Big data has little value without big analytics


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At a recent conference on customer analytics & intelligence, I noticed a gathering interest by many attendees in getting ahead of the curve on big data initiatives.

This event had an academic/commercial feel with a small but well-qualified audience. Presenters were a mix of commercial end users and big data technology vendors.

The more forward-thinking retailers had some compelling stories on their experience leveraging big data to date. These organizations are ahead of the curve and actively engaged with leveraging big data. Their goal is leveraging the large amounts of data they gather daily across multiple channels and turning it into market/business intelligence for competitive advantage. The companies that are leading the charge are already well on their way and reaping rewards in commercial deployments. Real life examples include tracking customer journey via text, voice, email, phone and video channels – even over days, using software and cameras to determine wait times at local banking branches, determine brand awareness and favoritism or dissatisfaction via sentiment analysis on twitter feeds and blogs, predicting when/where customers are most likely to purchase based on demographics, past behavior, seasonal factors etc.

Most retailers and financial services organizations are however, in the early stages of learning what big data can do for them. They know there is value in their data but just have no idea on how to leverage it. They are gathering large amounts of data on a daily basis across multiple disparate channels on services that were for the most part acquired years ago by separate departments and for a specific purpose. Representatives of these companies freely admit they are actively looking to other companies as good role models for their future plans.

Product and service providers in the field are focused on addressing the needs of this emerging market. Services here include sentiment analysis, data analysis and representation, pattern recognition, finding the needle in a haystack and tracking and predicting consumer behavior.

The market for behavioral analytics, customer intelligence and tools to analyze big data sources like social network feeds, the phone channel, real-time and recorded video, real-time and recorded speech analytics, SMS, MMS etc. is still in its formative stages. Retailers and vendors that get in early and/or provide a unique offering in this field will have a distinct competitive advantage.

Having all sources of customer interaction available for analysis and using newly developed tools in innovative ways is the right place to start.

I will be writing more on this subject in future posts.

Daniel O'Sullivan
CEO, innovator and technologist in software engineering and product development. Created and implemented Adaptive Technology and Fastrack Software products that have optimized over 1.5 Billion self-service phone calls worldwide and saved clients over $100M to date. Electrical Engineering undergrad with a Masters in Computer Science. Lucent/Bell Labs alumni. Winner of worldwide eco-design project and received several patents. Currently CEO of Software Technology Partners.Focus: Business Development, Technology Partnering, Mobile, Web and Cloud Technologies and Human-Computer Interaction.


  1. Interesting post. I’m looking forward to hearing where you think things are headed. I’ve seen some really compelling research on combining customer support data with usage data in churn prediction.

  2. Thanks Robert. Yes, I believe there’s gold to be mined. Unlike gold though, it’s knowing how to look, not just where.


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