The amount of data shared on the Internet each minute by its 3.29 billion users is mind-boggling. In each minute, 204 million emails are sent, 2,93,000 Facebook statuses are updated, 5,47,000 tweets are tweeted and 2.4 million Google search queries are made, all mostly in the form of text!
Over 80% of all data in the world is unstructured and largely in the form of text and hence an analyst needs to know how to analyse text data. Without this skill he/she is just tapping the surface of the iceberg.
If companies want to understand their customers & business drivers better, they have to analyze this huge volume of text. Companies want to know what their customers are saying about them on social media, product review sites, forums, and via emails. As text is unstructured data, they face a lot of difficulties when analyzing this data. Let’s see what some of those difficulties are.
Why analysing text data is not so straight-forward?
Structured data is in tables of records with fields having fixed meanings. Text of course, is intended for human consumption, not for computers, and hence is unstructured.
From a data perspective, text is very complex. Words have different lengths, people use slang and incorrect grammar when they write, and they use abbreviations unpredictably.
As text is intended for communication between people, context plays a key role. It’s difficult to evaluate any particular word or phrase in a sentence without considering the entire context.
For these reasons, you must process the text data before it can be used as input in a data mining algorithm. The field that deals with gathering, processing and analyzing text data is called text analytics.
Text analytics is about making human communications comprehensible to computers – it is an emerging technology that empowers companies to understand their customers better, and help them determine customer needs, buying trends, etc., by analyzing the data(in the form of text) coming from various sources.
The global text analytics market has a potential to reach $5.93 Billion by 2020, at a CAGR of 17.5%.
The increasing popularity of text analytics is mainly due to the stupendous growth of social media platforms and rapid adoption of cloud technology for data storage. The ability of text analytics to extract meaningful information from unstructured data would benefit companies to gain business intelligence, comprehend market dynamics and gain insight into their customers and also the competitive landscape of the market. FMCG, BFSI, Telecom, Healthcare, and Retail sectors will be the major consumers of text analytics. These industries will use text analytics applications to maintain brand reputation, detect fraud, learn more about their competitors, and for predictive analytics and CRM.
Apart from this, the ability of text analytics to analyze data in real time has influenced its adoption in various businesses.
Text analytics will re-define the way business decisions are taken in the future and will be one of the most important skills to possess for any analyst.
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