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Predictive Analytics: Definition and Examples

Predict the future with precision through mathematical formulas? This bold ambition seems to be within the scope of predictive analytics. This data analysis method is a subset of Big Data. It aims to predict future trends, particularly in the marketing, finance, insurance and even health sectors.

The heart of predictive analytics is the models. A person or unit will be measured to predict possible future behaviour. A concrete example would be an insurance policy that anticipates a driver’s risk factors, including factors such as driving experience, age, and health status. From the sum of all these factors, predictive analytics can calculate the potential risk of accidents, and therefore the amount of the insurance premium.

Data mining: the basis of analysis

In practice, the term data mining is often used in place of predictive analytics. Most of time, data mining methods play a vital role in the process of researching predictive analytics approaches. Predictive analytics, however, refines the functioning of data mining and includes additional techniques. Elements of game theory and Machine Learning are taken into account. In addition, are also used in the application of predictive analytics specific analysis methods, which are based on complex algorithms in order to clear a recognizable pattern from all seemingly unrelated contributions from social media or blog articles.

Data mining tries to define large data models using algorithms and mathematical and stochastic methods. Ideally, the knowledge thus acquired can be used to identify and anticipate trends and potential developments.

To better understand the features of predictive analytics, it is helpful to have an overview of common terms used:

  • Regression Analysis: Interactions between various dependent and independent variables are identified. For example, sales will depend on the price of the product and the creditworthiness of customers.
  • Clustering: By segmenting the data, potential customers can be classified according to their income or other similar factors. This is a grouping practice.
  • Association analysis: The objective is to identify the structures whose variables lead to identical results. This makes it possible to draw conclusions about the possible behaviour of the customers and, ideally, to make predictions on the future purchases.

The characteristic of predictive analytics

Recognizing patterns in data sets is the nature ability of the human brain, although the complexity of Big Data analysis far exceeds its capabilities. In fact, there is a parallel between applied data mining structures and neural networks of the human brain, since artificial networks are also able to identify and store patterns as a result of certain sequences. Therefore, data mining is fundamentally related to Artificial Intelligence. In this process, computer programs learn almost independently through introduced basics and acquire new information based on newly developed models.

It is here that we can observe a big difference between data mining and predictive analytics. Conventional data mining usually aims to show structural patterns on existing information and groups. However, the emphasis on near-self-development of calculations that progressively extends beyond the data group (and which is therefore a feature of Artificial Intelligence) plays a decisive role in defining predictive analytics. The already existing algorithms must be combined to bring new conclusions and to be able to predict, for example, behaviours of a target of potential buyers.

Applications of predictive analytics

The introduction of predictive analytics has already proven itself in a wide range of fields and industries. In addition to high-technology companies, the health sector, for example, uses this method to anticipate the likely changes in certain diseases. The energy sector is also an important area of ​​application including the development of smart grids, smart grids. Energy consumption can be estimated based on customer behaviour patterns in order to precisely regulate the required contribution of wind and hydroelectric power.

The maintenance of a machine is also provided by the predictive analyzes. Here, existing data from a running machine is used to predict its future load and wear. The weak points of the production chain can thus be quickly identified and repaired, in order to avoid, for example, a total stopping of this production.

It is better to use predictive analytics when there are lots of different data packages that are very different from each other and as complete as possible. All data packets are then integrated into the analysis, and the more data from various domains, the more accurate the results will be.

Going a step further, we can also perform prescriptive analytics. This method begins where predictive analytics reaches its limits. The goal is to understand behavioural guidelines to try to reproduce them in a targeted way. This procedure is made possible by analytical structures based on complex models and stochastic simulations of the Monte Carlo method. As with predictive analytics, the more known and reliable variables are used in these models, the more relevant the results will be.

 In summary

There are countless examples of predictive analytics operations and applications. The method depends on the quantity and quality of the data. Nevertheless, the algorithms used are more and more finely meshed, which means that the predictions are also more and more precise. Thus, the so-called prescriptive analyzes also evolve in this direction, to be also more and more sure.

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