Data management in the “multiverse of data madness”

In the Marvel Cinematic Universe, “Multiverse” is now a reality: three Spider-Mana from three different worlds in one movie. On the other hand, here on Earth, managers face a problem: different versions of unstructured data from several decades, a multitude of different (storage) universes – some still useful and some utterly useless – must be managed in a meaningful way.

It’s no surprise that many IT departments prefer to keep their heads in the sand when it comes to managing data in this frenzy of multi-vendor and multi-cloud data – and happily still stores every item that has ever accumulated in the company. But with petabytes of data, mostly duplicate, obsolete, orphaned, or just plain useless, ignoring it is no longer a viable option.

Sascha Hempe, Datadobi’s Regional Sales Manager for DACH, describes how we can reasonably manage mountains of unstructured data today. It also deals with various types of data management solutions available on the market.

It is now common knowledge that unstructured data is spiraling out of control. The amount of data grows – and with it the amount of locations and technologies used in the company. Heterogeneous multi-vendor storage environments and multi-cloud environments are now the rule rather than the exception in most organizations.

The question then arises: how can you manage unstructured mountains of data in a meaningful way in this multi-world data madness? There are many tools out there that promise sound management. On the one hand, memory manufacturers offer their own tools.

At the other end of the spectrum of data management solutions, there are vendors who promise to analyze the multiverse of data and thus enable it to be organized. The promises are full and always promising at first glance.

But how can solutions be compared with each other? And who understands the pros and cons of each different approach? Helps you to categorize the data management solutions you offer first.

Four types of data management solutions
Data management solutions that aim to organize the collection of unstructured data can be roughly divided into four major categories, each with its own advantages and disadvantages throughout the data value chain.

  1. storage and cloud service providers
    The products of cloud storage providers and services are perfect for data storage. Capable of handling any amount of data, they offer basic data backup and a host of other storage-based features. We are all familiar with these products, and typically most organizations have more than one deployed in their data storage environment.

    By their very nature, these providers specialize in good data management as part of their offering. Data management in complex multi-vendor and multi-cloud environments is obviously not on the same level with these solutions. Some vendors claim that their memory management also works in heterogeneous environments.

    But on closer inspection, this is not the case. Almost every solution’s approach begins with storing the data in its own memory first, which completely defeats the purpose of a hybrid environment.

    The software used for this is usually an underfunded and underdeveloped by-product of the hardware or the cloud. Because they are at the core of their vendors’ business – and the limitations of the accompanying software make this clear.

  1. backup provider
    Multi-vendor backup solutions are ideal for protecting your data and managing a large number of copies over a period of time, which are either restored to the original platforms or served directly from your backup device for near-instant recovery.

    Backup solutions provide robust capabilities for managing structured data related to the applications with which they are programmatically integrated. They are less capable of dealing with the unique challenges of unstructured data.

    Again, most vendors assume that data must first be stored on their platform to start the data management process. However, this is not the biggest problem with data management with backup solutions.

    This means that your backup solutions start with exactly what they were made for: backup. In other words, it is a temporary copy of the data and not the actual, ever-changing data itself.

  1. Business data value applications
    At the end of the data value chain are the data value applications that can best extract business value from data. Whether it’s for business intelligence, research, compliance, or any other use of your data.

    Business applications have the advantage in this area. The problem with this genre is that business applications are not designed on the scale of today’s unstructured storage environments.

    They are unable to organize the relevant data to do their job in multi-TB environments with over a billion files, and in the field they need help to identify the right data first.

  1. The latest way to manage unstructured data: vendor-independent solutions
    None of the three data management software genres above appears to be suitable for managing the heterogeneous landscape of unstructured data stores in the enterprise.

    Conversely, as a basic requirement, an ideal solution would of course have to be completely independent of the manufacturer’s commitments and the limitations of storage and cloud vendors and business value applications.

    The good news is that this kind of software has actually been around for a short time. For lack of a better name, it has so far been offered as “vendor neutral data management software”.

    However, while marketers and analysts are still considering a more appropriate name or acronym, it is worth considering the benefits of this new type of unstructured data management software in a multi-vendor data environment and in multiple clouds.

The benefits of vendor-independent data management software
Of course, any professional data management solution should consider the companies’ most important goals when managing unstructured data. The four most important aspirations of companies in general are: reducing the cost of data storage,

Minimizing the risk of data loss, creating more value from data – and as a relatively new corporate goal – reducing CO2 emissions. How exactly can a vendor-independent solution help you achieve these goals?

  • Cost cutting: A vendor-independent solution helps lower costs by facilitating cloud adoption, moving data to cheaper local storage, accelerating the decommissioning of redundant storage, and removing redundant, obsolete and trivial (ROT) data.
  • Risk reduction: It improves the security of unstructured data by enabling IT leaders to see what data they have, why they have it, where it is, and who owns it. Organizations can then take appropriate action by merely backing up their useful unstructured data and eliminating ROT, obsolete, and malicious data.
  • More added value from data: For example, StorageMAP enables organizations to bring data to the right place at the right time so that data-consuming applications can make the most of it.
  • CO2 emission reduction: He supports companies in complying with ESG guidelines and achieving CO2 reduction targets. End users are able to visualize which of their warehouses uses the most energy and how cheaply the warehouses can be powered by renewable energy compared to other sources.

Conclusion: manufacturer-neutral data management is desirable
If you look at the predefined categories of data management solutions, it quickly becomes clear: Organizations that want to meaningfully manage unstructured data in a multi-vendor, multi-cloud environment need vendor-independent software that works in any storage or cloud environment that runs. .

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