Data blending means combining data from multiple database sources into a single worksheet or table of a given solution in a relatively quick and straightforward way for discovering pattern between data. Big Data blending is gaining importance in businesses as data blending allows uncommon types of data to coexist in a model. New and better strategic decisions can be made by bringing together unusual feeds into one repository. Data blending and data joining are not similar and have significant differences. Joining involves combining data from the same source whereas blending involves combining data from different sources.
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In modern world, analytics plays a significant role and therefore various organizations develop cohesive analytics for customer satisfaction and thereby leading to enhancement of sales. According to some retailers, customer experience drives revenue while analytics focuses on an organization's commitment in terms of value to the customers.

 Audi representative Aaron Cohen (the company's general manager of strategy and analytics) explained the process of optimizing customer experience and hence the importance of data blending. Companies are shifting their focus to freemium services, open source platforms for valuable insights. In this context, Monobina Saha (senior data engineer at Sears) highlighted the significance of R-programming among data scientists.

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