The increase in digitization and the emergence of multichannel processes has led us into the age of information overload. Organizations rely on this unstructured data to derive actionable insights in terms of business decisions that boost customer satisfaction. In addition to scrutinizing the information provided by the customer, organizations also rely on information collected from various devices. Gartner proclaims, around 80% of data collected is unstructured, assembled from emails, social media feeds and user devices. Here are 10 steps to analyze unstructured data:
• Identifying Data Source: It is important that the data collected is relevant, hence the enterprises should decide on the sources to avoid junk or corrupt data. Online big data development tools can be of great assistance in sorting out the relevant sources from the noise.
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• Managing the Unstructured Data Search: Without a definite end-result, the search could be distracted in various different directions. To focus the search on your specific need, use results in predictive analysis before they go through segmentation and integration in the business’ information store. Transform the data into a structured format with the help of competent business management tools.
• Eliminating Useless Data: It is important to sort through the data and separate the relevant data from the unnecessary information. To avoid confusion and wastage of time, this useless data should be deleted.
• Preparing Data for Storage: In addition to keeping the original, it is beneficial to clean up a copy by deleting noise such as white space, or symbols or converting informal text in strings to formal language.
• Deciding on Technology for Data Stack and Storage: It is essential that enterprises use the latest technology to save and stack the data so that in addition to providing easy access to the data, it possesses an updated data backup and recovery service.
• Keep All Data Until Storage: As mentioned earlier, with potential natural disasters knocking at our doors, it is critical to have an updated data backup recovery system.
• Retrieving Useful Information: Retrieving data is another important step in the analysis of unstructured data. Not only does it recover data, but even retrieves information after converting unstructured information.
• Ontology Evaluation: It is beneficial to discover the relationship between the source of the information and the extracted data. This assists with useful insights in terms of organizing the data.
• Record Statistics: Once the data is cleaned and structured, it is time to organize it into statistical representations. The study of these statistics would help the organizations recognize patterns and derive actionable insights.
• Analyzing the Data: After organizing the unstructured data, it is time to analyze and derive decisions that would affect the course of your company. Indexing the data offers the added benefit of consistent patterns for future utilization.