# 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.

## Data mining – the basis of various analytical surveys

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.

Fact

Data mining aims to find inherent patterns from large amounts of data using mathematical and random methods and algorithms. Trends and potential developments can be read and anticipated from the findings.

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.

## The differentiation of predictive analytics

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.

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## Area of application for predictive analytics

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.

## Conclusion

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.

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