Cracking The Big Data Code

26 Nov 2014


ANALYZING BIG DATA DOESN’T HAVE TO BE OVERWHELMING, but analyzing it comes with a laundry list of challenges. For technical experts, it means working out the best ways to gather, store, organize and present vast amounts of varied information. For C-suite executives, the challenge is drawing insights from big data analytics rather than being overwhelmed by them.


According to The Data Warehousing Institute, big data’s scope includes not only familiar “structured” data, such as inventory lists and customer account records, but also “unstructured data coming from sensors, devices, third parties, Web applications and social media.” Because it is so diverse, big data offers opportunities for analysis and interpretation in highly specific market segmentation, helping viewers gather insight into the user experience and gauging the efficiency of manufacturing, supply chain management and other business processes. Executives should keep in mind that the sheer amount of data may mean investing in increased storage and processing capacity.

Solutions for big data interpretation are determined by its audience (e.g. line managers, data scientists or the C-suite). Viewers of big data analytics will access results through a dashboard, and each role will call for various dashboard implementations configured to report relevant information and relationships.

At the other end of the data-gathering process, the tools available for big data analytics offer differing levels of detail. According to Tiffany Chow, Chaotic Moon Interaction Designer, some tools (like Universal Analytics) can provide a macroscopic view of particular environment; other tools focus more closely on individual visitor behaviors, such as time on page, browser information and assigned user roles. While significant on their own, macro- and micro-level data sets are most powerful in combination, as each provides context for the other. Similarly, big data analytics can provide the foundation for both quantitative and qualitative insights.


Analyzing big data comes with an inherent bias toward the quantitative rather than the qualitative. Numbers and trend lines may appear crisp and specific, while qualitative research can seem too subjective. However, quantitative data by itself may not provide the whole picture: Chow recalls one application that offered a “help” functionality with multiple links. Quantitative analytics showed that users frequently clicked on these links, which stakeholders deemed a metric of success. Interviews, however, revealed users were clicking link after link because they weren’t finding answers to their questions. In this case, the qualitative method contextualized the “what” that analysts derived from their quantitative data, ultimately helping to explain the “why.”

It’s easy to feel inundated by big data analytics, but the information they provide can be critical for C-suite executives. In order for big data to truly be useful, an organization must first define its success metrics. Those metrics will inform the infrastructure for what type of data to track and how to glean insight.

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