Predictive Analytics Systems: Why Make The Transition?

Predictive Analytics Systems

Predictive Analytics Systems: Company data is becoming increasingly useful and important, both for marketing issues and the structure’s internal organization. Management must necessarily take care of the need to store and analyze the data themselves, using BI techniques, but to keep up with the times, more than the application of business intelligence is required. Today, predictive analysis systems are literally conquering the business world: let’s now see their advantages, possible uses, and an overview of how they work, after taking a look at the recent history of data storage and use, starting from the 80s.

The first steps of SQL and the evolution toward BI

The urgency of recording and organizing company data of various kinds was already clear around the 1950s: the classic, mammoth paper archives – with some sporadic appearance of information on audio tapes or disks – preserved the historical memory of the company itself, with obvious difficulty in finding the oldest or non-aggregated data stored here. With the beginning of the digital revolution, it was precisely data archiving that received the greatest attention from programmers at the time. In the mid-70s, SQL was invented, a language capable of creating databases of structured data in which it was possible to recall specific information thanks to a query. In short, a real revolution in terms of simplicity and effectiveness of data archiving which, not surprisingly, is ANSI and ISO, a decade after its invention.

But the first problems were not long in coming, one above all, the difficulty of merging all the functions useful for managing the company database into a single application. It is true: in many cases, the industries relied on Microsoft or Oracle, purchasing complete but modular Enterprise Resource Planning, which guaranteed flexibility and uniform data processing, but in many other cases – especially for companies with limited economic capacity, unable to bear the costs of an ERP software signed by Microsoft, Sap or Oracle – we found ourselves having to use different software for every single function, treating the data in a non-homogeneous and therefore unreliable way. Precisely this extreme inhomogeneity, which caused great difficulties in the extraction and coherent use of historical data of the company, created the need to design the so-called “data warehouses,” i.e., virtual warehouses in which the data is not only organized according to operational terms but including also metadata and other previously excluded information, according to precise and well-studied models in advance. Thus, business intelligence began to emerge: an articulated system of methodologies, schemes (and also dedicated professional figures) capable of collecting data relating to each individual corporate production process, organizing them in a coherent manner and allowing maximum usability and availability, allowing to analyze the data collected both in aggregate and in a single calculation.

Predictive analysis systems: a step into the future

As can easily be understood, with the refinement of business intelligence techniques and the increase in available storage resources, the data collected by companies increase hand in hand. The large set of data is called “big data”: these much talked about aggregates of information are nothing more than operational news, metadata, and customer profiling information collected over time by companies. And this huge amount of data is a real gold mine: it allows us to guess the weaknesses of a particular company’s production process or to identify the typical customer more precisely. But that’s not all: predictive analytics, a particular technique that allows unsuspected relationships to be revealed between various aggregates of data, allows for even more particular operations to be carried out, even allowing us to take a look at the future thanks to calculations based on specific behavior patterns, created on the basis of previously collected. With similar techniques, it will be possible to predict the propensity to purchase a product of a particular target or even identify the common characteristics in crimes such as fraud to avoid them more easily in the future thanks to these alarm bells. The application potential of predictive analytics is enormous and not yet fully developed: in the next few years, we will witness a real boom in predictive analytics based on the organizational patterns of contemporary BI.

If you want to implement business intelligence solutions or if you want to delve into the world of predictive analytics in your company, you can contact Next Engineering. The long experience gained in the software development and data database sector has allowed Next to develop broad and in-depth know-how regarding the latest business intelligence and predictive analysis techniques.

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