Machine Learning and Predictive Analytics

Running head: MACHINE LEARNING AND PREDICTIVE ANALYTICS
Machine Learning and predictive Analytics on Business System Analysts
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MACHINE LEARNING AND PREDICTIVE ANALYTICS
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Table of Contents
Abstract........................................................................................................................ 3
Research Approach and Methodology................................................................................... 3
Detailed description of the significant trend identified................................................................3
Challenges..................................................................................................................... 6
Opportunity.................................................................................................................... 8
Considerations.............................................................................................................. 11
Impact of trend on business analysts................................................................................... 12
Conclusion................................................................................................................... 16
References................................................................................................................... 18

MACHINE LEARNING AND PREDICTIVE ANALYTICS
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Abstract
Machine learning and predictive analytics are very common in our lives today. It can
affect almost everything we do, including retail and wholesale pricing, consumer habits and
behavior, marketing, entertainment, medicine, logistics, gaming, AI speech recognition, AI
image recognition, self-driving cars and robots. There are many other things. However, being a
new mode of doing things, system analysts may experience some challenges. Understanding the
implication of predictive analytics on system analysts could help companies prepare
appropriately in order to embrace the concept of predictive analytics. The paper will explore the
issue of predictive analytics and its impacts on system data analysts.
Research Approach and Methodology
To achieve the goal of the paper, case studies and the findings from major research companies
such as Gartner and KPMG on how the companies are implementing machine learning and
predictive analytics would be reviewed. The paper would cover such aspects as the current trend
of the issue, the challenges, opportunities, and the impact of the trend on data analysts
Detailed description of the significant trend identified
Business analysis has evolved from static reports telling what happened to interactive
dashboards that help you dig deeper into data and try to understand why this happened. New
sources of big data, including the Internet of things devices, are pushing businesses to move from
passive analytics - when we look at a period in the past and look for trends, or check once a day
for problems - to active analytics that can warn of something in advance and allowing you to
create dashboards with real-time updates. This helps to make better use of operational data,
which is much more useful if it was received “just now”, while conditions have not changed yet.

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Many companies are interested in such active analytics, which allows you to keep abreast of the
pulse of your business. But even dashboards show only what has already happened.
For this reason, various areas of in-depth analytics, including predictive, are developing
most rapidly. According to a Gartner report, by 2019, in order to maintain their competitiveness,
more than half of large organizations around the world will use in-depth analytics techniques
(and algorithms based on them). The figure below summarizes the findings from the report.
Figure 1: Trends in Analytics and Business Intelligence
Source (Gartner 2019).