Heterogeneous predictive analytics: solving data management challenges?

The software-defined storage (SDS) market is growing rapidly as users come to realise that this type of  heterogeneous predictive analytics technology allows them to boost performance and support numerous workload applications, while saving extensive amounts of money and time.

By virtualising the storage layer, SDS solves the problem of vendor lock-in, integrating legacy infrastructure and processes with modern and commodity storage, including all-flash, hybrid and hard disk drive arrays.

Siloed analytics won’t fix the problem

Users are already benefiting from SDS platforms that allow seamless migration, recovery, protection and de-duplication of data – on or off the cloud.

But one of the biggest challenges to the momentum of SDS is the growing need to analyse, understand and manage the data being stored.

To address the issue, a number of vendors are adding analytics to their storage solutions.

However, in almost all cases, reporting is limited to monitoring a vendor’s specific silo offerings and does not address the entire storage infrastructure.

This is not a long-term solution because siloed analytics merely add complexity, cost, time and risk at a time when enterprises are working harder than ever to drive down storage costs.

>See also: What’s the block with predictive analytics?

Giving organisations access to analytics and insights across heterogeneous storage environments would enable them to take action, proactively and reactively, as needed.

They would be able to better manage capacity, performance and availability.

Heterogeneous analytics would create real-time insights of higher-value intelligence across the entire environment with less distortion from multiple tools, events and monitoring points.

Intelligence is required

The good news is intelligent, heterogeneous predictive analytical tools are a reality.

It is already possible to use real-time and historical data points for intelligent decisions to automatically adjust functions like performance, capacity usage, cache size, security, optimisation, uptime and service levels.

Advanced analytical tools can also identify trends or patterns to forecast future requirements, detect problems before they result in failures or downtime and convert insights into policy-based actions like changing IO paths, storage tiers or DR strategies.

>See also: Predictive analytics: science or magic wand?

Adding SDS to the mix gives IT organisations the insight and tools to manage storage from a proactive business perspective, instead of a reactive technical perspective.

The ability to analyse, assess and allocate storage, physical or virtual, across an entire infrastructure regardless of which vendor’s equipment or which storage service provider is being used is likely to become critical.

Organisations with access to a truly heterogeneous, multi-vendor tool from a single pane of glass will be able to analyse an entire storage infrastructure, resulting in increased operational efficiencies, better SLA management for key workloads and far superior return on investment compared to segregated single-vendor tools.

Heterogeneous multi-vendor analytics can provide intelligent data services with predictive analytics across any primary or secondary storage hardware, in the cloud or on-premise, to help predict, prevent and troubleshoot storage utilisation problems and bottlenecks before users are affected.

The future is heterogeneous

The entire storage ecosystem can be monitored and predictively managed from a single point of interaction.

This includes physical drives, storage pools, virtual LUNs and connected physical hosts.

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By providing organisations with a holistic, accurate view across the entire storage pool, it generates real-time insight they can take action on.

Predictive analytics can help identify application storage performance bottlenecks and the associated storage causes by providing real-time performance, health and inventory information from the entire storage stack.

Heterogeneous analytics also gives organisations the capability to forecast capacity consumption and predict when they will run out of storage.

It ensures they do not over-provision capacity through ignorance of their consumption.

Organisations can see where storage is under-utilised and re-provision it to key applications to save time and money.

Being able to accurately predict when they need to order storage and how much, enables organisations to take back control of their budgets and forecast accurately and consistently.

Heterogeneous analytics can help organisations overcome the limitations of siloed data analytics tools and gain greater insight into their entire storage infrastructure based on real-time information.

They can easily identify hotspots and bottlenecks to optimise performance and availability; control costs better by predicting capacity utilisation and plan more effectively for growth across the entire storage environment.

Faced with a choice between restrictive, costly and complex siloed analytics tools and heterogeneous analytics that exploit SDS to provide insight into the entire storage infrastructure, it’s not hard to predict which one organisations should opt for in the future.

Sourced by Farid Yavari, VP of Technology at FalconStor

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Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...

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