The transition into the information age has brought a massive proliferation of data, but fortunately, at the same time there has been rapid innovation in tools to analyze this data. In past, the focus of most large scale data analysis solutions has been on metrics that are easily measured – the number of visitors to a webpage, what products visitors purchase, how many ‘likes’ certain posts get. However focusing only on things that are easy to measure can mean missing the most important data.

However, there is a limit to how much text reading can be automated. Having a program go over the comments section on a business’s Facebook page will never be the same reading it personally. However, with the sheer amount of data out there it can no longer be feasible to have a human look at everything, as labour costs add up quickly compared to the cost to run a program. In addition, computer programs can be objective where humans tend to make mistakes – for example, a human reader may pay too much attention to certain passages of text over others, reading their own perceptions and emotional biases into the raw data.

Copyright Issues

It should also be noted that in some situations there could be copyright issues with text mining, if the analysis is being performed on copyrighted data. Just because someone has the rights to read and access a certain piece of text does not mean that they can carry out an automated analysis of it. This is more of a problem in countries that have less permissive copyright laws. Users should research whether there are any relevant copyright issues before running analysis on any non-public data.

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New methodologies in data analysis seek to change that to tap the potential of a much wider selection of data sources. One of these data analysis technique is Text analytics, also known as text mining, a way of transforming raw, unstructured text into structured data, which can then be measured and analyzed scientifically. It seeks to quantify the sprawling masses of text such as product reviews, customer service interactions, or comments on a product page, and turn it into measurable data, indentifying the “who,” “what,” “when,” “where,” “why,” as well as the emotional tone of conversations.

Thus, text analytics can be valuable for everyone from small businesses to multinational corporations. As it can be a complicated field, companies can benefit from outside help in the form of a technology consultant with expertise in this area. A good technology consulting firm can advise on the most appropriate software and help organizations get the most value from its use. Since it is such a new and diverse field, we still do not know all potential uses of text analytics, and as such, businesses could be surprised by innovative ways in which it could help them.