The Unicorn HRO Blog
Trends in Big Data and Predictive Analytics for 2016Posted Monday, June 13, 2016 by Unicorn HRO
Towards the end of 2015, there were many predictions from experts on what would be the major happenings in Big Data and Predictive Analytics for 2016. Some of the most common predictions included an explosion in the volume of Big Data, implementation of machine learning algorithms on a wide scale, and an increasing number of businesses utilizing cloud technologies. Below are three Big Data and Predictive Analytic trends that have been identified for 2016 so far.
1.Big Data and Cloud Technologies
Cloud and hybrid services are playing an increasingly important role in Big Data. While platforms such as DropBox have proven their longevity in storing and sharing data and information, newer companies such as DigitalOcean focus on the user experience, making it easier and quicker for people to access this increased volume of information. As cloud technologies continue to mature andallow for real time and more complex analytics to be applied to data, more and more companies are using cloud computing to support their big data projects in a cost effective way.
2.Predictive Analytics – Smarter and Streamlined
The rise of Big Data has driven the trend for finding meaningful ways to make sense of the information recorded resulting in an increased need for predictive analytics. Analytic tools are getting simpler to use and more affordable. They are also getting smarter and allow for deeper forecasting. Today, HR managers can access analytics that predict which employees are more likely to reach their targets and why. Programs such as SAS Enterprise and SAP Predictive Analytics can take big data and help companies to better understand their customers’ habits so that they can develop more effective business strategies, as well as plan for the future and anticipate any changes that might emerge. Many companies are now using predictive analytics to design marketing campaigns for their products and improve their operating processes to increase efficiency. There are currently a variety of predictive analytic tools on the market which can suit any business needs, from full suites of analytic tools to outsourcing for specific analytic tasks.
One of the newest ways that Big Data is being analyzed is through machine learning. Machine learning can be defined as algorithms that help computers to learn from previous experience to better predict customer behavior and future trends, consequently improving predictions over time. Programs like Siri by Apple and Cortana by Microsoft are just the beginning, as experts envision a post-app future in which, instead of using buttons and menus on a smartphone, people can acquire information by speaking to an artificially intelligent agent. Machine learning begins with identifying the outcome (“how many sales did I make this month?”) and works backwards, with computer programs being taught to uncover the factors that are driving the outcome. Machine learning can often uncover hidden and complicated interactions between multiple factors and so can greatly improve the accuracy of predictions.
The amount of external data and diversity of data sets available to employers will continue to grow at an alarming pace as technology advances. Companies who apply business intelligence and predictive analytics to Big Data can garner real business value from this information and stay ahead of global competition.