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When I first got into the area of predictive analytics, it was quite simple.  Identify one or two variables, gather some data, and plot a trend line using SPC (Upper and Lower Limits, etc.). This was then used to predict events. Predictive Analytics at its most basic. This I could do.

But things changed. More and more data became available to be analyzed.  Techniques became much more sophisticated, and even though my degree is in Mathematics, you started to need additional education to be able to work with and analyze the data.  The era of the data scientist was born. This represented an evolution from the business or data analyst role.  Data scientists were expected to be able to work with in the fields of mathematics, statistics and computer science. They need the ability to find and interpret rich data sources, manage large amounts of data, merge multiple data sources together, ensure consistency of the data, create visualizations to aid in understanding data, build models using the data, and present and communicate any insights.  Quite a lot of skills needed. Definitely not me, nor a lot of people, as the skills that are required are rarely found in one person, and normally a team of people is needed to perform all the tasks.

Time progressed and visualization tools became easier to use, the need for data constancy became generally understood, and tools became available to manage and analyze large amounts of data. The age of big data was upon us. Everyone now had the capability to generate insights.  However selecting the appropriate variables and generating the model was still a major task. How do we identify what variables to analyze? How do we build a model that can be used for predictions? How can we act on the predictions? And of course how do we modify the model in light of reality? All this still required some of the skills of a data scientist. And having a data scientist on staff was still outside the reach of most companies.

Today things are changing. SAP InfiniteInsight in many cases removes the need for the data scientist. By being able to uncover dependable patterns, and with the ability to automate the process of building sophisticated predictive models, the models can be generated in hours. And being able to easily deploy the models into production; you can embed predictive analytics in your day to day business processes.

So to answer the initial question; no today you do not need to have a PHD to do predictive analytics. What are you waiting for? To paraphrase another Brit. Give predictive analytics a chance.

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