Social Analytics Go to the Head of the Class

Several students in cap and gown take a selfie with a Dell tablet

As I shared in my last post, February’s TDWI event in Las Vegas held special meaning for me, since it marked the organization’s 20th anniversary and my fourth consecutive year as a faculty member. Dell Software also hosted a book signing for my latest publication, co-authored with Krish Krishnan and entitled Social Data Analytics: Collaborations for the Enterprise.

This book is the first practical guide for professionals who want to employ social data for analytics and BI. We offer a series of use cases and examples to help readers make the most of the five social data types: Sentiment, behavioral, social graph, location and rich media data. I shared some of the insights from the book in my class, aptly called “Social Analytics Driving Real Business Value with Big Data.”

Every time I teach this course, I’m struck by how the use of social data continues to mature. Early on, social analytics discussions were met with equal amounts of skepticism and cynicism. Executives, in particular, had a chip on their shoulders and couldn’t see where social data fit in their enterprises. Now, instead of challenging the validity or usefulness of social data, companies want to understand how they can take advantage of social analytics to be different and more innovative.

To demonstrate the continuing maturity of social analytics, I hosted my first “Level Four Maturity” student in Las Vegas, as his organization is participating, listening, integrating and analyzing data in a project. Each of these represents a level of my Social Media Maturity Model as described and depicted below:

  • Stage 1: This is where most companies begin their social data journey.  Data consumption is limited with companies focusing mostly on data creation and participation rather than actual data utilization.
  • Stage 2: Companies begin to invest in social monitoring tools that enable them to listen to the social sphere. While some decision processes enter into the picture at this stage, they’re not integrated into more sophisticated systems. As a result, data and the applications tend to be siloed in sales, marketing and customer service departments.
  • Stage 3: The integration stage is a critical step forward as it treats the social-based data as a corporate data asset, which then needs to be integrated into traditional analytic and operational systems to add value to business insights.
  • Stage 4: Companies take a more sophisticated approach to social data, leveraging its low latency characteristics as well as the opportunity to integrate and utilize unstructured sources to gain better perspective and drive processes forward.
  • Stage 5: Companies achieving this level of interaction are getting the full value from social data analytics. The integration of data into mission-critical processes enables organizations to take real-time action from social graph, behavior and sentiment data.

 Social Media Maturity Model graphic

Most organizations get stuck between levels two and three because it requires them to integrate siloed social and business data. However, my student, who worked for a retailer, shared how his company had cleared this hurdle to have a major impact on their supply chain. Not only did the company successfully integrate multiple systems with multiple data sources, they then went on to aggregate and mash it to produce actionable insights to better serve customers.

By listening and engaging with customers, the retailer developed a better understanding about which particular brands and products were liked best. This data then was integrated with geo-location data to determine regional preferences for different brands and products. Armed with this knowledge, decisions could be made about which models should drive the supply chain.

While this example went straight to the head of the class, the majority of students still were struggling to understand the best ways to listen and join the conversations around the five major social data types. And for good reason.

Each type brings a unique set of capabilities from an analytics standpoint, especially when you marry them. The possibilities for innovation are endless. Interactive billboards speak to your phone and engage you on the spot. Retailers capture in-store location data to produce behavioral insights designed to improve your shopping experience.

As social analytics mature, the line between innovative and icky gets blurry pretty fast. That’s why we spent time talking about the cultural aspects of maturing social models. Everyone agreed that companies need to invest in policies and best practices that address transparency and privacy.

The time is right to get schooled on the impact of social analytics because as it evolves, walls between siloed areas will come down and make it much easier to integrate data into wider decision platforms. Solutions, like Dell Statistica, analyze highly useful social sentiment data that helps customers create more enhanced views or highly detailed slices of their customers.

Actionable business insights are within reach of companies that effectively listen, engage and integrate social data from one platform to another. Then, they can bring all this information into their critical workflows for enhanced decision making. As this area continues to evolve, slews of real-world examples will surface that will inspire others to learn how to fulfill the promise of social analytics within their own organizations.

I can’t wait until my next class to find out which new innovative uses of social analytics make the honor roll. Until then, shoot me an email at with your best social analytics use case.

About the Author: Shawn Rogers