The best definition of big-data I ever heard was simple and to the point: "Its the stuff we used to call 'a whole lotta data,' " a presenter at a recent conference said.
He wasn't joking. While big-data is arguably the buzzword of the year, defining it continues to be a source of endless consternation. So what's real? What makes sense? Here are three perspectives that define, discuss, and help to focus the role of big-data today.
Big-data is how we get to the right message, to the right people, at the right time, on the right platform, on the right place. Oscar Padilla, VP of Strategy, Luminar
Luminar is an analytics and modeling provider that serves the US Hispanic market, with a goal of "transforming Hispanic consumer data into true insights and business intelligence." At the recent Portada Annual Conference, Padilla noted that big-data can help marketers reach their goals and make an effective sell of everything, from credit cards and hair-care products to automobiles and political messages.
Big-data is enabling marketers to accelerate their growth through more relevant and precise data models, he said. In other words, it's creating opportunities for "big analysis" that can provide greater accuracy, more practicable analyses, and improved performance and forecasting.
Big-data is a relative concept. What is big today won't be big tomorrow...
But big-data isn't about that. It's a story about the meaning, the challenges, changes and opportunities that it represents. Patricia G. S. Florissi, VP and Global CTO, EMC
During a recent presentation at Worcester Polytechnic Institute in Worcester, Mass., Florissi predicted the era of big-data we will trigger changes similar to the ones that occurred during the Industrial Revolution, with some industries ceasing to exist, new ones being created, and others reinventing themselves.
Those trying to police what is or is not big-data will often do so based on what their interest, sphere of influence, knowledge or experience and jobs depend on. Greg Schultz, Founder, Server and StorageIO
In a post entitled "Little data, big data and very big data (VBD) or big BS?" he noted, "Big-data is not BS, however there is a lot of BS marketing BS by some along with hype and fud adding to the confusion and chaos, perhaps even missed opportunities."
While "big-data is real," he writes, there are variations, use cases, and types of products, technologies, and services that fall under the big-data umbrella. In addition, it is wrong to characterize all data as big-data. "What this all means is that there are different types of applications for various industries that have big and little data, virtual and very big data from videos, photos, images, audio, documents, and more."
How do you define big-data? Do you agree with any of the points these data pros made?
Big Data is any data that doesn't fit well into tables and that generally responds poorly to manipulation by Structured Query Language (SQL).
[T]he most important feature of Big Data is its structure, with different classes of Big Data having very different structures.
With that definition, we can start to look at examples. A Twitter feed is Big Data; the census isn't. Images, graphical traces, Call Detail Records (CDRs) from telecoms companies, web logs, social data, RFID output can all be Big Data. Lists of your employees, customers, products are not.
Ariella, - So true though what he says. Big-data is just lots of data in different terms. Funny though how as soon as someone found the shortest word to refer to lots of data, it became complicated. I believe that most of the difficulty in explaining big-data is in actual sense difficulty in understanding the tools of handling big-data and their dynamics
@Noreen: Excellent topic. Defining big data is something I pondered on while penning my recent musings on the definitions of other industry terms. I like the statement about 'big data' being relative. It's kind of like calling something 'new'. Well, it's new now, but for how long? And when it's not anymore, will we take the new off? In my first analytics job, I worked on a SAS server that had 800 GB of disk space. We thought that was a ton. Now you can buy mulitple TB drives off the shelf at Staples. Is 800 GB still 'big'? Nope. Exa-, zetta- and yotta- are big, for now, but for how long?
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Visual Analytics: Who Carries the Onus? The Issue: Data visualization is an up-and-coming technology for businesses that want to deliver analytical results in a visual way, enabling analysts the ability to spot patterns more easily and business users to absorb the insight at a glance and better understand what questions to ask of the data. But does it make more sense to train everybody to handle the visualization mandate or bring on visualization expertise? Our experts are divided on the question. The Speakers: Hyoun Park, Principal Analyst, Nucleus Research; Jonathan Schwabish, US Economist & Data Visualizer
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