Question

Discuss five sources of unstructured data and why analysis of these data are important. Use examples to support your answer.

Answer 1

ANSWER::

Sources Of Unstructured Data:-

Source	Examples
Social Media	Facebook,Hike,Google,Instagram,Youtube.
Geo Data	Traffic,Weather,GPS
Sensor	Call Datail Records,Data Records,webblogs,Tranding System.
Digital Streams	Images,Vidio,Audio.
Logs	Clickstream,File Log.
Micro-Blogging	Customer Feedback Streams,Twitter.

why analysis of these data are important :-

• The bulk of data generated today is unstructured data,It is important that organization find ways to manage and analyse it si that they can act on the data and make important business decisions.This helps organizations prosper in highly competitive environments.If this information is ignored, organizations are not using everything that's available to them successful.
• Unstructured Data helps you improve customer experience
• Unstrucured Data offer the key to helping you really get to know your customers.You can come to understand things like what trends they value on social media,what opinions they have and what they want from your brand.

Examples to support your answers.

• Input To Predictive Models:-

  Text analytics -- using natural language processing (NLP) or machine learning -- is being used to structure unstructured text. For example, organizations can extract entities (people, places, or things), themes, or sentiment from call center notes. That information can then be combined with other information about customers to build predictive models. For example, entities, concepts, and themes can be clustered using statistical techniques.

  Additionally, companies can use survey responses verbatim, assigning entities, concepts, and themes as data and using this for prediction without structured data. Some organizations I've spoken with say that these models can outperform models that use only traditional structured data.

    

• Chatbots In Customer Experience:-

• Chatbots have been in the market for a number of years, but the newer ones have a better understanding of language and are more interactive. Here, based on who you are (e.g., whether you have status with the company) and what you asked for (using NLP for text analysis), you will be routed to the right customer representative to answer your specific questions. Other companies use chatbots for personalized shopping that involves understanding what you and people similar to you bought, in addition to what you are searching for. These use cases require smart NLP-based search as well as machine learning.

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