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 emerging from the realms of data science projects to help companies understand exactly, make decisions, and act in real-time to better serve their customers and target markets. The IT techniques and tools to execute big data processing are new, very important and exciting.
"Big data" started at least 120 years ago-- even if the buzzword is more recent. Vincent McBurney wrote a superb blog entry about "big data", which I highlighted in my own here:
We have spent the past yeart trying to define "big data." In the end, it comes down to a couple of things. A) people like to think they have big data, even when they don't. Here is why I say this. If you have a 32 GB 8 core SQL server and you have a database table with 250 GB of data in it. yeah, you might think you have big data. If you have 900 GB of data, and 512 GB of RAM on your database server, you might think you have "big data." No, you don't, B) If you have a 6TB table of data in your DB, and you have 2 TB of RAM, and you think you have big data, YES. YOU DO. Well, at least according to my definition...my definition, constrained by current techological limitations, goes like this: If you have to make a call to disk for data, and the amount of time it takes to return that data to your users exceeds the amount of time that users are willing to accept, AND the ONLY WAY to alleviate this issue is to increase your bandwidth to disk because you have ALREADY MAX'D out the technologically available amount of RAM within which the database could reside, then you have "BIG DATA." I hope this clarifies things. My team deals with this on a daily basis from some rather gigantic companies with rather large amounts of corpuscular data. By "corpuscular," I mean single, extremely large chunks of data that can not be fragmented across multiple systems and MUST be treated symptomatically. That is, the data is interrelated, and the users seek to examine it as a whole, and not as discrete entities. I really could go on and on about this....
LEADERS FROM THE BUSINESS AND IT COMMUNITIES DUEL OVER CRITICAL TECHNOLOGY ISSUES
The Current Discussion
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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