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Big Data: We Need A Broader, Less Biased Definition

Blog by Philip Russom
Research Director for Data Management, TDWI

All kinds of people have recently weighed in with their definitions and descriptions of so-called “big data,” including journalists, industry analysts, consultants, users, and vendor representatives. Frankly, I’m concerned about the direction that most of the definitions are taking, and I’d like to propose a correction here.

Especially when you read the IT press, definitions stress data from Web, sensor, and social media sources, with the insinuation that all of it is collected and processed via streams in real time. Is anyone actually doing this? Yes, they are, but the types of companies out there on the leading edge of big data (and the advanced analytics that often go with it) are what we usually call “Internet companies.” Representatives from older Internet companies (Google, eBay, Amazon) and newer ones (Comshare, LinkedIn, LinkShare) have stood up at recent TDWI conferences and described their experiences with big data analytics; therefore I know it’s real and firmly established.

So, if Internet companies are successfully applying analytics to big data, what’s my beef? It is exactly this: a definition of big data biased toward best practices in Internet companies ignores big data best practices in more mainstream companies.

For example, I recently spoke with people at three different telcos – you know, telephone companies. For decades, they’ve been collecting big data about call detail records (CDRs), at the rate of millions (sometimes billions) of records a day. In some regions, national laws require them to collect this information and keep it in a condition that is easily shared with law enforcement agencies. But CDRs are not just for regulatory compliance. Telcos have a long history of success analyzing these vast datasets to achieve greater performance and reliability from their utility infrastructure, as well as for capacity planning and understanding their customers’ experiences.

Federal government agencies also have a long history of success with big data. For example, representatives from IRS Research recently spoke at a TDWI event, explaining how they were managing billions of records back in the 1990s, and have recently moved up to multiple trillions of records. (Did you catch that? I said trillions, not billions. And that’s just their analytic datasets!) More to the point, IRS data is almost exclusively structured and relational.

I could hold forth about this interminably. Instead, I’ve summarized my points in a table that contrasts a mainstream company’s big-data environment with that of an Internet-based one. My point is that there’s ample room for both traditional big data and for the new generation of big data that’s getting a lot of press at the moment. Eventually, many businesses (whether mainstream, Internet, or whatnot) will be an eclectic mix of the two.

Traditional Big Data

New Generation Big Data

Tens of Terabytes,
sometimes more

Hundreds of Terabytes,
soon to be measured in Petabytes

Mostly structured and relational data

Mixture of structured, semi-structured, and unstructured data

Data mostly from traditional enterprise applications: ERP, CRM, etc.

Also from Web logs, clickstreams, sensors, e-commerce, mobile devices, social media

Common in mid-to-large companies:
Mainstream today

Common in Internet-based companies:
Will eventually go mainstream

Real-time as in Operational BI

Real-time as in Streaming Data


I’m sorry that I’m foisting yet another definition of big data on you. Heaven knows, we have enough of them. But I feel we need a less Internet-biased definition in preference of one that’s broad enough to encompass big-data best practices in mainstream companies, as well. For one thing, let’s give credit where credit is due; and a lot of mainstream companies are successful with a more traditional definition of big data. For another, we run the risk of alienating people in mainstream companies, which could impair the mainstream adoption of big-data best practices. That, in turn, would stymie the cause of leveraging big data (no matter how you define it) for greater business leverage. And that would be a pity.

So, what do you think? Let me know!

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Some of the material of this blog came from my recent Webinar: “Big Data and Your Data Warehouse.” You can replay it from TDWI’s Webinar Archive.

Want to learn more about Big Data Analytics? Attend the TDWI Forum on Big Data Analytics, coming in Orlando November 12-13, 2012.

Posted by Philip Russom, Ph.D. on May 1, 2012


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