Over the course of the last fifteen years, social media has gone from a novelty to a ubiquitous feature of the modern world. Pew Research survey data on social media usage shows just how universal these platforms have become, with the median American adult reporting that they use at least three of the eight most popular platforms on a regular basis. With so many users involved, the social media data being generated presents enormous opportunities for companies with strategies in place for managing unstructured big data.
Social media users are continuously engaged with various aspects of each platform as well as other users. Every one of these interactions creates a quantifiable data point that can be tracked, segmented, and analyzed for insights. Social media data essentially creates a record of user behavior that allows companies to build engagement strategies that help to promote their business.
One of the key advantages of this data is that there’s simply so much of it. A staggering 2.62 billion people used a social media platform of some kind in 2018. By 2021, that number is expected to exceed three billion. Facebook, by far the most popular social media platform, has a little over two billion active users alone. The data generated by these platforms isn’t just vast; it also provides a real-time glimpse into what users are doing. Rather than waiting for annual or quarterly reports on consumer behavior, companies can follow trends and reactions as they happen.
In its raw form, social media data can include a variety of easy to track metrics:
Of course, many of these data points are meaningless without some sort of context. Early social media strategies focused on what have come to be known as “vanity” metrics, such as follower counts and superficial engagement through likes or shares. The problem with these metrics is that it’s difficult to draw actionable conclusions from them without additional analysis. Simply having a lot of follows on a platform doesn’t automatically translate into business success.
Although it’s often associated with other data-heavy industries such as healthcare, Big data analytics has made it possible for companies to pull meaningful insights from social media performance metrics. While social media provides a lot of structured data pertaining to users (form-based information like name, email address, gender, and so on), the vast majority of it is unstructured, meaning that it doesn’t conform to any particular format and can contain almost any information. Since roughly 80 percent of all data produced is unstructured, this should come as no surprise.
Using powerful algorithms, big data methodologies allow companies to manage this data more effectively. Most social media platforms provide some form of analytics tools that help give context to otherwise scattershot unstructured big data. This is helpful for developing and refining a social media strategy, but it only scratches the surface of the trove of insights hidden in social media data.
Unstructured data encompasses far more than performance and engagement metrics. Shared files, images, videos, audio, comments, and messages are all unstructured data types. When a user posts something on a social media platform, they’re providing a glimpse into their lives. This information is invaluable to organizations seeking to develop products and services that meet the needs and address the pain points of their target audience. In fact, even identifying who a target audience should be is a challenge that could potentially be solved by analyzing user behavior on social media platforms.
But with well over two billion people using social media, that’s a LOT of data to analyze. To make matters worse, a large percentage of unstructured data types is just noise. That’s where big data analytics tools powered by artificial intelligence and machine learning are invaluable for companies. These programs can analyze billions of pieces of information to extract meaningful insights about their customers.
As a notable big data practical example, a year-long study of Twitter usage in the London Underground analyzed the content of tweets at certain times of the day and cross-referenced the results with the platform’s geotagging feature to identify where, when, and what users were tweeting about. The results led the researchers to recommend what types of ads should appear throughout each station’s rotating digital billboards at various times of day to maximize their effectiveness. This is just one big data example showing how social media data can deliver actionable information.
The same data mining techniques can be used to help companies develop better products and services. Consistent demands for new features on a product or complaints about a service can provide guidance for researchers and engineers working hard to create better customer experiences.
Big data poses a challenge for many companies due to the massive amounts of storage and computing power needed to implement powerful analytics programs. Fortunately, colocation data centers possess the connectivity capabilities to help them build hybrid cloud networks that integrate their own servers with the scalable computing power of cloud service platforms. These services allow companies to manage their unstructured data types more effectively, preserving the security and control they need over their own infrastructure while giving them access to powerful analytics tools offered by many cloud-based services.
As social media data becomes more sophisticated, companies will need to improve the ways in which they manage this valuable information. By setting up networks that can facilitate big data analytics, they can get actionable insights faster than ever, allowing them to develop flexible strategies to better meet the needs of their customers. Data centers can help them to build those networks, empowering their digital transformation with interconnection options and innovative hybrid cloud deployments.