5 Ways to Mitigate Costs Associated with Machine Data

    When a term like the Internet of Things, or IoT, is used as frequently as it has been in recent years, it’s tempting to write off the concept as a buzzword. Companies know that it ties into their work, but unless they’re involved in a few specific industries, they may not think it directly affects their daily operations.

    This kind of assumption couldn’t be farther from the truth. IoT data encompasses log and machine data and it’s produced constantly — by the servers, networks, security systems and sensors that you might expect, but also by the applications managing enterprise, security and operational analytics. IDC predicts that 42 percent of all data will be machine generated by 2020. As this data grows rapidly, many organizations are discovering that yesterday’s storage systems weren’t built to handle machine data.

    To keep up with machine data growth and avoid costs it traditionally incurs, such as scaling physical hardware and dealing with cloud access fees, companies need to combine on-premises storage performance and availability with the elasticity and economics of the cloud. In this slideshow, Ellen Rubon, CEO and co-founder of ClearSky Data, has identified five ways organizations can make the best of both worlds a reality for their team.

    Ellen Rubin is the CEO and co-founder of ClearSky Data. She is an experienced entrepreneur with a record in leading strategy, market positioning and go-to market efforts for fast-growing companies. ClearSky Data’s global storage network simplifies the entire data lifecycle and delivers enterprise storage as a fully managed service. Most recently, Ellen was co-founder of CloudSwitch, a cloud-enablement software company that was acquired by Verizon in 2011.

    5 Ways to Mitigate Costs Associated with Machine Data - slide 1

    Managing the Explosion of Machine Data

    Click through for five ways organizations can mitigate the costs associated with the exponential growth of machine data, as identified by Ellen Rubin, CEO and co-founder of ClearSky Data.

    5 Ways to Mitigate Costs Associated with Machine Data - slide 2

    Determine Storage Needs

    Determine the storage needs for every enterprise app.

    Machine data analytics applications, such as Splunk and the ELK Stack, analyze massive amounts of machine data – and require a hefty amount of it in tiered storage, as well. Every app requires a different level of storage performance and latency to run smoothly. By tuning into the needs of individual apps and keeping overall goals in sight, companies can increase the ROI of every solution they implement.

    5 Ways to Mitigate Costs Associated with Machine Data - slide 3

    Optimize Existing Infrastructure 

    Optimize existing infrastructure before you purchase more.

    Last year, what did your team spend on data storage? Now, how much capacity did it actually need? Organizations in every industry are over-provisioning storage because they might need it eventually, and it’s wasting critical company resources. As you build a storage strategy to manage machine data growth, consider how you can improve or accelerate existing solutions before increasing your IT footprint.

    5 Ways to Mitigate Costs Associated with Machine Data - slide 4

    Consider Hybrid IT Infrastructure 

    Extend the value of the public cloud with hybrid IT infrastructure.

    Public clouds are almost infinitely scalable, with low costs and inherent flexibility. On paper, they’re a perfect fit for machine data growth. However, many of the applications and processes that tend to generate machine data are located on-premises, and hosting that data in the cloud can introduce access fees and latency issues. While the public cloud can be a major part of any plan to manage machine data, it should be part of a hybrid IT strategy that brings together the cloud and the on-premises data center.

    5 Ways to Mitigate Costs Associated with Machine Data - slide 5

    Use a Metro-Based Approach 

    Cut costs by conquering latency with a metro-based approach.

    Content delivery networks (CDNs) use metro-based hubs to bring data to the edge of networks, enabling users to access information as if it were local. This approach powers streaming media services, such as Netflix, and Gartner recently recommended applying the same tactics to enterprise IT and storage infrastructure.

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    Explore the Possibilities

    Know that machine data doesn’t have to be a burden – for CIOs, it can be a goldmine.

    When machine data analytics applications are matched with high-performance, scalable storage, the headaches disappear – and the possibilities unfold. Organizations can unlock new insights in their data, make informed business decisions in real time, optimize the customer experience, cut down data analysis cycles, tighten security, and ultimately improve the company’s bottom line.

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