As more businesses recognize the variety of data to which they are privy, attention to big data analytics continues to grow. Increasing amounts of data exist in the digital universe. Businesses now realize that scrutinizing this data can yield valuable insights and rapid ROI. But recognizing that this mother lode of data holds value is far easier than analyzing it.In a perfect world, data would be structured and ready for analysis, but in reality, as much as 80% of business data is unstructured, according to estimates from Gartner. Unstructured data can include text data from customer calls, videos, images, emails, blog posts, and social media channels like Facebook, Twitter, and Instagram. Any business that overlooks the potential insights unstructured data holds -- in particular sentiment data from social media channels -- should be reminded of Pew Research's most recent reports. Their research indicates that 74% of online adults use social networking sites. When you consider adults ages 18 through 29 that number jumps to 89%. Armed with insights from unstructured data analysis, business can plan product improvements, better manage pricing or merchandising, and enhance marketing campaigns. For example, a clothing store could connect the dots between social media sentiments and purchasing trends. By doing so, they could gain insight into which styles or products are popular and which are not -- in turn, promoting the "in" designs. Sounds great, right? Before jumping on the analysis bandwagon, it's important to recognize that analyzing unstructured data is no easy task. The Challenge of Analyzing Unstructured Data Unstructured data does not fit into neatly sorted categories, and cannot be easily visualized, searched, or analyzed in the way that structured data can. Still, valuable insights lie buried within those mountains of data. Programming an analytics engine to process structured data is somewhat straightforward; this information is well-defined and relatively clean. But unstructured data can be a bear to handle. Consider, for instance, the content of most tweets. They are often filled with misspellings, grammatical errors, and ambiguous sentiments. The average business is not in a position to analyze this data. Most IT teams are not qualified to handle manipulating and tweaking complicated analytics engines. Moreover, hiring an analytics team or consultants to manage software is a pricey alternative. The challenge for businesses is finding an analytics backend and knowledgeable analysts to wrangle the data, extract insights, present the results, and ultimately return ROI. The Answer: Solution as a Service Analysis of unstructured data should be placed in the hands of the industry's most capable analysts. These data scientists must have a vast business expertise to pinpoint insights and relationships among unstructured data sets that are pertinent to a particular industry or business. With a Solution as a Service (SolaaS) approach to analytics, a single vendor combines a top-of-the-line analytics backend with highly trained analysts. Because a single vendor provides both software and services, the scientists have intimate knowledge of the analytics backend. Additionally, these analysts work in close collaboration with clients, identifying a business's particular goals and user needs so that the best insights are gathered and presented in a useful presentation layer. Additionally, a SolaaS vendor like PolyVista offers flexibility and affordable solutions, and will engage in proof of concepts, one-time, month-to-month, and monthly contracts. Summary The volume of unstructured data ripe for analysis continues to grow. By analyzing this data, businesses gain valuable insights and greater ROI through data-driven decisions. Though it's challenging to find analysts and software capable of handling unstructured data analysis, businesses can hire a vendor with a Solution as a Service (SolaaS) approach to analytics. Through SolaaS, businesses have access to a single vendor's powerful backend and capable analysts. These analysts work in close collaboration with their clients to best wrangle the unstructured data and ensure presentation of targeted, user-focused insights.