Organizations capitalize on intelligent data management
Intelligent data management concepts are opening new avenues for organizations to make better data-centric decisions and extract more value from their data.
Let's face it, we no longer talk about data backup applications; we now talk about data management platforms. This theme can be confirmed by visiting your favorite data vendor's website. It has never been about just the basic function, process or application. Going back to the simplest system commands -- tape archive, dump and restore, copy -- it was easy to find new use cases for these data sets.
These processes not only make a copy for recovery in the event of data loss, data corruption or system failure, but they also make the data portable and reusable. This makes data management platforms and intelligent data management concepts the "natural evolution" of data protection applications. Research from TechTarget's Enterprise Strategy Group (ESG) has shown that as organizations continue their path to digital transformation, it has become obvious that in one form or another, data is central to all businesses. One in five organizations identified data as their core business.
To utilize the value of data, organizations must be looking at management capabilities through a data-focused lens. To be effective in this new paradigm, whether you are an end user or a vendor, you must be able to follow the data. Elements such as backup, recovery and archive must work in distributed cloud environments where the data lives. Disaster recovery services must include cyber-resilient capabilities, and you have to be able to quickly and easily make trusted data copies for reuse. These must be compliant while supporting the business with critical information or developing new data-centric products and services.
Which of the following statements best describes your organization's perspective on data?
At the heart of any effective data management system is the concept of data classification. You should know and understand your data to manage it intelligently. With any data that is not properly classified, an organization runs a bigger risk of exposing the sensitive data it is unaware of and risks the potential of not extracting the full value of its data for the same reason. ESG research showed that when it comes to data classification, 61% of organizations use both metadata and content-level classification.
Which of the following best describes your organization's approach to data classification?
With any data that is not properly classified, an organization runs a bigger risk of exposing the sensitive data it is unaware of and risks the potential of not extracting the full value of its data.
Metadata classification uses attributes about the data, such as data type, age, access and creation date. Content classification enables deeper inspection of an organization's information. It can help uncover the existence of sensitive data within a file or data set. At a high level, classification sets the groundwork for effective data management.
These capabilities enable organizations to make more intelligent decisions related to data management. This includes decisions related to data placement, data archiving, data reuse and even data deletion for redundant, obsolete or trivial data sets.
In future blogs, I will be exploring other key elements of data management, including the impact that distributed environments, data silos and regulations have on intelligent data management practices -- stay tuned.
