Without question, the plethora of Big Data is more prevalent now than ever before. More data has been created in the past two years than in the entire history of the human race, Forbes reports. By 2020, 1.7 megabytes of new information will be collected every second for each individual on the planet.

Data scientists have labeled this new era of data with “four Vs,” where the volume of data, variety of data sets, velocity of data analysis and veracity or uncertainty of data quality are paving the way for new trends in predictive analytics.

Today, companies are hungrier than ever to utilize data to gain greater insight into their business performance, with a strong emphasis on the variety of data used. From traffic and weather patterns to changes in consumer sentiment and volatility in Asian markets, companies are dealing with more complex problems and are turning to external Big Data to for the answers.

No longer is data isolated in the IT department, and executives are looking to Big Data to provide big answers. They want to discern which external factors will impact the sales and demand of a particular product in the next six months - and by how much. They want to accurately determine which markets their company should exit and which markets are poised for growth. The demand for such answers – in real time, no less – is bringing about three distinct trends in today’s predictive analytics process.  

1. Predictive hypothesis testing

As businesses begin to ask more strategic questions about what’s really driving their performance, executives are understandably demanding proof before executing on this new era of insight. Specifically, businesses want to understand the cause and effect of various sets of data, and know that this analysis can be extrapolated over different periods of time. This new form of analysis, powered by machine learning, is critical to businesses looking to gain a competitive edge.


2. Closing the gap between data and delivery

The hunger for Big Data doesn’t end with merely gathering the right kind of data. From the thousands of data sets available, executives who are looking to better leverage data to solve complex problems need more streamlined ways to glean insight from the variety of data being collected. Currently, companies often deal with this by creating their own analytics platforms, which is very expensive and doesn’t support all facets of the predictive analytics process. Companies are looking for easier ways to close the data gap and are turning toward more streamlined cloud computing in order to speed up the time to insights.

3. Shrinking the barrier between internal and external data

While internal systems have been consolidated for years, the influx of external data is creating unforeseen silos within businesses. In order to increase efficiency and streamline workflows, companies have implemented web-based data transactions, which have created a great divide between internal data and external data. Companies that are spending thousands on a tool to gather a wide variety of external data sets are now faced needing to spend even more on a solution to combine this data into their internal workflows. As such, executives are demanding easier, quicker and less expensive ways to close this barrier.  

Embracing the new predictive analytics process

The diversity of data sets and sheer amount of external data continues to grow with the speed of technology. Global companies certainly recognize its power, but only now are they beginning to find ways to glean real business value from the insights this data can provide.

From implementing more seamless processes for gathering and correlating external data to finding flexible solutions that enable hypothesis testing and analysis of various data sets, companies looking to fully leverage Big Data to solve big problems must embrace this new era of predictive analytics. By incorporating these three trends into business processes, companies can truly see what’s driving their performance and, ultimately, stay ahead of global competition.