Text mining predictive methods help organizations enhance the value of unstructured information by deploying insight from text analysis in software applications and business processes. Once textual information is transformed into a set of structured data using text mining (or text analytics) it can be combined with traditional data mining algorithms to generate new insight for sentiment analysis and predictive analytics. Whether it is marketing and competitive intelligence, customer relationship management, social media monitoring, operational risk mitigation or threat discovery, big data is a key element for understanding where you are and where you're going. Text mining predictive methods support organizations in staying competitive. It helps them improve the ability to quickly react to customer feedback, market changes, competitive landscape evolutions, etc.
With so many different possibilities for implementing text analytics in your organization, you'll want to narrow down your use case before evaluating your options. Choose a source of text to analyze. Rich, unstructured customer feedback such as survey verbatims, product reviews, and support tickets often lay untapped within your organization. Choose data that you would read yourself if you had the time and resources to do so. Decide how much to analyze and how often.
There has been a significant growth in the volume and variety of data because of the accumulation of unstructured text data. Companies are now relying on technologies like text analytics and Natural Language Processing (NLP) for making sense of such massively collected data. Text analytics and NLP hold the key to unlocking the business value within these huge data sets. NLP is concerned with making natural language accessible to machines, while text analytics refers to the extraction of useful information from text sources. Today, text analytics and NLP are gradually transforming into a field extremely useful for various business applications, such as competitive analysis, and improving the quality of machine intelligence systems.
Rapid advancements in predictive and prescriptive analytics have seemingly surpassed the overall utility of descriptive analytics. But as we strive to determine what will happen, and to prepare accordingly using technologies like machine learning, it is easy to forget the main value proposition of descriptive analytics which, although less celebrated, continues to endure. Descriptive analytics doesn't reveal what might happen, what should happen, or what your plan of action should be. Instead, it illustrates something much more concrete--what actually did happen and, with the proper analysis, what to do to get the most advantageous outcome out of a situation. Sentiment analysis is perhaps one of the most pervasive use cases for descriptive analytics today.
AI and data analytics will allow the utility firms to optimize the management of customer data and connect with the customers at a deeper level. FREMONT, CA: Public utility companies have failed in maintaining customer experience levels. The inability to live up to the customers' expectations can be partly attributed to the lack of competition in the sector. However, there is another aspect to it too. Public utility companies had conventionally utilized the legacy systems that are slow and lack the efficiency required to meet the current service demands.