Our work every day confirms that we are passing through and co-creating Data 4.0 at every step. But data by itself does not drive digital transformation, we must do correct data management, risk management, and a guided definition of responsibilities.
And in this process of growth in scale, automation and greater security, it is artificial intelligence (AI) that makes quantitative and qualitative scalability possible.
Ahead of 2021, a survey by the Enterprise Strategy Group (ESG) anticipated that investment in data identification and classification would accelerate this year because data loads, analytics, and machine learning were driving data growth both on-premises. like in the clouds.
Today we see how in a few months this trend is a fact because AI changed the way in which companies approach their IT work, as well as the purchases and architecture of their businesses.
Big Data is the fuel that power the IA and more specifically the machine learning: the ability of machines to see, understand, and interact with the world is growing steadily achieving assimilate and analyze data and finding patterns, insights, and trends almost in real-time.
When we talk about Data Management we understand that we are facing a formula specially designed by each company to manage the data that allows them to make better business decisions. The massive generation of information requires us to implement a CROSS modernization process: intelligent data and processes that are optimized at every step.
This modernization process towards Data 4.0 can be very beneficial when it comes to marketing actions, for example, but it is also a security framework so that companies can operate large volumes of information, making responsible use of that complexity that involves the subject. It is about adding profitability to the resources and investments of the company at the time of disposing and managing the data that is continuously generated.
Artificial intelligence (AI) and machine learning (ML) streamline tasks every day: detection, labeling, mapping, descriptions, and data integration by using clever combinations. All this is part of the enrichment process of that same information that is deepened thanks to data analysis.
Innovation At The Service Of Intelligent Data Management
Simplicity and integration: the system improves the accuracy of matches and also adds the ability to include non-developers and business users in the information comparison process.
Schema Matching: With the implementation of this technology, it is easier to automatically infer and link target schemas with source data. This AI-based schema matching automates data integration by automatically creating target models from source structures, inferring links between columns, and identifying data relationships.
Charts: The system creates metadata insight charts from scratch, extremely useful for simplifying management, applying quality rules, and identifying sensitive information across the data landscape with high precision.
Automation of the data process, verification of the quality of the information, integration of legacy data, and development of own rules for the automated management of data are, in short, the functionalities that provide the greatest value to companies and the reasons why each More and more organizations are turning to artificial intelligence and machine learning as allies in their growth strategy.