Reinforcing the Record Management Scenario with Text Analytics Tools
Viewing from the current technological standpoint, bigdata appears to be a platform embedded with an enormous pool of structured, unstructured as well as semi-structured information; so vast that it requires state-of-the-art data processing applications. Acting as an integral part of data processing, text analytics tools that extract high quality structured content from unstructured data formats plays a productive role. Sources of the content include the data procured from e-mails, qualitative data, websites and survey responses.
Pinpointing the vastness of the text analytics market, Forrester claims that over 200 firms are currently offering software products for text mining. Deploying an array of advanced techniques, the products effectively extracts a plethora of text data from the content and forms a major part of the total enterprise content. Apart from the traditionally executed statistical analysis and keyword search techniques, the products are widening their knowledge base with highly advanced machine and semantic analysis; transforming itself into an intelligent platform.
As per the Markets and Markets research company, the text analytics market will experience a rapid surge from 2015’s USD 2.65 bn to USD 5.93 bn by the year 2020; with a compound annual growth rate (CAGR) of about 17.5 percent.
Significance of Commercializing Text Analytics in an Organization
When it comes to text analytics technology, some enterprises utilize off-the-shelf services such as customer experience management and social media monitoring systems aimed to collect and analyze a huge pile of unstructured data. By implementing the technology, an enterprise can effectively convert the unstructured data into a structured format, and understand the views of their partners and customers. This initiative allows them to leverage about 80 percent of the entire data that includes people’s views and conversations that act as leading indicators; reducing the sales data reliance in an innovative manner.
Several vendors across the globe are commercializing the text analytics technology. Additionally, apart from English, they also offer many other languages such as Spanish, French, Japanese, German and many more.
Supporting the Process of Sentiment Analysis
Sentiment analysis deals with the aspect of sensing and identifying the customer’s emotion behind the words. This innovative strategy is effectively deployed across social media focused on supporting a variety of applications such as customer service and marketing. Acting as a major factor, text analytics technology plays a vital role in reinforcing the process of sentiment analysis. To initialize this analysis, the technology offers the most trending topics and ideas in the form of text acquired from various sources. Later, negative and positive views of the data are identified using sentiment analysis.
As a smart solution to boost enterprise productivity, sentiment analysis can also be leveraged in the process of Brand Management. Brands often sponsor major events such as charity, Olympics, and local marathons. Poorly managed events triggers negativity and ruins the overall brand reputation. With sentiment analysis enabling the facility of listening to the reviews across events, brands can catch the negative feedbacks and understand the actual cause of a sudden decline in sales.
At an enterprise level, text analytics portrays a wide range of benefits which include:
Unified Framework for Data Integration
Text analysis stages a scenario where a single framework can be used to integrate and analyze the data received from various data sources. It fortifies the process of comparing the information and designing effective strategies for predictive analytics.
When compared to manual coding, analyzing large amounts of data involves considerable cost savings. After designing a stable text analytics framework, the proceeding tasks are almost automated with a minor level of human intervention.
Provides Deep Insights
Apart from being a cheaper option against manual coding, text analytics also boosts the capability to collect and arrange information according to the meaningful and predictive data received from the texts.
Enterprises across the globe should be able to effectively manipulate the collected data. To achieve unprecedented accuracy and interpret better results in text analytics, it is crucial to modify the collected data and meet business specific goals.
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