The data came to bring us authentic photographs of the state of the companies. This is an aspect that has become reality with their digital transition – one of the great revolutions caused by the Internet and technological evolution. Decisions that used to be made with little information (who doesn’t remember deals made “by instinct”?) are now made and analyzed in detail. Regular performance reports have emerged (which previously took months to complete), rigorous KPIs (indexes to be met) and, above all, a unification of data from different sources. Business status information has become more and better.
With the introduction of artificial intelligence, decisions are becoming faster, smarter and…predictable. All thanks to intelligent algorithms that collect, process and analyze data in order to predict the patterns and consequences of certain behaviors that often escape human analysis. Among these solutions, we will examine in detail the EPS – how it works, how to get the most out of it and what are its practical applications.
Informed decisions based on real data
PSE is a Portuguese company that works with data – what we call today data science. Present on the market since 1994, it specializes in the world of small data comfortable bigdatawith one goal – to build the smart datathat is, intelligent analyzes that adapt to the reality of each company.
What is the big advantage of this smart data? The analysis is not only analytical, but also predictive. The intelligence of the system makes it possible to anticipate scenarios and obtain future estimates of events. In a practical example: a shoe company does not have to keep “doing the math” on the need for more Stock. A predictive analytics system, given the direction of sales, automatically determines – and ideally sends an alert – the need to restock Stockin what sizes, with what colors and models.
The great advantages are always when the predictive analysis is adapted to the reality of each company. And it is in this adaptation that PSE, as a data specialist Science, has worked to improve decision-making processes in companies. The important thing for these systems is the partnership between PSE and IBM, particularly in the use of IBM SPSS Statistics: a technological system that helps improve all research processes through analysis ad hoc (analysis specific to the needs of a client).
Optimize production and fight against fraud
One of the most important areas that intelligent systems with their own learning have revolutionized is the fight against fraud, thanks to systems such as Fraud management. Designed by PSE and based on data sciencethe system helps identify fraud and risk patterns and incorporates components of reports have the ability to cross-validate with other data sources that have historical information, as well as with implemented business rules. In this way, the system makes it possible to detect payment risks or, for example, suspicions of money laundering processes.
Another scenario, as in the example of the shoe company, is that of production optimization. Any company that works with assembly lines seeks a balance between cost reduction and asset availability, i.e. bringing the line to a halt as quickly as possible. Predictive systems can be an important contribution in maintenance, anticipating breakdowns or repair needs, thus trying to significantly reduce the number of unplanned interventions (i.e. unplanned stops in the production line ).
Predicting market potential is already a reality
More than studying the past, solutions for data science predict the future. It’s service PES Group which makes it possible to obtain permanent information on mobility and its characterization in a municipality or region, with information on the socio-demographic profile of the populations and the origin and destination of trips. This is very relevant information, which was previously only estimated and which today is almost a certainty, for applications such as, for example, a company that wants to open a new store to the public and needs to know the busiest streets. This information is also relevant in more macro contexts, such as territorial reorganization studies or transport network planning.
Data has come to shape the world. Today, intelligence is transforming the way they are used. Looking to the future has never been easier.