5 Ways to Optimize Your Cloud Storage ROI

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Every day, machine data or data gathered from the Internet of Things (IoT) data is continuously produced by almost every process businesses run, including storage, applications, servers, networks, sensors and security systems. By 2020, IDC projects that more than 40 percent of all data will be machine generated. It’s a giant snowball rolling downhill, and your company’s IT department is standing in its path.

The on-premises storage solutions of recent years weren’t designed to manage this amount of data, nor the speed of its growth. The public cloud should be the perfect solution for IoT data, as it offers almost unlimited storage and can be very cost effective. However, the solution isn’t as easy as it sounds. On-premises applications are often the ones generating machine data, so hosting that data in the cloud can lead to issues like latency and increased access fees. These setbacks threaten to put a serious dent in the value most companies envision they’ll gain from the public cloud.

How, then, can companies avoid being crushed under the weight of machine data growth without over-extending budgets and IT resources? Below are five guidelines to help make sure your company leverages the hybrid cloud world successfully and gets control of its machine data.

1. Know Your Data’s Specific Requirements

The first step to effectively managing machine data is learning exactly what information the current environment is generating. There are many data analytics solutions on the market that can help with this investigation, however, it is important to make sure these applications are running efficiently.

For example, thousands of enterprises use Splunk (NASDAQ: SPLK) to monitor and analyze IT operations and security data. The platform can scrutinize terabytes of data every day, but for every terabyte analyzed, it requires 23 times that in tiered storage. For Splunk to effectively run indexing processes, IT teams must be able to quickly shift data among cold, warm and hot tiers, and maneuver through a hybrid world of on-premises and cloud environments.

There are also open-source options for machine data analytics, including Elastic’s ElasticStack (Elasticsearch, Logstash and Kibana). These solutions take advantage of the cloud and its scalability, while keeping data close to its source. All these applications require high-performance storage and low-latency access to their source systems – organizations that provide these terms are poised to take the biggest advantage of what the solutions have to offer.

2. Avoid Overprovisioning Storage

Before investing in any new storage options for machine data, ensure IT teams are not purchasing more capacity than they need. This sounds obvious, but it’s amazing how many companies overprovision storage because they’re afraid of not having enough capacity. When building a strategy to store and manage machine data and its rapid growth in the next few years, investigate the different ways that current solutions can be improved before expanding the company’s storage footprint.

3. Make Edge Computing a Priority

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When companies move data to the cloud, a common fear is the potential latency and performance issues that cloud infrastructure can invite. Edge computing uses distributed architectures to bring data center resources to the edge of networks, allowing data to be analyzed and used in real time while dramatically reducing latency.

Edge computing is a perfect fit for machine data analytics applications. Because it delivers high-performance storage for rapidly growing data, it’s also effective for operational analytics, multimedia content and financial applications, such as financial trading and capital markets applications. In fact, any company that stores large amounts of data on-premises and needs high performance is a solid candidate for edge computing.

4. Partner with Data management and Cloud Experts

Smart IT teams support their companies’ business-critical priorities while working toward ways to improve the efficiency of core operations. However, the sheer amount of machine data in the enterprise can make it virtually impossible for IT teams to manage on their own. Service providers that specialize in managing and maximizing massive data sets and cloud services can be a strategically beneficial partnership for companies struggling with this issue, as working with them helps free IT teams to do what they do best.

5. Don’t Let Machine Data Go To Waste

Machine data should be a business priority for a reason: it can be very valuable. With the support of analytics applications and high-performance, scalable storage, organizations can discover the insights hidden in their massive stores of information. These insights can help improve all aspects of a business, from optimizing customer experiences and enabling truly informed decisions in real time to drastically cutting data analysis cycles and tightening data security.

Machine data growth isn’t slowing down any time soon, but that doesn’t mean your company has to be buried under its weight. By using these guidelines, you can manage machine data effectively without swamping your IT department or blowing up storage budgets. With machine data under control, your business can use the information for its most valuable purpose: to gain important insights into your company’s operations and vastly improve your bottom line.

About the author: 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.

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