See the vXchnge Difference at Our National Colocation Data Centers

Schedule a Tour

Improving Unstructured Data Management With a Data Center

By: Kaylie Gyarmathy on April 25, 2019

Organizations gather quite a bit of data on a daily basis, but managing and analyzing that data to extract information that could be used to inform business decisions is challenging. That’s because about 80 percent of all data produced is considered unstructured, which means that it doesn’t conform to a specific format that would make it easy to categorize and file for analysis.

Unstructured data can come from almost anywhere, including documents, emails, audio files, images, and videos. Collecting and storing all of this data is challenging enough; finding a way to derive insights from it is even more difficult. Fortunately, data centers can provide a number of tools that allow companies to improve their unstructured data management.

Scale Storage Capacity

It may sound like a simplistic solution, but sometimes a company just needs more space to accommodate its data. The combination of cloud computing and improvements in hard drive technology has alleviated many of the concerns experts once had about data capacity. Whether they’re outgrowing their on-premises data solution or looking to back up fallible hard disks, more and more businesses are turning to data centers to accommodate their growing data needs.

Through colocation or by migrating data to cloud-based storage, data centers offer a number of attractive storage options to companies looking to find a more viable long-term solution for unstructured data management. Since some forms of unstructured data include personally identifiable customer information, placing that data in a facility that meets requisite compliance standards is another major benefit. The ability of data centers to deliver consistently high levels of system uptime also ensures that companies will be able to access that data when they need it most.

AI Analysis

Artificial intelligence has powered a revolution in unstructured big data, providing exciting new ways to sort through and analyze information that would have been overwhelming just a few years ago. From sophisticated algorithms to even more complex machine learning tools, companies have an exciting array of options when it comes to mining their unstructured data for actionable business insights.

Unfortunately, AI and machine learning programs are incredibly resource-intensive in terms of computing power. Until quite recently, the cost of either was simply too great for anyone but the largest companies. As data centers begin to offer high-density rack deployments and more efficient cooling systems, however, many small to medium-sized companies now have access to the IT infrastructure need to implement the processing-intensive algorithms capable of sorting through their unstructured data to identify key trends.

Multi-Cloud Deployments

Even if an organization opts for a colocation solution to store and manage their data, they often need to be able to use that data within a cloud environment of some kind. Perhaps they’re using a cloud-based unstructured data analytics program to identify and eliminate “junk” information within their unstructured data, or maybe they need to utilize another cloud platform to clean up data for use in a structured system. In some cases, they can simply drop the data into a public cloud platform, but security and compliance considerations might prevent this. The data might also need to pass between multiple platforms to gather different insights.

For companies with complex unstructured data management needs, a multi-cloud deployment can address many of them within a data center environment. Whether building a single vendor or best-of-breed multi-cloud, data centers can construct a network that offers tremendous flexibility while still providing the security and control of a private network. A good multi-cloud deployment gives companies a variety of options for segmenting and analyzing their unstructured data effectively.

Keep Data at the Edge

A portion of the unstructured data being gathered today is coming from IoT devices, and the amount they gather is only going to increase in the future as usage increases. Intel estimates that autonomous cars alone will produce and consume 40 terabytes of data for every 8 hours of drive time. Finding new strategies for processing unstructured data is critical if companies are going to continue to improve their products and deliver the services customers demand.

Edge computing will help to reduce some of this data pressure. Not every piece of data generated at the edge of a network by IoT devices is valuable. Current cloud computing data management practices essentially hoard every piece of unstructured data available and sort through it afterward for useful information that might be able to inform decision making. But since today’s IoT devices already possess some processing capabilities, they can selectively determine what data might be critical and what data can simply be deleted shortly after creation. This filters out a large percentage of “junk” data, but when combined with an edge data center that can process and sort unstructured data more thoroughly, an effective data management system begins to take shape. By cutting down on the amount of unstructured data being funneled back to larger hyperscale facilities where more intensive big data analytics processing is taking place, edge computing architecture can help companies to manage their data much more efficiently.

Data centers have always played a major role in data management, but with more unstructured data being generated than ever before, they are especially important for today’s companies. A good data center partner provides the resources organizations need to identify valuable, actionable data points within their unstructured data, helping them to make better decisions and adapt quickly to market forces.

Hi there! Speak to an Expert About Your Company's Specific Data Center Needs