The 3 pillars of data management in the Data 4.0 era
Posted: Wed Jan 22, 2025 4:28 am
Learn the 3 pillars of data management at the dawn of Data 4.0 to achieve scalability, automation and trust.
We have entered the era of Data 4.0. It all happened very quickly, in just a decade: with Data 1.0 we were using data to power specific business applications. Then we evolved to Data 2.0, a stage where data aggregation was already geared towards supporting business processes across the organization. With Data 3.0, data began to drive our digital transformation processes.
Today, at the dawn of Data 4.0, data management takes on a truly strategic dimension.
Additionally, artificial intelligence (AI) and machine learning (ML) for intelligent and automated management of information in Cloud environments is increasingly the starting point for staying competitive and ig database an analytical advantage . In a context where companies modernize their big data analysis in the Cloud, cloud data warehouses , data lakes and lakehouses play a fundamental role.
Of course, this scenario is not without its challenges. One study found that 64% of organizations struggle with data management issues. And in order to truly reap the benefits of cloud data warehouses, data lakes and lakehouses , they need to evolve toward intelligent cloud-native data management .
New call to action
3 pillars for intelligent and automated data management
To achieve successful results in the world of Data 4.0, the three basic pillars of native Cloud data management must be developed, which are the following:
√ Metadata management , which allows you to effectively catalog, discover and understand how data moves through your organization.
√ Data integration , offering support for bulk ingestion of files, databases, and Internet of Things (IoT) streaming data to populate data lakes, optimize, and process them in the Cloud.
√ Data quality , which enables the delivery of reliable data through comprehensive profiling capabilities, rule generation, data dictionary, etc.
Many companies already understand the central role that data plays.
In another study by the same consultancy firm, 67% prioritized the creation of a data management capability that allows them to turn internal data into information by organizing, maintaining and improving data sets and processes. However, according to the report, 45% of organizations were still at a low level (1 and 2) of maturity for data excellence; only 19% had reached the highest level 5.
We have entered the era of Data 4.0. It all happened very quickly, in just a decade: with Data 1.0 we were using data to power specific business applications. Then we evolved to Data 2.0, a stage where data aggregation was already geared towards supporting business processes across the organization. With Data 3.0, data began to drive our digital transformation processes.
Today, at the dawn of Data 4.0, data management takes on a truly strategic dimension.
Additionally, artificial intelligence (AI) and machine learning (ML) for intelligent and automated management of information in Cloud environments is increasingly the starting point for staying competitive and ig database an analytical advantage . In a context where companies modernize their big data analysis in the Cloud, cloud data warehouses , data lakes and lakehouses play a fundamental role.
Of course, this scenario is not without its challenges. One study found that 64% of organizations struggle with data management issues. And in order to truly reap the benefits of cloud data warehouses, data lakes and lakehouses , they need to evolve toward intelligent cloud-native data management .
New call to action
3 pillars for intelligent and automated data management
To achieve successful results in the world of Data 4.0, the three basic pillars of native Cloud data management must be developed, which are the following:
√ Metadata management , which allows you to effectively catalog, discover and understand how data moves through your organization.
√ Data integration , offering support for bulk ingestion of files, databases, and Internet of Things (IoT) streaming data to populate data lakes, optimize, and process them in the Cloud.
√ Data quality , which enables the delivery of reliable data through comprehensive profiling capabilities, rule generation, data dictionary, etc.
Many companies already understand the central role that data plays.
In another study by the same consultancy firm, 67% prioritized the creation of a data management capability that allows them to turn internal data into information by organizing, maintaining and improving data sets and processes. However, according to the report, 45% of organizations were still at a low level (1 and 2) of maturity for data excellence; only 19% had reached the highest level 5.