Reasons for intelligent and automated data management in the cloud
Posted: Tue Jan 21, 2025 4:23 am
Learn the top 5 reasons to adopt intelligent and automated data management in the cloud with new data warehouse and data lake models
In times of data worship, the strategies adopted by organizations for managing data in the cloud can be a key business differentiator. The manual work that analysts and data management specialists used to do is now unfeasible due to the volume of information generated in digitalized processes.
Today, automation and intelligent data management are working methods that must be incorporated to obtain the greatest possible value from the large flow of bytes that circulate through systems, applications and networks.
HubSpot BLOG
46 % of companies admit to not having the right self employed database to obtain value from their data. In addition, investments in software for data management ( 68% ), machine learning ( 48% ), blockchain ( 44% ) and edge computing ( 42% ) are projected between now and 2030 .
Source: Dun & Bradstreet
In the first of the strategies being addressed, data modernization is gaining momentum, supported by new data warehouse models and the growing use of data lakes in the cloud. Initiatives are also being seen in which data warehouses and data lakes are merged into a single platform, which some call a “lakehouse” .
HubSpot BLOG 2
You may be interested in continuing reading
Data Lakehouses are consolidated as data management in 2022
Modernizing data management in the Cloud
The need to adapt to current data management challenges is widely accepted among IT managers and even among directors and investors. At the business level, it is clearly understood that digitalization opens the doors to maximizing data capture, optimizing the management of various sources and storage environments (both on-premises and in multi-cloud scenarios ) and unleashing innovation in terms of analyzing and leveraging the value of data to feed back into business processes and even to monetize them.
CDO Insights 2023. How to boost data-driven business resilience
Reasons justifying a modernization of the data platform include:
The need to gain flexibility, taking into account aspects of integration and data quality.
The ongoing quest to reduce costs with the help of artificial intelligence and automation.
Business demands for agility through continuous data flows between various applications and systems.
The imperative to increase productivity in software development processes using, once again, automation and artificial intelligence.
The virtue of offering self-service mechanisms for data access and analysis, including the use of natural language.
HubSpot BLOG 2
Keep reading
Take your data from chaos to clarity with cloud data management
To solve all these challenges, there are a set of strategies that are rapidly gaining ground. Automated data capture in the cloud is the first step to feeding not only a cloud data warehouse, but also new data warehouses and data lakes.
This combination of tools allows for the generation of quick reports for immediate use in operations, or for raw data that can be explored under the data science paradigm. Many organizations carry out these tasks in phases, first in a particular area to learn and verify results, before generalizing the practice.
In times of data worship, the strategies adopted by organizations for managing data in the cloud can be a key business differentiator. The manual work that analysts and data management specialists used to do is now unfeasible due to the volume of information generated in digitalized processes.
Today, automation and intelligent data management are working methods that must be incorporated to obtain the greatest possible value from the large flow of bytes that circulate through systems, applications and networks.
HubSpot BLOG
46 % of companies admit to not having the right self employed database to obtain value from their data. In addition, investments in software for data management ( 68% ), machine learning ( 48% ), blockchain ( 44% ) and edge computing ( 42% ) are projected between now and 2030 .
Source: Dun & Bradstreet
In the first of the strategies being addressed, data modernization is gaining momentum, supported by new data warehouse models and the growing use of data lakes in the cloud. Initiatives are also being seen in which data warehouses and data lakes are merged into a single platform, which some call a “lakehouse” .
HubSpot BLOG 2
You may be interested in continuing reading
Data Lakehouses are consolidated as data management in 2022
Modernizing data management in the Cloud
The need to adapt to current data management challenges is widely accepted among IT managers and even among directors and investors. At the business level, it is clearly understood that digitalization opens the doors to maximizing data capture, optimizing the management of various sources and storage environments (both on-premises and in multi-cloud scenarios ) and unleashing innovation in terms of analyzing and leveraging the value of data to feed back into business processes and even to monetize them.
CDO Insights 2023. How to boost data-driven business resilience
Reasons justifying a modernization of the data platform include:
The need to gain flexibility, taking into account aspects of integration and data quality.
The ongoing quest to reduce costs with the help of artificial intelligence and automation.
Business demands for agility through continuous data flows between various applications and systems.
The imperative to increase productivity in software development processes using, once again, automation and artificial intelligence.
The virtue of offering self-service mechanisms for data access and analysis, including the use of natural language.
HubSpot BLOG 2
Keep reading
Take your data from chaos to clarity with cloud data management
To solve all these challenges, there are a set of strategies that are rapidly gaining ground. Automated data capture in the cloud is the first step to feeding not only a cloud data warehouse, but also new data warehouses and data lakes.
This combination of tools allows for the generation of quick reports for immediate use in operations, or for raw data that can be explored under the data science paradigm. Many organizations carry out these tasks in phases, first in a particular area to learn and verify results, before generalizing the practice.