An under-reported trend in storage these days is the mounting dissatisfaction with server-centric storage infrastructure as conceived by proprietary server hypervisor vendors and implemented as exclusive software-defined storage stacks.  A few years ago, the hypervisor vendors seized on consumer anger around overpriced “value-add” storage arrays to insert a “new” modality of storage, so-called software-defined storage, into the IT lexicon.  Touted as a solution for everything that ailed storage – and as a way to improve virtual machine performance in the process – SDS and hyper-converged infrastructure did rather well in the market.  However, the downside of creating silo’ed storage behind server hosts was that storage efficiency declined by 10 percent or more on an enterprise-wide basis; companies were realizing less bang for the buck with software-defined storage than with the enterprise storage platforms they were replacing.

Whether you have heard it called software-defined storage, referring to a stack of software used to dedicate an assemblage of commodity storage hardware to a virtualized workload, or hyper-converged infrastructure (HCI), referring to a hardware appliance with a software-defined storage stack and maybe a hypervisor pre-configured and embedded, this “revolutionary” approach to building storage was widely hailed as your best hope for bending the storage cost curve once and for all.  With storage spending accounting for a sizable percentage – often more than 50% — of a medium-to-large organization’s annual IT hardware budget, you probably welcomed the idea of an SDS/HCI solution when the idea surfaced in the trade press, in webinars and at conferences and trade shows a few years ago.

Liquidity is a term you are more likely to hear on a financial news channel than at a technology trade show.  As an investment-related term, liquidity refers the amount of capital available to banks and businesses and to how readily it can be used.  Assets that can be converted quickly to cash (preferably with minimal loss in value) in order to meet immediate and short term obligations are considered “liquid.” When it comes to data storage, liquid storage assets can be viewed as those that can be allocated to virtually any workload at any time without compromising performance, cost-efficiency/manageability, resiliency, or scalability.  High liquidity storage supports any workload operating under any OS, hypervisor, or container technology, accessed via any protocol (network file systems, object storage, block network, etc.), without sacrificing data protection, capacity scaling, or performance optimization.

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.

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.

In the previous blog, we established that there is a growing need to focus on Capacity Utilization Efficiency in order to “bend the cost curve” in storage.  Just balancing data placement across repositories (Capacity Allocation Efficiency) is insufficient to cope with the impact of data growth and generally poor management.  Only by placing data on infrastructure in a deliberative manner that optimizes data access and storage services and costs, can IT pros possibly cope with the coming data deluge anticipated by industry analysts.

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?

To the extent that the trade press covers, meaningfully, the issues around digital information processing and technology, it tends to focus rather narrowly on the latter:  infrastructure.  The latest hardware innovation — the fastest processor, the slickest server, the most robust hyper-converged infrastructure appliance — tends to be the shiny new thing, hogging the coverage.

Interestingly, in the enterprise data center trade shows I have attended recently, the focus was on systemic risk and systemic performance rather than on discrete products or technologies; exactly the opposite of what I’ve read about hypervisor and cloud shows, where the focus has been on faster processors, faster storage (NVMe, 3D NAND) and faster networks (100 GbE).  This may be a reflection of the two communities of practitioners that exist in contemporary IT:  the AppDev folks and the Ops folks.