As companies seek to leverage big data to make the best possible business decisions, a divide is growing between the types of data organizations must deal with. Big data—defined by tech research firm Gartner as high-volume, high-velocity, and high-variety information assets—includes both structured and unstructured content.
According to a 2014 Business Insider story, more than 90 percent of the data generated by social media is unstructured. “The future of social media turns on being able to make sense of unstructured data—the firehose of texts, posts, tweets, pictures, and videos that even the most powerful computers are unable to classify,” writes Business Insider’s Cooper Smith. “Why is this important? Because social networks have only mined the tip of the iceberg in data terms—information such as likes, dislikes, occupation, location, and age. That leaves a lot of other social activity that hasn’t been parsed yet.”
While structured content is generally considered information that can be neatly stored in company databases, the unstructured kind is more nebulous—and not so easy to pinpoint, categorize, track, or use. As a result, many organizations choose to ignore this more ambiguous side of the data equation, particularly in social media. This is a huge mistake; forward-thinking companies can employ social data to their advantage in the quest to use analytics to eke the most value out of advertising campaigns.
Making sense of unstructured data isn’t easy, but it is coming into clearer focus for a wider range of companies. For example, MapR Technologies’ Michele Nemschoff says the analysis of unstructured data is becoming more common in the business world in A Quick Guide to Structured and Unstructured Data. She defines unstructured data as information that doesn’t yet have a defined data model. “One of the ways many businesses are utilizing this data is to gather brand sentiment,” Nemschoff writes. “Using the right tools, unstructured data can add a depth to data analysis that couldn’t be achieved otherwise.”
In the direct response realm, distilling unstructured data into actionable insights means being able to answer questions such as: What is the action? What is the response? What is the consequence—negative or positive—and how can it be quantified? How can my company generate positive financial results by using social media-generated, unstructured data?
“If you have this wealth of information, demographic detail, customer profiles, and buying behaviors at your fingertips, you’re missing out if you’re not using it to grow market share, sales, and your brand,” says Steven J. Edelstein, a veteran DR professional and acting CEO and COO of HYD for Men, a direct-to-consumer men’s grooming retailer.
According to Booz & Co., unstructured data presents an opportunity that won’t go away anytime soon. In a recent research report, the firm says unstructured data represents the majority of growth in data volume, with 56 percent growth since 2005. Some 68 percent of all unstructured data this year will be generated by consumers, the report adds, and since every user has an average of more than 200 contacts, continued growth in numbers of Internet users will drive even more growth across the social networks.
These numbers point to an opportunity for marketers that figure out how best to leverage the big data at their fingertips. “The data processed can provide companies with insights about customers that can help boost productivity in areas such as marketing, operations, and risk management,” a Booz & Co. report says. “But with the amount of data expanding, technology challenges, organization limitations, and privacy/trust concerns—among other obstacles—businesses must approach analyzing this data with an array of new and emerging technologies.”
Ultimately, it’s important to remember that social media is a mechanism, much like e-commerce, television, or print. Because these and other mechanisms serve as core response drivers for advertising campaigns, it’s important to factor structured and unstructured data into the equation when making campaign decisions. By taking the time to dissect and categorize the unstructured data generated via social media platforms such as Facebook and Twitter, marketers can effectively decipher bits and bytes of information about the words, context, comments, motivations, and more, and use these insights to make inferences and assumptions.