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 behaviour. 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 re­cog­nis­able 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 behaviour and, ideally, to predict future purchases. For example, if a customer is in­ter­ested in shoes, they might also want to buy a shoe rack.

Re­cog­nising 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 pro­grammes 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 behaviour, 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 be­ha­vi­our­al 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.