It is estimated that by 2025 there will be 20 billion devices connected to the Internet. Efficient A.I.-based systems will analyze this compendium of data in exabytes (one trillion Gigabytes each) to convert it into useful information for companies regarding the consumption habits of their customers and equipment, such as printers.
Big Data was born with the idea of creating personalized strategies based on this data with the aim of better satisfying consumer needs by making intelligent decisions through A.I. It works thanks to specialized NoSQL databases that store data in a flexible way, making it possible to analyze totally different sources of information.
Every time we enter a website, activate the GPS, send a WhatsApp or connect to a Wi-Fi network we are providing a series of data that will be converted into useful information through Big Data.
This new technology allows a new source of revenue, as companies will know in depth the needs and the way their customers act towards products and services. However, many corporations get stuck in the initial stage of their Big Data projects due to lack of understanding and training, in addition to not knowing how to properly store this data or not selecting the best tool for the analysis and storage of this data. The data and its collection must be differentiated from the activity of processing, analyzing and using the information extracted from the data. Translated to the printing environment, the data would be the toner level indicated by the printer, and the information would be to know if that printer is going to need a new cartridge in a few days and whether or not that cartridge has already been shipped, in order to avoid sending a cartridge months in advance or duplicating its shipment.
Often the service derived from the data collected is generated and managed automatically. This is done for efficiency reasons, especially for recurring activities (such as sending a replacement cartridge). But in other cases, companies need to hire skilled data professionals trained in the field. This would be the case for using data to determine compliance with ESG (Environmental, Social and Governance) objectives.
What is Data Governance? It is the one that ensures that data is consistent and reliable and that it is not misused. A proper Data Governance program includes a steering committee and a group of data stewards.
A good example of how to take advantage of Big Data is the Netflix platform: it monitors what each user watches, analyzes their ratings, the media they use, their geographic location or the date of viewing. With all this information, it adjusts the complete profile of each subscriber.
Thus, due to the digital transformation, companies are faced with a large volume of complex data that cannot be managed with traditional software. Therefore, they need experts who know how to manage the data with advanced A.I. systems and, above all, how to analyze it.
Nubeprint developed the first engine with artificial intelligence (A.I.) for MPS in 2013, improved a year later for management optimization and implemented to this day (Nubeprint invests 30% of its resources in R&D&D). With its MPS-specific Machine Learning, machine learning algorithms use historical data as input to predict new values, always based on A.I.
SOURCE: iebschool.com / Nubeprint