Emerging Analytics Trends and Forecasts for 2016

By CIOReview | Wednesday, December 30, 2015

FREMONT, CA: The emerging field of data analytics upholds promise not only for IT sector but increasingly for research, strategy, product development and marketing. Data analytics helps companies bring down cost incursions, gain consumer insights and enhance product offerings. With enterprises realizing the importance of Big Data in making informed business decisions, analytics has become the one-stop solution for boosting revenue, solving business problems and decoding consumer behaviour. In the light of the analytics sector’s dynamic evolution, here are five future analytics trends for 2016 as highlighted by Polly Mitchell-Guthrie who leads Advanced Analytics Customer Liaison Group in R&D for SAS Inc.

Internet of Things (IoT) to bring data revolution

According to Gartner, the revenue generated from IoT devices will exceed $300Bn in 2020. This however is just one bit of the bigger picture. As IoT enables network of objects with sensors collect and exchange data and substantially enhance the potential for insights, it is going to have a positive impact across the data universe, encouraging companies to upgrade their tools and processes to derive maximum benefits out of all their new data findings.

Machine learning adoption by enterprises to grow

Machine Learning-as-a-Service (MLaaS) exists as the central point for applications of Big Data analytics. The predictive capabilities of Machine Learning are the means by which the enterprise can make use of all its data. It can enable organizations to combine structured data with unstructured, external data to automate analytics processes making businesses more efficient.

The capability of MLaaS to accelerate the Data Science process ensures that the developers can create better applications more easily to derive near real-time action from analytics. It automates the Data Modeling process by producing models on both present and future data to expedite what otherwise would be a time consuming affair. Mitchell-Guthrie predicts that the adoption of machine learning will grow as large enterprises and practitioners are finding creative ways to use machine learning techniques to select variables for data models.

Big Data to offer opportunities to enrich modeling

Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. In the coming year, Big Data will be providing real value as data modelers will be able to access a wider range of data types such as unstructured data, geospatial data, images and voice to enrich their models. 

Analytics to strengthen cyber security

Research firm Gartner said that Big Data analytics will play a crucial role in detecting crime and security infractions. By 2016, more than 25 percent of global firms will adopt analytics for at least one security and fraud detection use case. The tools of data analysis will evolve further to enable a number of advanced predictive capabilities and automated controls in real time.

Increase in industry demand for academic recruits

As more companies are setting up academic outreach with an explicit interest in research collaborations, industry-university partnership extends beyond collaborative projects to direct hiring of academic alumini. For instance, one of the machine learning researchers, Yann LeCun who worked at Bell Labs, became a professor at NYU, was the founding director of the NYU Center for Data Science, and now leads Artificial Intelligence Research at Facebook. INFORMS, which supports academic-industry collaboration by providing academics a resource of teaching materials related to analytics, will be offering industries a searchable database of analytics programs to facilitate connections and the new Associate Certified Analytics Professional credential to help vet recent graduates.