Top 7 Requirements for Unstructured Data Mobility

Unstructured-Data-Mobility-Blog_Resource_Thumbnail_800x533-002This article has been adapted from its original version on InsideBigData.

Unstructured data is growing everywhere and is the future of AI and ML success. To manage it well, it needs a lifecycle management strategy. File and object data should move to less expensive storage and backup technologies as it ages or declines in value. Unstructured data also needs to be available to move to data lakes and analytics applications.

IT leaders need a strategy to manage unstructured data mobility. Here’s why:

  • Cost management: Data is growing too fast, straining the ability of IT to adequately store and protect it for near and long-term needs. Most enterprises are spending at least 30% of their IT budget on data storage, according to the Komprise 2022 State of Unstructured Data Management Report.
  • Aging data: Organizations need a nuanced approach to data rather than treating it all the same. It’s not sustainable, it’s too expensive, and it’s wasteful. Ensuring easy mobility for the data as it ages and understanding the best options for different data segments is paramount.
  • Data reuse: Another reason why unstructured data mobility is imperative is due to growing AI and machine learning adoption. Once data is no longer in active use, it has the potential for another life in big data analytics programs; AI depends upon large quantities of unstructured data.
  • Technology refresh: Storage architectures typically become obsolete every three to five years. A flexible unstructured data management architecture can meet new business requirements as they come up so you can find, segment and move data to new locations without undue hassle or cost.

Seven new requirements for ongoing unstructured data mobility

Ad hoc strategies to address data mobility no longer work in this complex data environment when needs are in constant flux. IT leaders need a systematic way to manage data movement and meet new requirements, cut costs, be sustainable and support new projects for unstructured data analytics. Here’s what’s involved:

  1. Visibility of data: The ability to look at data across storage silos for trends, patterns, anomalies and to do cost modeling is critical to make smart decisions. Similarly having a unified way to search for data across silos is important to find specific data sets and move them to new locations as needed.
  2. Analysis on data: IT organizations need to understand data across various characteristics to make the right decisions for its management. Age of data and time of last access, file size and type, top data owners, costs, volume of data and data growth rates are some of the top metrics to track.
  3. Cold data tiering: Segment and tier inactive or cold data to low-cost object storage such as AWS Glacier or Azure Blob before you migrate. Too often, organizations will send large data sets to the cloud to save money.  However, they miss out on significant savings because they are lifting and shifting data from one expensive storage location to another.
  4. Understand cloud storage classes: Cloud storage options are always changing and maturing for customers. Choice is great but can be overwhelming. Partner with a cloud data storage expert to help guide these decisions so you can efficiently map the right data sets to the right cloud storage service and create a plan for cloud data management.
  5. Departmental collaboration: Working directly with data owners on strategies is essential to avoid conflicts and to ensure that decisions for data mobility and management are sound.
  6. Policy automation: In large scale data environments with many different stakeholders, shares and directories, you can’t support data lifecycle management manually. Use an unstructured data management solution that allows you to easily create and automate policies to copy, tier, migrate and confine/delete distinct data sets. This will result in more savings, better compliance and the assurance that data is always living in the right place at the right time.
  7. Native access to data: The notion of native access to data simply means that if you move data to a new storage location, such as object storage in the cloud, you can access it there and move it somewhere else without needing to go through your file storage layer. This avoids unnecessary licensing fees and the need to maintain primary storage capacity. Cloud native access is essential for using cloud-based AI and ML services.

Unstructured data is both a liability and an asset. Managing it properly with a plan for long-term data mobility should be one of the top initiatives for enterprise IT today. By doing so, you can get more value from massive unstructured data volumes, be as cost-effective as possible and enable new ways of finding and using data to better serve the broader organization.

Watch this short demo to learn about the different use cases for unstructured data mobility with Komprise.

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