Opt for optimized data management
Hybrid cloud environments offer many advantages for businesses. But migrating to the cloud, and then managing different environments operationally, is a complex and time-consuming process. Hence the need to rely on Cloud data management (CDM) to define data management standards and tools.
Hybrid and Multicloud are a reality for professionals. According to an IDC report, 79% of companies are currently investing in a hybrid environment, or plan to do so over the next twelve months.
Despite the urgency "decreed" by management, who wish to rely on innovative solutions, the IT department or team must, on the contrary, progress slowly. Rushing can be costly.
As a company expands, its IT environment becomes a combination of multiple clouds, more or less legacy local systems, and increasingly large and heterogeneous volumes of data.
Heterogeneous data
Adding a public cloud (or clouds) to an on-premises data architecture can take months of planning and preparation. The machine has to be perfectly oiled. Data integration and real-time synchronization play a crucial role in a company's business.
In a highly competitive environment, companies are faced with two parallel challenges. Firstly, to be able to analyze their data in real time. Secondly, to be able to integrate heterogeneous data and adapt it to the business context.
Keeping track of all your data from different sources and giving it a common meaning isn't always (rarely, in fact...) straightforward. Everything, from the logical and physical locations of your data to metadata and other elements, needs to be tracked.
The explosion in data volumes (both structured and unstructured) requires increasingly powerful computing infrastructures, which are becoming difficult to obtain in-house. Organizations' information systems (IS) are showing their limits. Hence the increasing use of the cloud.
But integrating different suppliers in the cloud is still a challenge for many companies. And it's still a project that takes several months to complete. And yet, an integration project should no longer take more than a few weeks, or even a few hours. Most cloud databases can be provisioned in 40 minutes or less, compared with weeks with the old On-Premise methods.
Hence the need for Cloud Data Management (CDM). A CDM consolidates various processes (backup, disaster recovery, archiving, analysis) and optimizes costs (based on actual costs).
In detail, a CDM makes it possible to :
Visualize your data in a single view
As your information becomes increasingly distributed and hosted on a growing number of storage locations, you need a synthetic view.
This 360° vision can help you save time and be more efficient in making the right decisions.
Optimizing storage
The important thing is to be able to align your storage requirements so that you can move production, secondary and tertiary workloads to the best location according to your needs.
Applying consistent policies
When you can have a global view of your data and workflows in the cloud and on-premises, you can also apply consistent management policies across your hybrid environment.
This ensures that the required security policy and governance controls are always in place, wherever data is held. It also enables consolidated reporting for more informed decision-making.
Respecting SLAs
When you use a single platform to manage data across files, applications, databases, hypervisors and clouds, you can achieve SLAs for backup and recovery, data control, workloads and infrastructure.
Automate data archiving
Legacy data and applications can be difficult to preserve and manage. With a comprehensive data management platform in the cloud, you can automate their archiving. It's also possible to move data and applications to the cloud for archiving or replacement (not always obvious for business applications) according to a predefined schedule.
By producing a strategic and structured vision of all data, data management aims to transform it into strategic capital. With the integration of clouds, data management has become indispensable for harnessing and capitalizing on the multitude of information created by companies. It must enable raw data to be transformed into useful information that creates value for the business.
