Predictive Analytics

Predictive Analytics

Predictive analytics provide tools to estimate business data that is unknown or uncertain, or that require a manual or expensive process to obtain it. Beyond the pure analysis of historical information carried out by descriptive analytics, the predictions of data that predictive analytics performs strengthen business decisions.

Through predictive analytics we can:

Anticipate customer demands

Anticipate customer demands across different points of sale, taking into account controllable factors, such as sale prices, work schedules or weather effects.

Detect Fraud

Detect if a financial transaction is likely to have been made as part of a fraud.

Estimate insurance policies

Estimate the adequate insurance policy for a given customer depending on the risk.

Forecast energy consumption

Machine learning computation methods help organisations and countries forecast their future energy consumption.

Catalog customers

Catalog a potential customer according to the impact that a marketing action could have on him.


Automatically find out the most important topics, keywords and ideas within large documentary sources, and group documents by content similarity.

Big Data Classification – How does it work?

Automatic classification is often based on a scientific discipline known as supervised machine learning, which consists in presenting various data from past classifications to a classification system, analyzing this data through statistical learning algorithms and managing to infer the knowledge an expert would give, imitating his work.

The result of this whole learning process is a classification system with the capacity to process large volumes of data to produce valuable information much faster, and at a lower cost, than through manual processes. In addition, this type of system is always improving as it can be fed with new data to readjust its classification processes, thus ensuring that it is kept updated with new business policies or changes in data trends.

The utility of these classification systems is done through the application of business rules on the generated classifications.

For each classification, the system generates an indicator of certainty or score that summarizes the confidence that the system has in its own decision. This allows establishing business rules such as automatically granting those policies classified as low risk with a high score, and deriving the most doubtful to an expert analyst. This allows to significantly reduce the workload of the experts, or to increase the volume of data processed with the same number of personnel.

Big Data Prediction – How does it work?

With similar procedures to those of automatic classification, prediction techniques are often based on supervised automatic learning and the study of time series. These techniques analyze historical data of the metrics to be predicted and, through probability and statistics, relate them to external factors that can influence them. A predictive model is capable of estimating the most likely values that certain metrics will have in the near future.

In a similar way to classification systems, a regression model is an evolving solution, which can be fed back continuously with new data as it is obtained, thus automatically adjusting to changes in unexpected trends or phenomena.

In order to facilitate the integration of the prediction model in the business process, additionally to the predictions, the model can generate variability indices or confidence intervals of these predictions, thus not only reporting the most probable value but its expected volatility.

This allows to implement business rules based on predictions, for example, supplying points of sale with greater stock if a high demand is expected.

Big Data Segmentation – How does it work?

Automatic segmentation has its foundation in a scientific discipline known as unsupervised machine learning. This transforms data into a numerical representation that allows to calculate similarities between elements, and thus detect related groups or elements that otherwise wouldn’t be possible or would require too much time and effort.

The result of a segmentation process is a list of related groups detected in the data, for further study by the company. As the data under study is updated, segmentation can be repeated as many times as needed, discovering changes in the identified groups, such as the appearance of new interests in a customer base, or new themes in a set of documents.

For example, if the desired application is the detection of anomalies, the result of the segmentation process is a detection model that can be deployed to identify in real time this anomalous data or behavior.

Similar to a classification system, the detection model generates a certainty or score value on the degree of abnormality identified, allowing to establish business rules such as, for example, automatically preventing access to a network to a user with a high anomaly score.

Benefits of predictive analytics

  • Process automation: automate data classification, prediction and segmentation, saving time and cutting costs.
  • Higher data treatment capacity: analyze larger volumes of data without increasing the personnel.
  • Higher response time: allow services in real time that wouldn’t be possible if they required manual processing.
  • Business rules optimization: elaborate more solid business rules based on reliable information provided by automatic classification.
  • Reduce uncertainty: reduce business uncertainty by predicting customer demands, sales or other relevant metrics.
  • Greater knowledge: increase business knowledge without the need of manual studies.
  • Dynamic updates: dynamic profile update to detect changes in customers or the market and allow a quick reaction.
  • Real-time detection: Automatic real-time detection of atypical or anomalous cases to prevent security or management problems.

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