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Analytics for Human Information: The New Top 10 Myths of Big Data - Myth #8

ChrisSurdak ‎02-05-2014 08:14 AM - edited ‎02-19-2015 01:37 PM

Welcome to 2014, and welcome back to The New Top Ten Myths of Big Data. Last month, we left off with Myth #7, where I emphasized that there is at least as much value in using big data tools and techniques to analyze the processes within your business as there is in analyzing your customers. In this installment, I hope to further clarify how to extract value from your analytics efforts.

 

 

Big Data Myth #8: Big Data is about the “what”  

When a math, science, or engineering person looks at a problem, it is typical that our first inclination is to ask “what” is happening. This is the result of years of education and training. After all, if you don’t have a firm understanding of what you are analyzing, you have no idea if the data you are collecting is even relevant, nor do you have any idea what questions to ask to achieve a deeper understanding of the problem at hand. For this reason, a great deal of the effort being expended in Big Data has focused on the question of “what.” 

  • What do my customers want? 
  • What do they think of my products or services? 
  • What will make them chose me over a competitor?
  • What can I do to more deeply engage my customers and earn their loyalty?  

Answers to the “what” questions come easily to technically-minded people, because they’re deeply left-brain focused. Answering these questions is also easier because you’re analyzing purely quantitative, transactional information—the kind that organizations have been collecting for the last several decades. However, the power of Big Data tools may best be leveraged, and their value best realized, when they are used to answer a whole new range of questions: “Why” questions, to be specific.

 

When we start to ask “why,” we start to develop a deeper understanding of behavior. Answers to “why” questions give us insights into how people think, what matters to them, what their decision-making process consists of, and ultimately, what makes them tick as a human being. As the character, the Merovingian, in the movie “The Matrix” pointed out, “To understand why is to have power. If you have no why, you have no power.” 

 

But to ask the question of “why,” we need to access a whole new range of data sources and types.  Transactional data simply cannot answer “why” questions; it doesn’t have the necessary context—context that is almost always held in unstructured, human-generated data.  This data, such as email, texts, audio, video, tweets, etc., is rich with context, meaning, and ultimately the answer to “why.” 

 

While our structured transactional data might tell us that a customer bought a birthday cake today, it’s their tweets and Facebook posts  that make it clear that it’s her mother’s birthday tomorrow, and not her own.

 

Unlocking the power of “why”

As the prior example shows, answering “why” can be a fundamental element of an organization’s efforts towards customer engagement. Indeed, the whole point of big data efforts are, or should be, to understand and engage customers at a deeper, more intimate level. This can only be achieved through understanding why they do what they do.  And these questions of why can only be answered when we combine structured data with unstructured data in new ways, and then start asking new questions. 

 

Many organizations are attempting to leverage big data without the ability to integrate structured and unstructured data. For this reason, they are still only addressing questions of “what,” rather than “why.” 

 

While there is certainly value in understanding questions of “what” at a deeper level and in a timelier manner, today’s data volumes allow us to answer “what” questions with profound precision. But at some point we get diminishing returns on these answers. Eventually, “what” only gets you so far. 

 

To take the next leap towards understanding, you need to address “why,” and this implies entirely new sets of data, consumed and digested in entirely new ways.

 

It is for precisely this sort of analysis and understanding that HP has created its HAVEn platform.  Leveraging Hadoop and Vertica for structured data analysis, and HP Autonomy’s IDOL technology for understanding unstructured data, we provide customers with the ability to ask and answer questions of “why” in real-time. Through this capability, your organization can achieve a dramatically deeper understanding of those they serve, and create a level of customer intimacy and engagement not previously possible. 

 

If this sounds good to you, give HP a call and let us show you what is possible when you start asking “why.”  You’ll wonder “why” you didn’t do so sooner!

 

New Big Data Myth #9 will explore why being dirty can be a virtue.

 

Click below to continue reading about The New Top Ten Myths of Big Data

#HPAHIB

 

Edited by Robin Hardy

 

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About the Author

ChrisSurdak

Chris Surdak is a Subject Matter Expert on Information Governance, analytics and eDiscovery for HP Autonomy. He has over 20 years of consulting and technology experience, and holds a Juris Doctor from Taft University, an MS from the Wharton School at the University of Pennsylvania, a CISSP Master's Certificate from Villanova and a BS in Mechanical Engineering from Penn State. Chris is author of the Big Data strategy book, "Data Crush," which was recently nominated as International Book of the Year for 2014, by GetAbstract. Chris is also contributing editor and columnist for European Business Review magazine.

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