• Matt Ritter

What is Predictive Analytics?


Predictive analytics aren’t always correct—they’re simply a best guess based on previously established patterns—but they are often reliable, especially if there is a good amount of data to be analyzed.

Before learning about predictive analytics, you may be asking what is analytics? Analytics at its core is simply a detailed examination of something, often data. That data is then interpreted. Predictive analytics simply focuses that interpretation on the future.

If you want to get started with using predictive analytics, it's also critical that you are skilled at proving your point with the data.

What is Predictive Analytics Used for?

Predictive analytics are used in a number of different what-if scenarios, especially in the areas of risk assessment, customer buying trends, and business. For example, predictive analytics can be used with a business’s sales history to determine when customers are most likely to make large purchases or which sales will do the best. It can also be used in a market as a whole to get an idea of when a business could safely try to expand without taking unnecessary risks.

In risk assessment, predictive analytics are often applied to crowd behavior and historical events. Those in this area look at how people react to certain things, especially when in a crowd (i.e., mob mentality). They often focus on reactions made during specific events. Risk assessments can be done for everything from investments to security, and this type of predictive analytics can be found in use in almost every industry.

For example, in a warehouse setting, risk assessment might look at accidents and determine how they could occur, who could be injured, and what methods were in place to prevent them from occurring.

What Methods does Predictive Analytics Use?

Predictive analytics makes use of a number of different techniques often used in all types of digital analytics. This includes:

  • Statistical modeling

  • The 'old school' methods, which are often still the right tools for the job (and still an area of active development)

  • Machine learning

  • Using algorithms to weigh the relative importance of various attributes of the input data

  • Data mining

  • The large scale search for valuable insights through massive datasets. Like strip mining a mountain, this is a systematic effort to find as much value as possible across a wide area.

Why Use Predictive Analytics?

Being able to accurately predict how people, industries, and the economy will react can be incredibly useful. Even if these predictions are not always accurate, they can still be very helpful. When they do accurately predict events, they can help a person or business avoid a devastating mistake or grab an opportunity they may not otherwise take.

Sometimes, following predicative Analytics may seem like taking an unnecessary or even foolish risk. This is especially true when looking at customer trends. The data may show customers are about to jump on a certain trend even though it doesn’t look like it will sell. However, if it does, those who put their faith in the numbers will greatly profit. On the downside, sometimes these risks do fail.

However, predictive analytics really comes down to one thing: it’s a best guess based on the data you have, and you have to ask yourself which is the better option: going with this best guess or taking a shot in the dark? Nearly everyone goes with taking an educated guess, which is why predictive analytics is so popular.

What Next?

Improving your own data skills, and the general analytics skills of your team, requires a deep understanding of both technology and culture. Predicting the future is not for the faint of heart, but you can find a variety of guides on how to improve everything from your SQL skills to keeping your stakeholders happy.

Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed http://www.datasciencecentral.com/profiles/blogs/what-is-predictive-analytics

#analytics #bigdata #targeting #datascience #baseball #Machinelearning #Statisticalmodeling #Datamining

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