It is assumed that many organizations possess over 80 percent of unstructured data in their databases. In other words, the data is not in the form of numbers or code, but simply free text. This textual data is evoked from social media, customers’ comments, call center notes, books, emails, messages and much more which holds an immense value for the organization that can decipher it, explore new information, topics, term relationships, and generate insights. When it comes to text mining at Provalis Research, it is a way to explore this huge pool of resources. Here are the six questions every SME in has about the same.
- What is the distinction between text mining and enterprise search?
There are many differences between text mining and enterprise search. Many companies deliver solutions involving search to assist organization manage unstructured data. But, this is highly unlikely to be enough to do more than identify documents or the headlines. For delving into the deeper insights, text mining is crucial.
- What are some practical examples of business impact?
One of the very best methods to learn how a technique is being implemented and in order to grasp about its potentials, refer to the research. For instance, refer to how Alberta Parks Department utilized text mining to learn the free text information by referring to the surveys of the visitors that the Parks Department has been utilizing since 2002. Automatic coding of data has immensely dispersed the process of analysis and by utilizing the text mining let the analyst of the Parks Department to evoke better insights from the data, get a deeper understanding of customer issues and what the park visitors really desire.
- Is text mining meant only for commercial businesses?
No, it’s not true. Healthcare is another booming sector with a special strong tradition of unstructured data, especially from the medical notes of the patients. For instance, a hospital in Denmark, Hospital Lillebaelt utilized text mining to make sure that doctors have full access to all the relevant information about the patients with no effort required to read every last word of their notes. Also refer in the detail about some of the methods implemented and how they were selected to assist the translation to other settings and country preferences.
- Does text mining need any ‘big data’?
It is always interesting to learn about the personal trials of a particular software. For instance, an author named Gerbard Svolba delineates on using the text mining software for exploring the fifty nine chapters in two books that he has published for the SAS press. He admits to the fact that this is not exactly a massive big data issue, but it does show the potential of the software to cluster and group information, and evoke the analyst to centralize on the areas of interest. It also proves that text mining is not only for big companies but for the smaller organizations as well.
