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Big Data's Big Problem: Little Talent

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Re: Big Data's Big Problem: Little Talent

#31

Post by tgflynn »

csomar wrote: post #28
tgflynn wrote: post #9

Earlier quoted context omitted.

This technology will change the world more than the Internet or any other technology in human history. You're right that adoption is very slow. I'm convinced that businesses could save trillions of dollars by applying existing weak AI to their problems. Why aren't they doing it ? For one thing there's a huge gulf between the average business person's understanding of what is possible and what actually is. On the othe…

I have had a website and a LinkedIn profile for over a year that make this fairly clear It doesn't work like that. You LinkedIn profile might easily land you any job in Software development, but not consulting. In my opinion, if you want to do consulting for big corp. you should figure out what it takes to it. An attractive website and presentation, few buzzwords, client testimonials, business cards, and the other bl…

That's definitely not my world and one reason I left big-corp in the first place.

EDIT ADDED: It seems like a really broken market if buyer decisions are completely orthogonal to the product being purchased.

Re: Big Data's Big Problem: Little Talent

#33

Post by NyxWulf »

pgroves wrote: post #29
NyxWulf wrote: post #16

Earlier quoted context omitted.

As someone in the big data field on the ground (VP of Engineering). Let me give you my thoughts on it. Your impression about the hype is correct. There are a lot of vendors offering BIG solutions, if you pay them BIGGER money. Where I used to translate the word enterprise to $$, now I translate Big Data to $$$$$$$. When I'm hiring, I don't go looking for Big Data people, because generally they don't exist. Statistics…

deadline because for 500th time someone promised something impossible to the client This is a killer in machine learning applications. The toolsets rarely cover the entire extent of what needs to be done, so at least some custom code needs to be written. But results aren't deterministic - you don't really know if it's going to work until you run it. Several iterations are often needed to get to the first useable resu…

I couldn't agree more. Accuracy is a problem, variation is another problem. Dealing with layers in the business who have no math or statistics background but very strong opinions is yet another complication.

These types of conversations aren't uncommon.

Other - "I need you to prove our stuff does X, Y, and Z".

Me - "Ok.."

Me - "Ok the data shows our stuff does X but Y and Z are just random noise"

Other - "We ran it once before with this other guy and it showed our stuff did X,Y and Z. We've been promising it to our clients for a year. He gave us several examples, but when the clients asked to see the underlying data he couldn't produce it. So we just need you to prove it does X,Y, and Z."

Me - "The data only shows it does X. Y and Z are impacted positively through X, but once you condition on X, Y and Z are not causally affected by our stuff"

Other - "Yeah...well I promised client we would give them a report by {{insert random ridiculous date here}} proving it did X, Y and Z. We are going to lose them if we don't deliver a report saying that"

Me - trying for the 50th time to explain they shouldn't promise a positive result when we've never looked at the data.

There are hundreds of variations on this conversation. Your code is wrong is one variant (which depending on the timeline is hard to dispute). Of course if you take long enough that your code is correct, then you are going to slow. This isn't a science experiment, just make it work is another. Watching someone go slack jawed and start drooling because you accidentally used a math term is always interesting.

I have a whole new perspective of being on the cutting edge. It seems like it mostly means you are on the cutting edge of comments from people who don't know how ridiculously hard what you are doing is.

Re: Big Data's Big Problem: Little Talent

#34

Post by BrianLy »

csomar wrote: post #28
tgflynn wrote: post #9

Earlier quoted context omitted.

This technology will change the world more than the Internet or any other technology in human history. You're right that adoption is very slow. I'm convinced that businesses could save trillions of dollars by applying existing weak AI to their problems. Why aren't they doing it ? For one thing there's a huge gulf between the average business person's understanding of what is possible and what actually is. On the othe…

I have had a website and a LinkedIn profile for over a year that make this fairly clear It doesn't work like that. You LinkedIn profile might easily land you any job in Software development, but not consulting. In my opinion, if you want to do consulting for big corp. you should figure out what it takes to it. An attractive website and presentation, few buzzwords, client testimonials, business cards, and the other bl…

I think you can go further than that. To get people asking for your time as a consultant you have to demonstrate experience and get close to vendors who already support clients you are interested in. For example, targeting a niche "big data" problem with a particular tool, and then developing a relationship with the community supporting that tool. That gives you access to the people who are looking for consulting.

Re: Big Data's Big Problem: Little Talent

#35

Post by tgflynn »

pgroves wrote: post #29
NyxWulf wrote: post #16

Earlier quoted context omitted.

As someone in the big data field on the ground (VP of Engineering). Let me give you my thoughts on it. Your impression about the hype is correct. There are a lot of vendors offering BIG solutions, if you pay them BIGGER money. Where I used to translate the word enterprise to $$, now I translate Big Data to $$$$$$$. When I'm hiring, I don't go looking for Big Data people, because generally they don't exist. Statistics…

deadline because for 500th time someone promised something impossible to the client This is a killer in machine learning applications. The toolsets rarely cover the entire extent of what needs to be done, so at least some custom code needs to be written. But results aren't deterministic - you don't really know if it's going to work until you run it. Several iterations are often needed to get to the first useable resu…

The company I used to work for had a performance based product. They only got payed if they actually showed improved accuracy against a given evaluation set. Then they got a fraction of the cost savings (say 1 year's worth).

This seems like it could be a good model for machine learning consulting, and one that I would certainly be willing to explore.

It would work something like this :

  1) You show me your problem and your data.  

  2) We  come to an agreement on how accuracy would   translate
into financial results and on a fair split of the savings or earnings.

  3) I develop a model.

  4) You evaluate it based on 2.

  5) I get payed based on 2.
If my model doesn't meet minimum performance criteria I don't get payed. If it does very well, and assuming the problem was economically interesting in the first place, you save a lot of money and I get a fair sized chunk of it.

Feel free to explain why this business model wouldn't work.

Edited for formatting.

Re: Big Data's Big Problem: Little Talent

#36

Post by fauigerzigerk »

Dn_Ab wrote: post #11
disgruntledphd2 wrote: post #5

Earlier quoted context omitted.

I completely take most of your points, but I think that pretty much all quantitative PhD's are going to be close to "data scientists". Given that stats and explaining your research are requirements, all that's left is to train them to program, which a lot of people are already doing. As a matter of fact, since I heard about this big data stuff I've been honing my skills in this area, in case the hype actually manifes…

_delirium's post acknowledges your point but is looking for an even rarer person: "There could be more of them in the future, but someone who is top-notch at all of statistics, programming, and data-presentation has long been less common than someone who's good at one or two of those" . Someone that can program, understands statistics and can present the data in an appealing manner without losing significant fidelity…

I agree, and it's all the more true if you consider that "presenting" data may actually be more like creating an interactive environment to explore data.

I believe that data analysis yields the best results when perusing the data and tuning the models are closely connected tasks.

Re: Big Data's Big Problem: Little Talent

#37

Post by kds »

mjw wrote: post #7

As an engineer who's investing in developing "deep expertise in statistics and machine learning" I can only stand to benefit from it, but something about the current wave of Big Data hype makes me instinctively a bit wary. Does this skills shortage really exist to the extent claimed? are there really enough people out there who would know what to do with a 'data scientist' if they were able to hire one? I see more ta…

I felt a similar kind of skepticism when I saw it took ~3 years to improve the Netflix recommendation system with just ~10% - in the context of the Netflix Prize, with great minds (data scientists and practitioners) participating and collaborating.

Maybe the initial system was quite good and it had no space for easy-and-fast enhancements, I don't know.

But 10% overall improvement result in 3 years (just as quantitative ratio, esp. if it translates directly to the same growth pattern in financial revenues) is something that makes the business types yawning.

Re: Big Data's Big Problem: Little Talent

#38

Post by NyxWulf »

tgflynn wrote: post #35
pgroves wrote: post #29

Earlier quoted context omitted.

deadline because for 500th time someone promised something impossible to the client This is a killer in machine learning applications. The toolsets rarely cover the entire extent of what needs to be done, so at least some custom code needs to be written. But results aren't deterministic - you don't really know if it's going to work until you run it. Several iterations are often needed to get to the first useable resu…

The company I used to work for had a performance based product. They only got payed if they actually showed improved accuracy against a given evaluation set. Then they got a fraction of the cost savings (say 1 year's worth). This seems like it could be a good model for machine learning consulting, and one that I would certainly be willing to explore. It would work something like this : 1) You show me your problem and…

Most business people aren't interested in model accuracy as a term. They want something that provides benefit, e.g. cost savings, increased revenue, increased profits, etc.

The sales process of convincing someone they need an accurate model is tough, especially because robust models are time consuming and expensive to build.

If you can come up with a model that shows good results, and people know they need those results, then you can start a company selling either a service or product to get those results. If people don't know they need your results - then you have to educate them, in which case it's a much more difficult business to start.

I don't know many business people with the temperament, understanding, or the pocket book to deal with general research type problems.

Re: Big Data's Big Problem: Little Talent

#39

Post by tgflynn »

kds wrote: post #37
mjw wrote: post #7

As an engineer who's investing in developing "deep expertise in statistics and machine learning" I can only stand to benefit from it, but something about the current wave of Big Data hype makes me instinctively a bit wary. Does this skills shortage really exist to the extent claimed? are there really enough people out there who would know what to do with a 'data scientist' if they were able to hire one? I see more ta…

I felt a similar kind of skepticism when I saw it took ~3 years to improve the Netflix recommendation system with just ~10% - in the context of the Netflix Prize, with great minds (data scientists and practitioners) participating and collaborating. Maybe the initial system was quite good and it had no space for easy-and-fast enhancements, I don't know. But 10% overall improvement result in 3 years (just as quantitati…

But what if 10% improvement means 10 M$/year ?

Anyway I think there are many applications where getting the absolute best performance isn't as important as finding the problem, figuring out how to apply a machine learning model to it (which includes getting the necessary training data) then training an off the shelf mode. The later of these may take a day or less, the other phases may well require both more thought and more effort.

Re: Big Data's Big Problem: Little Talent

#40

Post by dbecker »

tgflynn wrote: post #9
throwaway1979 wrote: post #4

Earlier quoted context omitted.

You're absolutely right. That said, I don't buy that big-data is as revolutionary as the Internet. While in theory, every single business can collect data and optimize based on what they see, this is way too complex for most businesses to deal with. While big data has certainly been critical for the business model of ad-based startups, I don't see it being used in other industries. People keep alluding to data-driven…

This technology will change the world more than the Internet or any other technology in human history. You're right that adoption is very slow. I'm convinced that businesses could save trillions of dollars by applying existing weak AI to their problems. Why aren't they doing it ? For one thing there's a huge gulf between the average business person's understanding of what is possible and what actually is. On the othe…

Whether or not you have these skills: potential employers need to SEE them. There are lots of pretenders out there, and employers are appropriately wary.

Are you showing employers results on your web page that a worse ML practitioner can't match.

Putting results from a kaggle competition on my LinkedIn page landed me my current job (and I am still contacted by potential employers every couple weeks).

The employers of the world aren't stupid, but they aren't omniscient either. So you need to make it easy for them to see that you have the skills you claim.

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