Understanding The Structure of Big Data

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To identify the real value of an influencer (or similar complex questions), the entire organization must understand what data they can retrieve from social and mobile platforms, and what can be derived from big data. They must understand the structure of big data itself.

Social platforms make a significant amount of data available to organizations (some free and some paid for). This is the data used in the different types of analysis that Big Social Mobile enterprises perform to better understand and build closer relationships with their consumers. When data from these plat- forms are joined together (either in a company-managed data store or through third-party social login technology), each new consumer action creates a more complete picture of who that consumer is, the make-up of their social circles, their interests, their behavioral patterns and trends, and even their psychographic information. Organizations can achieve a more granular, detailed understanding of their perfect customers, creating a linkage between consumer attributes and how they are likely to behave—whether they are prone to ideal behaviors and how they react to each company interaction, pertinent news stories, advertisements, and other brand-relevant topics.

This distinction between what is relevant to the brand and what is not pre- vents organizations from becoming lost in the size of big data. Too often, big data practitioners collect all data possible, resulting in a quagmire of facts and figures. Overwhelmed, some companies respond by focusing on a narrow swatch of information; or they concentrate on the most accessible or easiest- to-understand data (i.e., number of followers). This, however, contributes to a segregated mind-set—little attempt is made to analyze this data with larger business objectives in mind. Management fails to ask important questions using this data—how are our social media efforts increasing customer value— and instead settles on using just a small part of big data.

Integrated enterprises, on the other hand, analyze the data from their social platforms in ways that combine sophistication with granular detail. Janrain, a company that uses social logins to provide its clients with a deeper understanding of their social consumers, can, for example, pull a significant amount of data from social platforms, making it and the analysis of it available to their clients. For example they can currently collect information relative to four major groupings, eight major sub-groupings, and over 35 individual data elements, plus core profile information, from Facebook. Twitter, on the other hand, only allows them to share relatively little information, only one major grouping, two sub-groupings, and five individual data elements. And they can do this for over 30 platforms, with new platforms integrated as they become more popular.

Social platforms are prone to sharing more information about their users, not less, since these platforms predominantly generate revenue through advertising (which relies upon this data for its increased effectiveness over traditional marketing) and partnering with companies that need this actual data itself (to increase their effectiveness as Big Social Mobile enterprises do). While the information that can be collected is different for every platform, it can always be broken down into standard groupings (some of which will not be available on some platforms):

Information that identifies an individual consumer

Standard demographic information about the consumer

The consumers’ interests, such as topics that attract them, televisions 
shows or other media that they watch, hobbies they participate in and 
career paths they’ve followed

Consumer activities relative to a specific platform (such as sharing of 
professional articles or sharing of personal photographs)

The size of the consumer’s social network on the platform

When thought of in these more clearly defined terms, organizations can see big data as something that provides more insight into consumers as individuals, groups of similar individuals, and market segments as a whole. They can then analyze information in ways that have a direct impact on major business issues, answering traditional questions about consumers—such as the value of an individual influencer. But this is only possible if big data is integrated with traditional enterprise data.

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