Looking for your help with a Big Data roundtable

Looking for your help with a Big Data roundtable

Summary: We're looking for a mix of executives, experts and academics to debunk big data and real-world implementations.

TOPICS: TechLines, Big Data

In October, we're looking to hold a big data panel to focus on the real-world implications for corporate IT. I'm looking to crowdsource a few ideas.

The overview of the panel roughly goes like this:

The big data and analytics panel will focus on how real world applications deliver value and how drowning in data has become the biggest issue in the enterprise. How do you filter and use that data to better understand customers, partners and employees?

That's the boilerplate, but here's where you come in.

  • What panelists and big data experts would you like to see?
  • What detours on the big data topic are worth exploring?
  • And what are your must have threads on the big data and analytics topic?

We're looking for a mix of executives, experts and academics to debunk big data and real-world implementations. The potential panelists will riff for an hour or so on big data. The panel will also be livestreamed with a meetup afterward.

Thanks for your help and I look forward to hearing your suggestions via the talkbacks below, email or Twitter @ldignan. Cheers.

Topics: TechLines, Big Data

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  • Thank goodness...

    I'm glad to see that people are starting to question the promises and buzz of 'big data'. I would like to see some realistic people with real-world experience on the panel. If they haven't actually worked on 'big data' projects, they can stay at home.

    As for threads of information, I would like to see a nice comparison of 'big data' promises vs. reality in terms of what problems people have actually solved. Most of the implementations I've seen and worked with have to do with HA and redundancy in systems. On the other side of the coin is the 'big data' analysis (with technologies like hadoop) to take advantage of parallel processing techniques. These still require real people to perform analysis, 'big data' doesn't mean there is now some magic button that does all the work for you. Right now, I see only very large institutions that can afford the type of hardware and people needed to run these types of functions.

    Also, if we could start killing the term 'big data'... it makes people think that it is somehow fundamentally different from any other type of data... it???s not... there???s just more of it. In another few years, with the rate of data acquisition, 'big data' will just be regular data anyway.
  • Hmmm...

    I can't say I support the notion of "debunking" big data or krsanford's desire to kill it. By the way, kr, you might want to explore the open source possibilities in big data (I know open source is frowned on around here) and you'd discover that one does not need to be a large institution to perform data mining on big data.

    I think you really need to talk to the data mining experts at http://www.kdnuggets.com/ which is the most popular portal on the web for data mining, analytics, machine learning, BI, etc. The fellow who runs it, Gregory Piatetsky, is one of the top experts in the field, founded a conference on the subject, etc. His profile is at

    He's been an invaluable presenter on some other panels I've seen and definitely a great resource for what you seem to be looking for.
    • OSS 'Big Data'

      Almost all of the big data solutions I've used and looked at are open source. I'm a fan of open source software. However, just because the software is free, doesn't make it cheap. When you actually implement it, you find that there are learning curves everywhere, you need people that are smart enough to set it up and use it properly ($$), and if you are using big data technologies on mediocre hardware, you aren't really being efficient ($$). (and if you do use small hardware setups, then you probably don't actually have 'big data' on your hands, you probably just have 'I can't fit it in excel' data). Now you need people to actually perform and interpret the analysis in a useful way ($$). Another issue in this whole thing is that some of these technologies are very new, and if you make the wrong decision, you could be left out in the cold after having spent so much to get up and running ($$). I don't doubt that this is a fast moving tech area, and very soon there will be some good solutions for smaller companies, but right now I just don't see it as feasible.
  • How about debunking . .

    How about debunking BYOD while you're at it? Or the notion that 100% cloud is somehow better than hybrid? Or that we should even be using the word "cloud" at all?

    Or better yet, debunk the idea that ZDNet cares about something more than buzzwords and panaceas? That's an easy one.

    ZDNet's obsession of panaceas is beyond belief. "Cloud" computing is a panacea for all ills. So is BYOD. So are social networks. So is big data. So are tablets and other mobile devices.

    Frankly, I'm sick of the obsession with panaceas. For every article like this one that wants to look at the drawbacks of a new idea, there are 100 articles that poo-poo any serious analysis of any drawbacks whatsoever. ZDNet in general prefers to ridicule rather than seriously study things, and prefers panaceas over reasonable solutions.

    Frankly, I'm tired of the panaceas. More articles like this, please.

    Here are some things I'd like to see studied:

    -The benefits. I'd still like to know what the actual, tangible benefits are. Right now, I'm not really seeing them. "We [i]might[/i] stumble on something that helps us" doesn't really sound like you're going to get a good ROI.

    -The systems that process "big data" have to be pretty big. That really sounds like a whole lot of overhead, which ties back into ROI.

    -The overhead of computing systems in general for businesses. I look at a lot of large businesses, and I do wonder if the overhead is really less than any loss of sales they'd get without it.

    I'm not an expert, but "big data" seems to amount to looking for a needle in a haystack - when nobody said the needle was there, and in fact might not exist. You're just searching the haystack because it happens to be there.
  • Which projects succeed?

    Big projects that succeed have clear problem statements.

    Alternatively they're incremental and build on functionality from other projects... so they're deemed to succeed regardless simply because they work.

    Big projects that succeed are also linked to certain people and certain software.

    This is theme should be explored in more detail to quantify what those qualities are.

  • Someone from Google or Facebook..?

    I would appreciate if you guys can pull someone from Google and Facebook. These IT giants have utilized their data even before it was termed as BigData. Especially "Google Ad" and the suggestion that Facebook throw at us is admirable. I am sure they create a pattern for every user.. I would love to see how these guys mine their data to get such accurate results.

  • Big Data and the "cloud"

    I love how the terms are used. How they generalize so much that isn't easily generalized. Virtualization, centralization, etc...

    Big data has big issues, including privacy.

    If I had anyone I'd list as most important it would be you and the other editors here and at smart planet etc.

    Big Data can be hyped all day long, but it can't steer free from common sense.
  • Start with a definition, end with a plan

    Big Data does not need to be debunked, it needs to be defined.

    I have seen "big data" used to mean: lots of data, unstructured data, data that needs to be constantly recategorized, data that I want but don't know how to gather or organize.

    Perhaps if the panel starts with a clear definition of what Big Data means, that will lead to a useful discussion on how it is used. Once you know what kind of Big Data you have, you can then move to understanding its application.

    Depending on your definition, Big Data can have very different applications for different sizes of company.

    I look forward to seeing more detailed analysis on this topic.
  • It's not all or nothing

    I have the benefit of getting to speak to a lot of Directors and VPs of IT. What I consistently hear is that when it comes to BigData, projects around them tend to be one of three camps: (1) Greenfield opportunities where the organization has a chance to really build something truely BigData-centric from the ground up using one of the many tools available (rare); (2) Nothing is happening (rare to moderate); (3) There is interest but the reality that the business faces is that the data lives in an existing application with an existing database and the prospects of getting that changed anytime soon is little to none (frequently).

    My takeaway is that BigData tends to be viewed as an all or nothing approach where to really have a project in the space, you need to be dealing with technologies typically associated with it (NoSQL, Hadoop, etc.) or you're not doing it. Reality is somewhere in the middle -- people are looking at real problems with real apps and real databases and are instead asking how they are going to scale it out with the resources that they have. It may not be BigData with a capital B, but it is introducing the question of how to address scale at levels well past what the original architects of the solution intended.

    I'm loathe to ask it, but BigData needs to go through that painful cycle that cloud did where definitions need to get dragged through the mud because like the folks that were building highly automated virtual compute farms finally were called clouds by the purists, I think the people that are hacking together solutions to get things done with datasets larger than what their creators intended should get the credit for working bigdata. There is a good discussion here.

    There is also the business question of value. Lots of cool visuals but I still get the occasional CIO that bluntly states that to date, none of the lines of businesses he supports can come up with a use case that isn't answerable using current solutions in place. Smart technical people can dream up use cases, but businesses are in-turn pressed to make use of the result.

    Taking BigData and going vertical with application-specific data and vertical-orientation to business is something that I think is also inevitable. It is difficult to extract value out of highly generic systems. Room for discussion here... I'm sure there are some people that are building tools that would beg to differ with me.

    The list goes on...