How to Transform Machine Data into a Strategic Asset

Much like “The Blob” in science-fiction movies, machine data is growing rapidly – so much that it may expand beyond organizations’ control. This data comes from applications, servers, sensors, storage, security systems and networks, and IDC has projected that it will comprise 42% of all data by 2020.

As most organizations have realized, storage solutions of the past simply weren’t designed to handle and manage the volume and types of machine data now being generated. And without a solution that can manage machine data, the “blob” threatens to overwhelm IT departments with added costs, hardware resources and demands on IT teams.

Forward-looking organizations can tame machine data and transform it into a strategic asset. By taking the following actions, companies can harness the value of machine data and put it to work:

Determine the requirements of managing machine data

Until a company understands the scope of its problem, it’s impossible to come up with a solution. This is the first thing that has to be determined before moving forward. Each application requires different storage, performance and latency conditions to perform at its peak. Knowing the thresholds and expectations for every application is critical in determining the needs for the overall IT infrastructure to run most efficiently.

Investing in a operational and machine data analytics platform, such as Splunk or the ELK Stack, can help shed light by analyzing the types and volume of data each application is generating. However, one thing to keep in mind about machine data analytics solutions is that they generate a massive amount of data themselves. For example, when Splunk analyzes a terabyte of raw machine data, it can create up to 23 terabytes of data in tiered storage. In fact, recent research has shown that 50% of investments in machine data applications like Splunk go to storage to support the platform.

To keep a machine data analytics solution operating as intended, data has to be constantly shifted among hot, warm and cold tiers, and also swiftly moved between the cloud and on-premises infrastructure in hybrid environments. Just like any other application, IT must take the business requirements of machine data analytics solutions into account when determining the company’s storage needs.

Tap into on-premises and cloud resources

Machine data growth can be so hard to accommodate, companies end up buying more storage than they need, assuming that they will use it eventually. This wastes resources and places excess burden on IT. Before purchasing any extra capacity, it’s crucial that existing storage capacity is optimized. The question becomes, how can this be achieved?

A good first step is extending into a cloud infrastructure as an alternative to overprovisioning on-premises storage. This can even help shrink the footprint of company data centers, making many a CIO very happy.

It’s important to keep in mind, though, that on-premises processes and applications are generating a lot of machine data. Hosting that data in the cloud has the potential to cancel its benefits through high access fees and performance and latency problems. Therefore, while the public cloud should be significant component of any machine data analytics initiative, it the true hybrid IT model that optimally uses the cloud and on-premises infrastructure that will deliver high ROI.

Bring data (and other resources) to the edge

With an edge-computing approach, distributed architectures bring data center resources to the edge of a company’s network, allowing data to be accessed and analyzed in real time. According to Gartner, edge computing can mitigate the latency, performance and cost issues discussed above.

There is no doubt that machine data analytics applications are ideal for an edge-computing model, due to the type of work they do and the amount of data they generate. And, since one of edge computing’s main advantages is how it can provide high-performance storage for large and growing data sets, it’s uniquely suited to maximize the value of applications designed for multimedia content, security intelligence and operational analytics.

Use machine data as a resource across the business

There’s no doubt that the sheer amount of machine data being generated can be a problem for companies not equipped to handle it. It’s important to remember, though, that machine data is generated for a reason: it is very valuable.

Efficiently managing machine data can help a company turn information into insight. For example, machine data analytics can help:

  • Reveal data trends and patterns to help make better business decisions;
  • Eliminate data silos and provide an enterprise-level view into company operations and strategies; and
  • Use real-time monitoring to discover security issues before they become serious problems.

Since it’s constantly growing, machine data seems like it will eat through storage and IT resources – essentially crippling a business. But it doesn’t have to be that way. After all, machine data exists to provide information about an organization’s applications, operations and environment. The key is pairing analytics applications with edge-computing services and forward-thinking data storage solutions to tame such data and make it work for the company, and not the other way around.