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3 Predictive Analytics Trends

Posted by Doctor Dan on Jun 19, 2017 1:01:08 PM

Some of the leading business trends across the world have had a great influence on the transition that has occurred in the market for predictive analytics professionals (PAPs) in the recent times. A phenomenal gesture has been observed which will lead to shaping the business landscapes over the coming years after the inclusion of the variety of major shifts such as the widespread adoption of data-driven initiatives, blending of data science and predictive analytics and the arrival of several professionals entering the analytics market.

Role of Data initiatives in the shaping of business trends

An extensive increase in the rate of quantitative analysis took all the significant trends developed in the so-called "Big Data" space to a distinctive level. Undoubtedly, these reports created a strong foothold for predictive analytics professionals in most of the major organizations ranging from Silicon Valley to Wall Street. It includes organizations such as growing startups, legacy corporations, Forrester among-profits and government teams.

Of late a joint survey was conducted by Forrester among the senior analytics leaders in order to measure the state of customer analytics adoption across the United States. The results were extremely convincing to predictive analytics professions stating that as opposed to the previous years the current outcome showcased a greater proportion of analytics teams in leader’s positions who were once considered as laggards or followers. However, it has to be mentioned that certain proportion of the so-called laggards still exist and there are lot more ground level challenges that have to be addressed which are associated with growing an analytics capability.

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Increasing number of new enthusiasts in the analytics market

Either from one of the trending predictive analytics master's programs or from any other career path, we could sense abundant of quantitative recruiters entering into the analytics market. Owing to the flooding job opportunities in the predictive analytics market, students tend to have a wider space to explore enormous educational opportunities in areas such as master's programs, boot camps, in-house training and flourishing online learning options. When compared to the earlier ratios, the percentage of finding out the predictive analytics talent has seen a rise with respect to the number of inexperienced professionals, junior-end professionals (one or two years of experience) and experienced predictive analytics professionals (four or more years of experience).

Amalgamation of data science and traditional predictive analytics

One should understand the difference between data science and predictive analytics in order for the purpose of further studies regarding the blending of two techniques. As we know, Data science is the study that deals with the management of unstructured data whereas predictive analytics deals with the study of structured data. The common knowledge needed for data science such as monitoring the continuously streaming data and computer skills won't be of much importance in the traditional predictive analytics technique.

In other words, data scientists can also be called as the subsets of PAPs who possess necessary computer skills that will facilitate the transformation of unstructured or continuously streaming data irrespective of its size, format, and source. Entities comprising unstructured data includes audio data, social media web scrapes, video streams, raw log files, long blocks of written language and sensor data. The latest trends in the predictive analytics format lead to a deeper understanding and blending of data science into the medium with the use of platforms such as Spark, Hadoop or Python. In fact, the recent statistics says that there are over 1,100 quantitative professionals corresponding to SAS, R or Python who have determined SAS as the less dominant tool choice among analytic professionals and three-year trend points to open source options picking up steam.

Topics: Data Science