Which are best use cases where Big data can be implemented?

Here, we attempting to list few areas where big data can be of great use:

Sentiment Analysis:

Normally Big data comprises of unstructured data from social media like Face Book and Twitter, which may have comments, likes, sentiments expressed, interest areas, products of interest and the like. Analyzing these unstructured data reflects the sentiments of a person through his or her likes, comments, emoticons and other ways of expression.

Customer 360:

These unstructured information when correctly linked to Customer data in relational databases in an organization, it will provide a complete view of their customers, which is referred to us 360 degrees of customer. The customer data in MDMs can be linked to social data to provide an enhanced view on the social and family life of their clients.

Big data + enterprise data provides advanced analytics

Customer’s Call Detail Records (CDR) combined with campaign responses through emails along with customer satisfaction surveys after a complain resolution and social media sentiment can help organizations in analyzing customer behavior and churn.

Click Stream Analytics

Through the navigational behavior of prospects and customers on their enterprise portals, organization can collect the clicks made by them to analyze Page Stickiness (How long the user spent on a particular page), Abandonment (where they left the portal), and  Conversion (Browsers turning into buyers)  rates.

Real-time Fraud detection

Big data also helps in fraud detection at early stage by way of  behavior analysis of certain segment of users through outliers,  segmentation, behavior analysis, financial organization can benefit to prevent frauds at an early stage before losing huge revenues.

 

Internet of Things

Data collected from sensors, GPS, gadgets, mobiles, wearables, smart grids,  and other connected electronic devices can be analysed in the areas of telematics, location analytics and smart retails.

What is the difference between NoSQL and NewSQL?

NoSQL (Not Only SQL) born out of requirements to handle large scale unstructured data without a relational data model. They are designed to provide a flexible data model, not enforcing a rigid or consistent schema to store the data. These ‘document stores’ expand upon the traditional key-value store by replacing the values with JSON-structured documents, each able to contain sub-keys and sub-values, arrays of value, or hierarchies of all of the above. But the draw back for NoSQL is that they are not ACID Compliant. Some examples of NoSQL are MongoDB, DynamoDB, Cassandra, HBase and CouchBase.

To bridge the GAP between RDBMS and NoSQL, New SQL offers relational data model along with ACID consistency in addition to scalability and speed of NoSQL. Some examples are MEMSQL, SAP HANA and NuoDB.

 

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