Unstructured Data Analytics (UDA) is one of the newest and hottest subfields in Big Data today. However, it comes with its fair share of misconceptions. This either causes businesses to be too hesitant to buy into this technology or to misinterpret what UDA can do for them. This in turn causes them to fail using it efficiently. UDA is analysis of unorganized data such as a string of text or numbers. So, let us clear up exactly what UDA does and how helpful are they?
Here are some common myths about UDA plus explanations regarding their potential use cases:
It Is Only Textual Data
Data analytics is changing the way we look at data. Data is everywhere, and it’s not just text anymore. Even photographs, videos, facial expressions can all be turned into numbers that tell a story about us. Such stories as our preferences for movies or clothes, how much time we spend on social media sites, or if someone makes us feel happy. These insights may even help companies decide which products to develop next.
Merrill Lynch estimates that over 80 percent of data found today is unstructured. But not all this information is structured as text alone. Unstructured formats include IMS chat reviews, and customer communication. It also includes scientific measurements such as seismographs and more ordinary forms such as body language or tone of voice.
It Is Not Helpful To Companies Without Chat Or Customer Review Data
Companies that don’t rely on customer reviews or chats to understand what their clients are thinking might be lacking service. They might not have enough content to analyze, or simply aren’t in the field of business. However,
any enterprise that communicates stands to benefit from UDA because it can help figure out how employees need support and address company image issues. Sometimes the most important part of repairing company-client relations is starting within your own organization by using UDA on internal emails for example.
It Only Means Sentiment Analysis
A common misconception is that UDA works only on Sentiment Analysis. Sentiment Analysis is a feature that works within advanced UDA by measuring how people feel about something (positively or negatively). It analyzes many different features of the data to construct a big picture including syntactical analysis (to determine importance based on syntax), topic categorization (by theme), and geospatial organization (which uses geography and setting to aid in the interpretation of the data). Advanced UDA platforms work with many other features to construct the big picture of data, not just sentiment analysis.
Taxonomies Are Enough To Conduct Unstructured Data Analytics
Taxonomies are no longer enough to organize today’s data. Taxonomies can sort the information into specific categories like “long” or “short,” but it cannot go further and do what contemporary UDA initiatives do: extract meaning from different types of candy boxes without ever tasting them.
The big data that is available is mostly unstructured data. But that data is still potential revenue for data-driven businesses. Advanced software technologies empowered with Artificial Intelligence (AI) and Natural Language Processing (NLP) can help. The Content Analytics Platform (CAP), developed by Scion Analytics, can do this by quickly rearranging any unstructured textual data into highly usable structured data.