improvement is IBM’s Watson supercomputer. It’s classified as the leading cognitive machine to answer complex questions using natural language.

The rise of big data mirrors the rise of tech. In 2016, we will start to see trends in big data education, as wells as a shift in data prediction patterns and error solutions. The future is bright for business and analytic intelligence, and it all starts with big data.

Dr. Athanasios Gentimis

Dr. Athanasios (Thanos) Gentimis is an Assistant Professor of Math and Analytics at Florida Polytechnic University. Dr. Gentimis received a Ph.D. in Theoretical Mathematics from the University of Florida, and is knowledgeable in several computer programming/technical languages that include C++, FORTRAN, Python and MATLAB.

Source: Three Big Data Trends Analysts Can Use in 2016 and Beyond

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