Today’s digital world generates a lot of data. With the rapid growth of internet-based media and more businesses moving their operations online, it should not come as a surprise that the US alone produces more than 2.5 million gigabytes of data every minute. All of that information has to be stored somewhere, and much of it is flooding into the estimated 1450 exabyte capacity of the world’s data centers.
Organizations with the ability to manage their data effectively can gain valuable insights and adjust their business plans accordingly. Poor data management not only incurs a substantial cost for storing the information, but also has a significant cost in terms of lost opportunities. Having the best data in the world won’t amount to much if it can’t be put to use.
This might sound like a simple proposition, but unfortunately, data is anything but uniform and simple to manage. One of the biggest challenges organizations face in making their data work for them comes in the form of unstructured data.
To understand how “unstructured” data differs from “structured” data, it’s helpful to understand how the earliest forms of data were converted into digital form in the mid-20th century. Accounting and inventory records formed the bulk of early computer data. Since this information was already organized into clearly defined file structures, its digitized form retained a level of uniformity. Data fields were aligned in predefined lengths, field characteristics like text vs. numeric, with specific fields appearing at static locations in each record. This strictly organized format makes structured data very easy to read, search, and understand.
But unstructured data does not have a specific format. It can come in any size, shape, or form, which makes it incredibly difficult to manage and analyze. Structured data is limited in the sense that it can only contain certain types and amounts of information in its defined fields, but unstructured data has no such limitations. While structured data is easy to search using basic algorithms, unstructured data doesn’t follow any predictable pattern that a simple algorithm can process.
Unstructured data can come from anywhere, but much of it takes the form of documents, images, emails, videos, audio, web pages, or social media feeds. Internet of Things (IoT) devices are also becoming a major source of unstructured data as more companies adopt edge computing strategies.
Researchers estimate that roughly 80% of data produced is unstructured. While this data contains information that could prove incredibly valuable to organizations, sifting through it is incredibly difficult. Drawing insights buried in documents, emails, or various types of media files is far too complex a task for a simple algorithm designed to search for field patterns. Unfortunately, unstructured data exists on such a monumental scale that analyzing it is far beyond the human capacity of any organization.
Cognitive, AI-driven technology is one of the most effective tools for drawing valuable information from unstructured data. These programs can interpret, evaluate, make connections, and draw conclusions from this data, which makes it easier to manage and utilize. Without this kind of analysis, it’s difficult to even know what valuable information might be contained within unstructured data. In some instances, that data can pose a substantial security risk. The CRM software giant Salesforce found this out the hard way in 2016 when a board member’s hacked personal email made public an attachment listing acquisition targets and market strategies.
Unfortunately, analyzing unstructured data is incredibly intensive, requiring significant amounts of computing resources beyond the infrastructure capacity of many companies. Even managing the storage and access for unstructured data in the first place presents a major obstacle. Because more and more unstructured data is flooding in each day, storage and computing needs can change quickly. Today’s IT infrastructure solution might not be able to accommodate a company’s future needs, especially if it’s experiencing rapid growth.
Today’s data centers offer a variety of scalable solutions for enterprises looking for better ways to manage their unstructured data. Utilizing cloud-based infrastructure, data centers can set up detailed policies to handle how data is captured, migrated, stored, accessed, and analyzed. The ability to scale up computing power and storage space makes it possible for companies to get the most out of the data they’ve gathered.
For organizations looking to expand their edge computing capacities, finding a data center capable of managing the data needs of IoT devices is critical. Many edge computing architectures store data in a variety of places based upon a strict set of protocols. Some of that data will remain on the edge in the devices themselves or in edge data centers, but some of it will be transferred back to a central server for analysis. In order to know where to send that unstructured data, the network will have to know what to look for and what to prioritize.
Of course, it’s critical to assess the capabilities of any data center before making a commitment to migrate data. The facility should have the cooling, power, and deployment density infrastructure in place to accommodate not just today’s needs, but also for anticipated needs over the next several years.
Unstructured data poses tremendous challenges to organizations as they expand their information gathering and storage capabilities. If they cannot find a sustainable solution for managing and analyzing this data to draw valuable insights from it, they will struggle to find success in an increasingly fast-paced and competitive environment. Fortunately, a reliable data center partner can provide enterprises with the storage and computing power they need to build towards the future.