How ‘Big Data’ Is Different

These days, lots of people in business are talking about “big data.” But how do the potential insights from big data differ from what managers generate from traditional analytics?

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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These days, many people in the information technology world and in corporate boardrooms are talking about “big data.” Many believe that, for companies that get it right, big data will be able to unleash new organizational capabilities and value. But what does the term “big data” actually entail, and how will the insights it yields differ from what managers might generate from traditional analytics?

There is no question that organizations are swimming in an expanding sea of data that is either too voluminous or too unstructured to be managed and analyzed through traditional means. Among its burgeoning sources are the clickstream data from the Web, social media content (tweets, blogs, Facebook wall postings, etc.) and video data from retail and other settings and from video entertainment. But big data also encompasses everything from call center voice data to genomic and proteomic data from biological research and medicine. Every day, Google alone processes about 24 petabytes (or 24,000 terabytes) of data. Yet very little of the information is formatted in the traditional rows and columns of conventional databases.

Many IT vendors and solutions providers use the term “big data” as a buzzword for smarter, more insightful data analysis. But big data is really much more than that. Indeed, companies that learn to take advantage of big data will use real-time information from sensors, radio frequency identification and other identifying devices to understand their business environments at a more granular level, to create new products and services, and to respond to changes in usage patterns as they occur. In the life sciences, such capabilities may pave the way to treatments and cures for threatening diseases.

Organizations that capitalize on big data stand apart from traditional data analysis environments in three key ways:

  • They pay attention to data flows as opposed to stocks.
  • They rely on data scientists and product and process developers rather than data analysts.
  • They are moving analytics away from the IT function and into core business, operational and production functions.

1. Paying attention to flows as opposed to stocks

There are several types of big data applications. The first type supports customer-facing processes to do things like identify fraud in real time or score medical patients for health risk.


Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
More in this series

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Christopher Rollyson
@thomas @paul @randy, thanks for a great summary. I'm currently researching big data for transformation as that will be a channel in the Chief Digital Office [], and Enterprise Analytics is a source. []. Although implied in points 2 and 3, I'd like to put in that part of the "magic" in big data analytics is knowing what questions to ask, given the data that's practically available (constantly changing), business goals and users. I say this because my firm has a defined methodology for its Ecosystem Audit offering in which we create analytics for understanding social data and we go far beyond social media monitoring platforms' reports. As the architect of the methodology, I find it both analytical and creative to use the data to get the answer for the question whose answer you need. We focus on things like trust and how it changes based on interactions, and quantitatively. It can get quite messy, but it works consistently. Given this, I can imagine that traditional data analysts, who are presumably accustomed to structured environments, will have a harder time in the "chaos" of the accelerating, external web, whose data is undoubtedly growing faster than internal structured. You have to be creative with social data because you are measuring human behavior and making logical inferences; thankfully, you can test the models quickly and fairly easily. Thanks again for a great summary!
As a creative opportunist helping fast growth SMEs innovate from their existing intellectual assets, I find the above article fascinating.  It seems to me that the interpretation of the big data gives large companies access to their own speedy Boyd loops in a ways they will not previously have anticipated.
Enjoy the article and good comments above, my take is: Big Data brings opportunities, also some distractions, only the well-mixed analytical team can work seamlessly to solve Big Data Puzzle --The business insight in decision making, customer experience optimization and talent management:

Big Data also means the full data life cycle management, from data storage, to data governance, but still need keep in mind: Big Data is means to the end, not the end. thanks
A very thought provoking article. In my opinion application of these concepts have been incubating for some time. Success is in the execution. The need to differentiate data and how it is utilized (i.e. labels of scientist vs. analyst) seems unnecessary. Yes, Ph.D’s with an MBA who want to code may be in short supply, and in 3-5 years do you feel that will still be the ticket to the club? It sounds like an important skill that is hard to find is the ability to be a boundary spanner. Combine this with the ability to access disparate information, synthesize, and apply it to solve business problems in ways that were not available before. The strength of the big data buzz will be that information value is based in the business unit and not in IT. It’s the concepts of velocity and ubiquity of technology to enable access to the right information that makes big data a big topic to me.
What you describe is fundamentally the difference of data at rest in reports, poured over by data analysts and data in motion, managed by data scientists who are looking for trends, flows, processes. Those static reports built up knowledge, but data in motion IS knowledge.

Big Data is about patterns more than discreet elements of information and that's where everything changes. There's a pattern of intelligence that has always been elusive and much like the The Matrix where some people could watch the flow of green 0's and 1's to see what was happening in real-time. 

I wrote up this idea here:
A great introduction to the new capabilities big data brings to the business! I need, however, a little clarification on what you think IT's role is or would be in the adoption of big data. You recommend "Moving analytics from IT into core business and operational functions." But you also say "IT organizations will train and recruit people with a new set of skills who can integrate these new analytic capabilities into their production environments."
Could not agree more with points 1 and 3.  In my article "The Big Bang of Marketing:  Big Data" I discuss briefly how the importance of any type of 'big data' initiative is to find the story/the trend journey behind the data that leads to meaningful consumer insights that drive long-term engagement if the right questions are known.  Data knowledge must be taken to the operations/marketing groups from IT in a partnership that is synergestic with the IT reporting role and the user role of functional/vertical groups.  
Finally, quants professionals should not be expected to be the sole interpreters of data, business and marketing knowledge is critical to get the 'big picture' of 'big data'.