Success in reaching the South Pole was the culmination of years of their own trials, as well as previous explorations by others who paved the way with new geological discoveries, polar survival techniques and scientific research. Expedition leaders, scientists, experienced sea captains and their crews persevered under extreme conditions to create a better understanding of this undiscovered continent on numerous levels, including its wildlife, vegetation, impact on the global climate and its past and present relationships with other land masses.
The New Age of Exploration: Discovering Best Practices and Architectures of Big Data Analytics
Why did Amundsen and Scott succeed where others failed? Because they utilized comprehensive planning based on experience and replicated methods that were successful (dog sleds and skis, yes … horses and cars, no).
Now we’re in the throes of the “Age of Internet Exploration” and one thing is for certain: everyone in the business intelligence field has an opinion about how to harness the power of big data analytics. Vendors, educators and analysts all have a point of view and a vested interest in the topic. While 60 mph winds and minus 35 degree Celsius temperatures are thankfully not a factor, like the intrepid explorers of the past, innovation in BI depends on solid footing from the start.
Strategy, Architecture, Integration and Realization
In our practice, we discovered early on that if an enterprise tries to start a big data analytics initiative without a clear framework, they’re virtually guaranteed to fall down a vast crevasse of wasted energy, resources and time. We believe that the solution is twofold:
- Be sure to invest your time and resources upfront in a comprehensive big data framework that is based on proven best practices.
- Consider breaking down this big undertaking into manageable tasks, starting with a small-scale project that has limited investment and risk to your enterprise. Then, as you gather data and prove the value of the concept, expand your program based on the lessons you’ve learned along the way.
First, a well-defined big data framework strategy for your big data initiative should clearly identify and outline all of the goals, business processes and players involved in accomplishing those objectives:
- Identify opportunities
- Build the business use case
- Determine governance and ownership
- Determine the solution architecture
- Develop a big data roadmap
This framework stresses achieving qualitative, not just quantitative, solutions that will help your organization make more informed strategic decisions. It’s one thing to measure a business based on a snapshot transactional metric, such as “How many widgets did I sell last month at this location?” The real business value of your big data program becomes obvious when your firm can draw pragmatic, actionable insight as to why your firm sold so many widgets at a particular location.
Social Media Analytics as a Starting Point
Building on your initial victories and lessons learned from smaller implementations will help determine what provisions, methods, manpower and tools will function best when integrating future big data processes. For many firms, the first step in the big data journey is an effective social media analytics initiative.
Social media analytics provides a measurable means of gathering, processing, analyzing and delivering business intelligence from social media channels. Many organizations have real concerns about embracing social media software, dreading to open a Pandora’s box of damaging information and negative sentiments that lack internal or external controls. These apprehensions are indeed legitimate; however, as big data and BI consultants who work with customers in boardrooms and data centers on a daily basis, part of our mission is to reinforce the idea that current knowledge is future power. To that end, greatest success is achieved by developing social media analytics components to channel and control that power responsibly, providing its proof of value within a larger strategy.
Antarctic explorer Ernest Shackleton of Endurance was the first, and arguably most brazen, polar expedition leader to successfully leverage social media (e.g., newspapers, presentations and tenuous connections with scientific societies and notable academics) to engage the public, persuade wealthy private patrons and raise funds for and then exploit his expeditions. In 2013, the social media landscape has evolved far beyond these traditional channels to include countless data resources, including but not limited to:
- Facebook, Twitter, LinkedIn, Google+, etc.
- Review sites, like Angie’s List, Yelp, Urbanspoon, TripAdvisor, etc.
- Blogs and news sites that include/encourage comments
- Video and photo sharing sites, like YouTube, Flickr, etc.
- Search engines, such as Google, Bing, Yahoo and others
All of these unstructured data points can yield critical perceptions about our brand, our products, where these items are purchased, who is using them and how they feel about them. To get accurate answers to these issues, an effective social media analytics consulting approach should be based on the following four components, or layers, that build on one another.
Awareness - It stands to reason that if you don’t know where you are, you won’t know which way to go. The starting point is to understand consumer awareness of products and services. Measurable, disparate data is taken from the following sources: 1) social media channels; 2) search activity; and 3) regions/location (today, almost everything can be associated to a location). To gather this data, use a combination of Web crawlers and application programming interfaces to extract social media data. Typical graphics used to illustrate awareness may include charts depicting year-over-year brand mentions by region, conversations by social media channel per day and trending hashtags.
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