Research firm IDC predicts that, over the next five years, more than 80% of the data collected by organizations will be unstructured data, and that will only continue to grow 40-50% per year for most enterprises. With the sheer volume of unstructured data yet to be created and used in the years ahead, it’s safe to say that the way organizations manage their data will need to evolve. IDC’s Research VP of Infrastructure Systems, Platforms and Technologies Group Eric Burgener authored an IDC Analyst Brief, sponsored by Datadobi, titled “The Data Mobility Engine as the Foundation for an Efficient Data Management Strategy.” In the analyst brief, Burgener urges organizations to implement a comprehensive data management strategy to confront this increasing influx of data, noting that a data mobility engine provides the foundation for an effective data management strategy and can drive significant benefits for the hybrid multi-cloud enterprise. “A good [data management] strategy takes into account not only the heterogeneity of storage in most enterprises but also a number of other areas, including on-and off-premises deployment models, application availability, data integrity, security, compliance and regulatory needs, efficient resource utilization and the fact that more than 80% of the data created over the next five years will be unstructured (i.e., file and object-based),” writes Burgener. In his analysis, Burgener outlines the five main components of an effective data mobility engine, including the following:
1) Vendor-neutral interoperability
The data mobility engine must focus on data, not systems, and be able to move data between different types of systems as well as cloud targets. Both file (NFS, SMB) and object (S3) access methods should be supported, preferably in a multiprotocol manner to support efficient capacity utilization when data must be shared across different types of applications.
2) Insights and intelligence
The data mobility engine must provide visibility into data metrics, access patterns, and usage activities that can provide the basis for classification, and this visibility must be comprehensive. It should also include AI-driven intelligence that can analyze these metrics to make policy recommendations that drive an effective data management strategy around storage location, data protection, security, compliance, migration and, ultimately, storage cost reduction. With more complete metrics, data residency can be managed to ensure that data is kept in the “best” location (given corporate objectives) and obsolete data is identified and deleted.
3) Orchestration and automation
With the complexity of today’s multilocation IT infrastructures, monitoring data usage and compliance and managing classification and data migration manually are risky propositions. Automation improves the speed and reliability of operations while improving administrative productivity, increasing the span of administrative control to lower costs.
4) Scan-optimize-copy capabilities
All of these operations must be comprehensive in nature. The scan provides visibility around data and its usage, collecting the metrics needed to search and intelligently manage data. Delta differentials, compression, and other storage efficiency technologies maximize resource utilization when moving and storing data, allowing operations to be completed as quickly as possible. The copy includes replication capabilities, which can further assist in optimizing data migration to create the most effective data placement strategy.
5) Integrity enforcement
The data mobility engine must support data integrity during all operations. File and object-level verification must go beyond just TCP checksums to be able to catch and correct silent data corruption using “before” and “after” hash digest comparisons, chain of custody, and advanced integrity protection (regular inspection of destination content to detect possible changes conflicting with the source).
While more companies are reassessing their unstructured data storage needs to keep up with the increasing amounts of human and machine-generated data, most enterprises are searching for a viable solution when it comes to executing a comprehensive data management strategy, Burgener’s report reveals.
Over the last several years at Datadobi, we’ve had more and more IT leaders come to us with concerns around data classification, data visibility, and organization-wide data accessibility, as well as how to handle aging data and the high costs that result from a fragmented data management strategy.
These IT decision-makers understand that the future is changing for their data—with many of them already storing petabytes of data that will continue to grow in the years ahead—but they’re hesitant to make changes to how that data is stored and protected. Their on-prem solution is a “comfort zone,” so to speak, and it’s intimidating to think about moving all of those assets to a new platform.
As data mobility experts, we know that enterprise data mobility, circulation, and protection are not only achievable but also a necessity for today’s businesses. Traditional IT infrastructure and manually oriented data management practices won’t be able to handle this amount of data going forward In short, Datadobi’s solutions lend organizations significantly greater visibility into their data so that they can better manage their digital assets, place their data in the right location depending on tangible metrics and understand critical aspects of their data, such as aging, level of protection—which Burgener relates to “hot,” “warm,” or “cold” data—in order to optimize cost and security policies.
Finally, Burgener states in his report that “the benefits of an effective data management strategy include reduced IT costs, easier data sharing, better security, less legal exposure, and an improved ability to demonstrate governance and regulatory compliance.”
You can read the IDC Analyst Brief, “The Data Mobility Engine as the Foundation for an Efficient Data Management Strategy” in full here: https://idcdocserv.com/US48881822.
