Databases Reference
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associated process cycles. The most important point to think here is from your organization's point of
view: What are some of the Big Data specifics that can fall into this category and what are the com-
plexities associated with that data?
Let us examine another consumer-oriented corporation and how they looked at this situation
within their organization.
Funshots, Inc. is a leading photography and videography equipment manufacturer since 1975,
providing industry-leading equipment both for commercial and personal use. The company was thriv-
ing for over 20 years and was known for its superior customer service. Funshots employed traditional
customer relationship management (CRM) techniques to maintain customer loyalty with incentives
like club cards, discount coupons, and processing services. With the advent of Web 2.0 and the avail-
ability of the Internet, smartphones, and lower-priced competitive offerings, the customer base for
Funshots started declining. The traditional decision support platform was able to provide trending,
analytics, and KPIs, but was not able to point out any causal analysis. Funshots lost shares in their
customer base and in the stock market.
The executive management of Funshots commissioned a leading market research agency to vali-
date the weakness in the data that was used in the decision support platform. The research report
pointed out several missing pieces of data that provided insights including sentiment data, data from
clickstream analysis, data from online communities, and competitive analysis provided by consumers.
Furthermore, the research also pointed to the fact that the company did not have a customer-friendly
website and its social media presence was lacking, therefore, its connection with Gen X and Gen Y
consumers was near nonexistent.
Funshots decided to reinvent the business model from being product-centric to customer-centric.
As a part of the makeover, the CRM system was revamped, the customer-facing website was redone,
and a strong social media team was formed and tasked with creating connections with Gen X and
Gen Y customers. Product research and competitive intelligence were areas of focus with direct
reporting to the executive leadership.
As the business intelligence team started understanding the data requirements for all the new ini-
tiatives, it became clear that additional data was needed, and the company had never dealt with this
kind of data in its prior life cycle. The additional data sources documented included:
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Market research reports
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Consumer research reports
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Survey data
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Call center voice calls
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Emails
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Social media data
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Excel spreadsheets from multiple business units
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Data from interactive web channels
The bigger part of the problem was with identifying the content and the context within the new
data and aligning it to the enterprise data architecture. In its planning phase, the data warehouse
and business intelligence teams estimated the current data to be about 2.5 TB and the new data to be
between 2 TB and 3 TB (raw data) per month, which would be between 150 GB and 275 GB post-
processing. The team decided to adopt to a scalable platform that could handle this volatility with
volume of data to be processed, and options included all the Big Data technologies and emerging
