In this article Jeff Catlin of Lexalytics lays out the case for text analytics and its importance to the rising interest in big data. This points to the future where sentiment data coming from social media, much of which is unstructured, can be blended with the structured data from retailers, e.g. POS, inventory, et al, to give insights into brand awareness and acceptance, and sales volume changes.
The link to the article is below and is well worth reading – below is a synopsis of his major points.
Sensors, tweets, emails, web clickstreams, CRM information, supply chain tools – data is flooding into every business, and the businesses that have the most facile processes for divining actionable information from the deluge are going to be the businesses that make the most money.
If it can be counted, it can be analyzed. If it can be analyzed, it can be interpreted. But what type of count or interpretation can be made from a voice recording of a customer service transaction? How are tweets or prose to be interpreted? What type of information can be gleaned from customer product reviews? What happens when those reviews are videos?
Unstructured data is a large part of big data. You can get a lot of information from purely structured data, things like the Click Through Rate (CTR) and conversions from an advertising campaign. But that’s not going to give you a view into what is actually being said. What is the conversation? In order to delve into the dialog, and whether it is a positive one for your business, you have to get into the unstructured side of things.
Big Data for Unstructured Text – Text Analytics
If structured data is big, then unstructured data is huge. The generally accepted maxim is that structured data represents only 20% of the information available to an organization. That means that 80% of all the data is in unstructured form. If businesses are gaining value from analyzing only 20% of their data, then there is a massive potential waiting to be leveraged in the analysis of unstructured data.
Unlocking this potential represents the next Big Data challenge.
The Business Value of Text Analytics
Identifying the “who,” “what,” “when,” “where,” “why,” and the sentiment of the conversation converts unstructured data into structured data, and enables businesses to listen to all of the conversations.
Text analytics can be used to develop a better understanding of the likes, dislikes and motivations of the customer. Changing loyalty program incentives to match customers’ desires can improve customer loyalty and increase sales.
The Coming of Age of Text Analytics – The Next Generation of Big Data
It can seem like a challenge to keep an ear to the ground, listening to conversations about your business, competitors, customers and suppliers. But if you’re not listening, you’ll be surprised when the winds change. While sometimes the surprise is good, often it is not. And when negative conversations take place, the impact to the business can be drastic.