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Most data isn’t “big,” and businesses are wasting money pretending it is

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Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#61

Post by spikels »

capkutay wrote: post #54
dewitt wrote: post #19

If I ever want to get rich, I'll set up shop convincing small businesses they need to do things the way Google does, if only they want to remain competitive. Oracle has used exactly this business model to great success, and obscene profit, for over 30 years.

Believe it or not, there's a world outside silicon valley where not every company can hire a large team of engineers to create and maintain their data infrastructure to process sales, do crm, keep track of manufacturing, etc. That's why companies like Oracle exist (and are very successful) Edit: I noticed you meant small businesses. However, Oracle does this mainly for the large companies that don't excel at technolo…

I hate the term "Big Data" but if it somehow puts Oracle down it can't be all bad.

Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#62

Post by fusiongyro »

joseph_cooney wrote: post #56
yen223 wrote: post #38

Earlier quoted context omitted.

To be pedantic, he said Oracle follows the same business model , not that Oracle follows Google .

But if google has only existed for 15 years how can the business model of "get money out of people trying to help them do a google me-too" have existed for 30?

You're putting us to sleep with this tiresome over-literal arguing. Be more interesting.

Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#63

Post by napoleond »

mattzito wrote: post #49
aswanson wrote: post #44

Oracle writes shitty 'enterprise apps' (god I hate that phrase) that they sell to big companies, because their salesmen/women wear great attire and are good at mirroring dumb ceos/cios, like the ones that run several companies I have worked for. Will someone please end this nonsense? At what point does usability/stability/utility become factors?

This is a viewpoint that I hear a lot, mostly from people who are not in the room when these grand enterprise implementation decisions are made. While it's true that a good salesperson can make a difference in winning a deal vs. another vendor, salespeople almost never convince a company that they need a big enterprise software platform. 95% of the time, the company has already decided that the current way they do X…

Wow, that was beautiful. And dead on.

Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#64

Post by ChuckMcM »

Of course one could see it as "IT's revenge" after Scott McNealy so famously said it was dead. There is a lot of power to be had by creating an interface for the customer and then keeping everything behind that interface 'obscure'. They have to have that interface to survive, and if they don't know what goes on behind it they have no way of discerning outrageous costs from reasonable ones. The current exemplar seems to be medical costs.

Back in the 60's there was this chamber of secrets called "the Machine Room" which had the "Mainframe" and various and sundry high priests who went in and out, and if you literally played your cards, as in punched cards, right you could get a report on how sales or manufacturing was doing this month.

That got lost when everyone had a PC on their desk, and now some folks are trying to reclaim it :-)

That said the article is still poorly argued. The cost of data management is fairly high. And generally a big chunk of that cost is the cost of specialists who provide business 'continuance' which is code for "makes sure that you can always get your data when you need it, and you can get the answers you need from it in a timely and repeatable fashion." That hasn't changed at all, and whether you have some youngster doing "IT" on the creaky Windows 2000 machine running Back Office or you are using a SaaS company like Salesforce.com, data management is and will continue to be a mission critical part of staying in business.

Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#65

Post by coldtea »

joseph_cooney wrote: post #55
dvse wrote: post #37

Earlier quoted context omitted.

The model in question is "convince small and medium businesses that they need to buy my software in order to do things the same way as large companies X and Y and have a hope of remaining competitive". For Oracle X and Y were banks, retail and logistics companies, for the new generation of "big data" vendors it is Google and Facebook.

But the poster in question didn't say "X" and "Y" did they...this feels like an exercise in pedantry now, but he really did say "exactly" and "google".

Ok, here's some "conversational language" insight:

He said: "Exactly this business model" -- that is, as it pertains to it's essence.

NOT to be read as:

"Exactly this business model as it pertains to inconsequential details, like which big company they should be imitating".

Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#67

Post by christopheraden »

userbee wrote: post #40
christopheraden wrote: post #30

Earlier quoted context omitted.

Statistics involves checking modeling assumptions. A lot of what I've seen with the big data people is the repetition of algorithms to the exclusion of understanding and checking modeling assumptions. While it's nice that the big data craze is making statistics more popular in the mainstream press, it is important that statistics does not become just an application of numerical methods without consideration of underl…

"Big data" also checks model assumptions, if only if by monitoring whether or not acting on the information moves a business metric. Statistics involves inference over prediction, but either one when done right validates assumptions. By the way big data will sit on your face for days.

I meant checking assumptions not just to see whether the use of the big data moved a business metric, but also that the model makes sense from a statistical perspective.

A lot of statistics in business does not bother to check modeling assumptions. Models are chosen based on whether they've been used in the past and what the team is familiar with.

I don't doubt that big data (as we call it now) will one day rule. Ronald Fisher would keel over if he saw the size of datasets we work with nonchalantly on a daily basis. 50 data points (the size of the Iris data) is laughable these days.

My reservation with big data is that the technologies are often unnecessary for the size of the tasks being done. Other than a few data scientists working on truly large projects, most of the big data talk I hear comes from people who aren't fighting in the trenches (execs, marketing, journalists).

Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#68

Post by cbsmith »

dewitt wrote: post #19

If I ever want to get rich, I'll set up shop convincing small businesses they need to do things the way Google does, if only they want to remain competitive. Oracle has used exactly this business model to great success, and obscene profit, for over 30 years.

I would argue Oracle's model has been to suggest to businesses that they need to do things quite differently from the way Google does...

Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#69

Post by fiatmoney »

christopheraden wrote: post #30
philip1209 wrote: post #11

I think that big data has made math sexy, and selling applied statistics and operations research to small and medium-sized businesses under the guise of "big data" with the intention of providing applied mathematical tools is what is happening in the market.

Statistics involves checking modeling assumptions. A lot of what I've seen with the big data people is the repetition of algorithms to the exclusion of understanding and checking modeling assumptions. While it's nice that the big data craze is making statistics more popular in the mainstream press, it is important that statistics does not become just an application of numerical methods without consideration of underl…

There are a huge number of useful machine learning techniques that don't have checkable "modelling assumptions" per se, just good performance on given tasks (decision trees for instance are really difficult to think about in terms of underlying statistical properties). Heck, even most statistical models are demonstrably false for any given application, yet simultaneously very useful.

Re: Most data isn’t “big,” and businesses are wasting money pretending it is

#70

Post by jiggy2011 »

dinkumthinkum wrote: post #53
kamaal wrote: post #50

As some one who is currently dealing with these sort of things I can tell this article hits the nail on its head. Most, heck something like 99.99% of all so-called big data I've dealt is something I wouldn't even classify as small data. I've seen data feeds in KB's sent over to be handled in as big data. It happens all the time. A simple data problem sufficient enough to be easily solved on something like a small db…

In some ways, it kind of is a sham. I think it is perpetuated by the blog/youtube style programming knowledge transference paradigm. Those mediums are fine but there seems to be a rallying cry against actually learning anything about computing in anything other than bite size pieces and thus get a lot of fad driven movements and an over population of redundant frameworks and libraries.

Yes, and then because companies start using NoSQL or whatever for problems they could have done fine in mysql they start asking for NoSQL experts when they are hiring.

This makes devs think that they need NoSQL experience and therefor they will find ways to shoehorn NoSQL into whatever problems they are currently solving.

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