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When users ask me if it is realistic to use backed-up data for a data analytics project, I first need to characterize what data analytics means in the context of this conversation. I use the SearchDataManagement definition that states:
Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and [organizations] to make better business decisions and in the sciences to verify or disprove existing models or theories.
On the surface, it would seem that data backup software and data analytics are completely disjointed from one another. After all, backup software is designed to protect data, while data analytics exist as a mechanism for deriving business information from data. Even so, both technologies deal with raw data.
The idea of using backed-up data for data analytics is not as crazy as it might seem. Many backup vendors offer an instant recovery feature that allows businesses to run a virtual machine from backup storage until a backup can be restored. Some vendors take this concept a bit further and allow companies to use backup storage as a virtual development/test lab. Veeam, for example, has a Virtual Lab feature that could be used to make a database available for performing data analytics without fear of disrupting production data.
Although newer backup applications may include features that are conducive to performing data analytics, backup vendors are not in the data analytics business. If backup vendors want to offer data analytics capabilities, they might partner with an analytics company to deliver an analytics offering based on backed-up data, but I just don't see vendors developing such a product by themselves.
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