Converged analytics has been achieved by some because the assets an organization has are mainly digitized to begin with, and big data was gathered via social media. Columnist Marshall Sponder explains.
In previous articles in this column, we have discussed the difficult road to convergence analytics for many businesses and organizations. In some instances, converged analytics has been achieved because the assets an organization has are mainly digitized to begin with, and big data was gathered via social media (particularly Google, Amazon, LinkedIn, Facebook and Twitter) to produce marketing and consumer intelligence.
Here's one example showing the career path of financial professionals working in companies directly related to the financial collapse of 2008 produced by LinkedIn Analytics.
Figure 1: LinkedIn Analytics
LinkedIn leveraged their large, mainly Oracle-based repository using sophisticated frontend applications supported by enterprise analytics (which I explored with my MBA Class at Baruch College recently) to produce the image above (figure 1).
Figure 2 - LinkedIn Talent Pool Report Subsea Engineering/Offshore Operations (Oil & Gas)
In a similar way, LinkedIn produced hiring intelligence with its talent pool reports (figure 2).
As mentioned in my previous article at ClickZ, organizations that can digitize their assets are in a position to provide converged services. Facebook has also made the most strides in aggregating its data and producing actionable intelligence with Facebook Graph Search.
In my work at Rutgers University (where I authored and teach a popular course on Social Intelligence and the Arts), Facebook Graph Search allowed me to see the digital interests of my students dictated in a more individualized approach to teaching than is customary.
Figure 3 - Facebook Graph Search Representation of Mason Gross School of the Arts various art disciplines - produced by WebMetricsGuru INC.
I was also able to produce a visualization showing how different percussionists are from pianists, based on interests (as identified by Graph Search) in their learning and engagement needs.
Figure 4 - Facebook Graph Search - Brand Interests of Musicians at Mason Gross School of the Arts produced by WebMetricsGuru INC.
Using the same method with Graph Search, I visualized people who work in Fashion in NYC and based on what they liked, could see that technology wasn't one of them (figure 5). This allowed me to align any marketing approach I will make with the target audience I teach Fashion Analytics at FIT (Fashion Institute of Technology) next year.
Figure 5: Fashion Interests (NYC) from Facebook Graph Search - Produced by WebMetricsGuru INC.
Google, with its many platforms and services, is probably the leader in digitizing its entire business that began with Search, but certainly hasn't ended there. Not long ago, Google launched the Think Insights platform as a way to do market research using the data Google already has. It even provides infographics through its dashboard for market research, like the 10 most subscribed to entertainment channels of YouTube in October 2013.
Figure 6: Google Think Insights Dashboard of the 10 most subscribed YouTube Channels in Entertainment during October 2013.
Twitter, with its massive use of data, has been an enabler of many attempts to visualize the web because it is very open in how the social data can be collected, harvested and used.
Figure 7 - Ryan Gosling Won't Eat His Cereal mapped by his Twitter followers - produced by Pulsar TRAC platform
Ryan Gosling, a popular actor, was captured in a series of Twitter Vine videos that spread virally; it was easy to visualize using the platform Pulsar TRAC (figure 7).
in sum, convergence analytics works well through social media channels like LinkedIn, Facebook and Twitter because the data and workflow are already digitized from the outset.
Organizations that have largely brick and mortar based business must think digitally while quickly transforming themselves without losing their intrinsic brand identity, in order to achieve the same results.
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For over a decade Marshall Sponder has influenced the development of the digital analytics industry with his WebMetricsGuru writings that focus on social media metrics, analytics and media convergence. He also possesses considerable in-house corporate experience with roles at IBM, Monster.com, Porter Novelli, and WCG while continuing to work with start-ups. Marshall is a Board Member Emeritus at the Web Analytics Association (DAA) and teaches Web Intelligence at Rutgers University and Baruch Business College. Marshall is the author of "Social Media Analytics: Effective Tools for Building, Interpreting, and Using Metrics," published by McGraw-Hill in 2011.
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