Pre­dic­tive analytics – de­f­i­n­i­tion and examples

Pre­dict­ing the future ac­cu­rate­ly using math­e­mat­i­cal methods – this ambition is within easy reach thanks to pre­dic­tive analytics. This par­tic­u­lar method of data analysis is a sub­sec­tion of big data analysis. Pre­dic­tive analytics aims at pre­dict­ing coming trends in dis­ci­plines such as science, marketing, finance, and insurance.

The most important element of pre­dic­tive analytics is the so-called predictor. This term stands for a person or entity that is measured to predict possible future behavior. A concrete example would be an insurance policy that predicts potential risk as­sess­ment factors by taking into account a vehicle owner’s driving ex­pe­ri­ence, age, and health. From the sum of these factors, pre­dic­tive analytics can be used to calculate the risk of a possible accident occurring and, therefore, the amount of insurance the driver should pay.

In fact, the term pre­dic­tive analytics is often syn­ony­mous with data mining. It is often the case that data mining methods play an essential role in the de­vel­op­ment process of pre­dic­tive analytics concepts. Pre­dic­tive analytics, however, elab­o­rates on data mining and includes other tech­niques. This is how elements of game theory and automated machine learning also end up playing an important role in this type of analysis. Fur­ther­more, specific analysis methods are used in pre­dic­tive analytics that are based on complex al­go­rithms, which is how rec­og­niz­able patterns are obtained from seemingly unrelated texts on social media or from blog articles.

In order to un­der­stand how pre­dic­tive analytics works, this overview of common terms of big data analysis and data mining may help:

• Re­gres­sion analysis: in­ter­re­la­tions between various dependent and in­de­pen­dent variables are iden­ti­fied. For example, the dis­tri­b­u­tion depends on the product price and the customer’s credit rating.

• Clus­ter­ing: by seg­ment­ing data, for example, potential customers can be sorted by income or similar factors.

• As­so­ci­a­tion analysis: the aim is to identify struc­tures with the variables that lead to identical results. It is then possible to draw con­clu­sions on possible customer behavior and, ideally, to predict future purchases. For example, if a customer is in­ter­est­ed in shoes, they might also want to buy a shoe rack.

Rec­og­niz­ing patterns in data sets reminds us of our brain’s in­ter­pre­tive power although big data analysis far exceeds its abilities in terms of com­plex­i­ty. In fact, there is a parallel between the practical struc­tures of data mining and the neuronic networks of the human brain since the ar­ti­fi­cial network is also capable of iden­ti­fy­ing and storing certain patterns after a few sequences. Therefore, data mining is struc­tural­ly related to AI (ar­ti­fi­cial in­tel­li­gence or machine learning). In this way, computer programs learn by them­selves on the basis of acquired prin­ci­pals and gather new in­for­ma­tion according to the already developed patterns as well as the ones that are still in de­vel­op­ment.

At this point, there is an important dif­fer­ence between data mining and pre­dic­tive analytics. Con­ven­tion­al data mining is mostly aimed at iden­ti­fy­ing struc­tur­al patterns in existing in­for­ma­tion clusters. However, the focus on the au­to­di­dac­tic new de­vel­op­ment of cal­cu­la­tions (which pro­gres­sive­ly extend beyond the database) is a char­ac­ter­is­tic of machine learning – and this plays a role in the de­f­i­n­i­tion of pre­dic­tive analytics. The pre-existing al­go­rithms should combine in­de­pen­dent­ly from the range of data and draw new con­clu­sions in order to make in­de­pen­dent pre­dic­tions about customer behavior, for example.

In­te­grat­ing pre­dic­tive analytics has already proven its worth in a wide range of in­dus­tries. In addition to high-tech sci­en­tif­ic companies, the health care industry also uses this method for pre­dict­ing the pro­gres­sion of diseases. A prominent area of ap­pli­ca­tion is also the energy sector, where the in­tel­li­gent power grid of the future is known as the 'smart grid'. In this case, power con­sump­tion can be predicted using stored customer be­hav­ioral patterns (smart customer data) in order to precisely regulate the required supply of wind and hy­dro­elec­tric power.

So-called pre­dic­tive main­te­nance can be used as an ad­di­tion­al example. In this process, the current data is fed into a con­stant­ly running machine to predict future use and the resulting wear. Weak spots within the pro­duc­tion chain can be iden­ti­fied and rectified quickly in order to prevent a loss in pro­duc­tion.

The best way to use pre­dic­tive analytics is to use a wide range of data packets that are as extensive and pure as possible. All data packets are then in­te­grat­ed into the analysis. The more data is available (and from as many areas as possible) the more precise the result will be. Most companies are turning to syn­er­gis­tic effects by adding pre­dic­tive analytics to their existing business in­tel­li­gence structure. The most popular tools for using pre­dic­tive analytics include:

• Alpine Data Labs
• Alteryx
• Angoss Knowledge STUDIO
• BIRT Analytics
• IBM SPSS Sta­tis­tics and IBM SPSS Modeler
• KXEN Modeler
• Math­e­mat­i­ca
• MATLAB

Pre­scrip­tive analytics can be defined as the next step in data analysis. This method is where pre­dic­tive analytics reaches its obvious limit: using in­for­ma­tion to predict the way things will develop in order to steer the future course of a trend. In other words, envisaged scenarios are easier to implement and at a certain step in the de­vel­op­ment, action can be taken to navigate trends in a different direction. This approach is made possible by an­a­lyt­i­cal struc­tures based on complex models and random MC sim­u­la­tions. Just like with pre­dic­tive analytics, the more com­pre­hen­sive and reliable the variables used to draw the desired data, the more accurate and in­for­ma­tive the results will be.

There are countless examples that show how pre­dic­tive analytics works. How suitable each method is depends on the quantity and quality of the gathered data. However, al­go­rithms are getting more finely meshed, meaning that the pre­dic­tive data is becoming more and more precise. Pre­scrip­tive analytics also benefits from this de­vel­op­ment as being the next step in the future of data analysis.