Disaster Recovery Journal Winter 2025

However, many more will opt to use data protection features provided by available storage systems. Some may find the features they need in their existing storage systems, while others may require new, modern storage solutions. “

retention requirements can further exacerbate these costs.

Approach #1: Catalog First Organizations with burgeoning unstructured data stores may struggle to answer basic questions such as: n How much data do they have? n What type of data do they have? n Where is it located? n How fast is it growing? n Is anyone or any application accessing the data after they store it? If so, how frequently does the data get accessed? n Is the data changing? If so, how frequently does the data change? n Is anyone managing the n Can we – and should we – consolidate and centralize the storage and management of the data? n Perhaps most importantly, how much does it cost to store and manage this data? To better answer these organizations should consider utilizing software that catalogs their unstructured data stores. Using this approach, organizations can lay the foundation to answer the questions posed above. A catalog helps to quantify: n The amount of unstructured data under management n Where it is stored n The types of data being questions, data? If so, who or what application manages the data?

2. Restore times become untenable . Restoring petabytes of data can take days, weeks or even longer to complete. Organizations may find their internal business processes cannot wait that long for restorations from backups to be completed. 3. Prioritizing which data to restore first . The time required to restore data represents only half of the challenge. Organizations may also need to prioritize the order of data restorations. This requires understanding the statefulness, or relevance, of unstructured data to properly prioritize data restorations. They should also recognize they may not need to restore all data to resume business operations. To make these determinations, they need to understand which data they must restore first. This becomes difficult due to the amount of unstructured data under management and how its importance may change over time. These three factors should motivate organizations with burgeoning unstructured data stores to tackle data protection differently. Those looking to do so will find at least three approaches to addressing this challenge.

n The data rate of growth n The data change rates n The applications, individuals, or business units that own and manage the data ROT Removal, Chargeback, and Guidance Possessing insight into their unstructured data equips organizations to know how to best manage and protect it. For instance, some organizations may find they should first perform ROT data cleanup. ROT stands for redundant, obsolete, and transitory data. By cataloging their data, they can separate the unneeded and residual junk data from the data they need. They can then remove or delete this ROT data residing in their unstructured data. Cataloging also serves an important secondary purpose for organizations as it positions them to implement chargeback.

Organizations can receive internal resistance when they attempt to delete any data in their unstructured data stores, even junk data. Should they get internal push back, they can justify charging the departments that ask IT to retain their junk data. Cataloging either helps overcome the data deletion objections or justifies charging these departments to store their data. A data catalog serves yet a third purpose. It informs and provides guidance on how to proceed with managing and protecting unstructured data. For example, the catalog may reveal: n Minimal or no data changes occur once data gets stored. n Large amounts of data change rates and/or growth occurring. n Data cannot be consolidated or centralized.

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stored (files, images, backups, videos, etc.)

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