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Data Management Moves to the Fore. Part 4: Why Cognitive Data Management?
Posted by Jon Toigo on May 11, 2017
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In previous installments of this blog, we have deconstructed the idea of cognitive data management (CDM) to identify its “moving parts” and to define what each part contributes to a holistic process for managing files and more structured content.

First and foremost, CDM requires a Policy Management Framework that identifies classes of data and specifies their hosting, protection, preservation and privacy requirements of each data class over its useful life.  This component reflects the nature of data, whose access requirements and protection priorities tend to change over time.

Elements of a Cognitive Data Management platform

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Data Management Moves to the Fore. Part 3: Data Management Requires Storage Resource and Services Management Too
Posted by Jon Toigo on April 7, 2017
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Previously, we discussed how data might be classified and segregated so that policies could be developed to place data on infrastructure in a deliberative manner – that is,  in a way that optimizes data access, storage resources and services, and storage costs over the useful life of the data itself.  From the standpoint of cognitive data management, data management policies constitute the instructions or programs that the cognitive engine processes to place and move data on and within infrastructure over time.

Cognitive Data Management Facility

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Data Management Moves to the Fore. Part 1: Sorting Out the Storage Junk Drawer
Posted by Jon Toigo on March 28, 2017
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Most presentations one hears at industry trade shows and conferences have to do, fundamentally, with Capacity Allocation Efficiency (CAE).  CAE seeks to answer a straightforward question:  Given a storage capacity of x petabytes or y exabytes, how will we divvy up space to workload data in a way that reduces the likelihood of a catastrophic “disk full” error?

Essentially, from a CAE perspective, efficiency involves balancing the volume of bits across physical storage repositories in a way that does not leave one container nearly full while another has mostly unused space.  The reason is simple.  As the volume of data grows and the capacity of media (whether disk or flash) increases, a lot of data – with many users — can find its way into a single repository.  In so doing, access to the data can be impaired (a lot of access requests across a few bus connections can introduce latency).  This, in turn, shows up in slower application performance, whether the workload is a database or a virtual machine.

Survey of 2000 company disk storage envitonments

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