[lvoudour]

I think the title of the article does not accurately reflect the author's thesis, which is more like "trending tech may not be the best solution to your problem". I agree that riding the hype train instead of using established "mundane" tools is probably a bad idea for most problems, but exploring existing tech that might help you also qualifies as "tech-driven problem-solving" IMHO

[marktangotango]

I've worked at a large financial services company and a large medical billing company who both thought they could use big data and machine learning to monetize customers(!) data. Niether had expertise with either. One muddled through with local talent, the other brought in a ph.d ML guy. Both spent millions and got nothing in return. Anecedotal, but representative I believe.

[Systemic33]

EDIT: Completely agree with your comment, and I've had similar anecdotal experiences.

1st rule of Big Data Club

Decide what knowledge you want to get — Then explore how you can generate information that can support this give you this knowledge, and finally you can begin to exploit the data you have at your disposal to generate this information.

The trick is not to generate lots of data, but to know how to extract information from it. Big Data™ Tools can assist in exploiting the data you have, to generate new more meaningful data, but that doesn't make it information, it's just more data.

Note that I distinguish between knowledge, information, and data.

[lvoudour]

I don't know how big those companies are, but they may qualify for big data if they have millions of users or enormous datasets to analyze. The question is, what's the advantage compared to other methods? Is being "left behind" a valid concern for the company's business or is it just a teenager's version of "left behind"?

[gaius]

The fundamental problem of Big Data is that the low hanging fruit has already been picked. Geezers (in a non gender specific sense) with 30 years in industry X have already either intuited it, or it has been uncovered by traditional KDD techniques. What do Big Data, Machine Learning or Data Science have to offer there? The remaining fruit may be very high indeed, and not economically pickable anyway...

[tixocloud]

I'm in the same club as you: Financial Services and Insurance.

One of the biggest challenges we had was introducing all kinds of new technology but minimal impact on bottom-line. In fact, it probably caused a great deal of productivity and cultural issues that just slows everything down.

[todayispotato]

You might be right. I do like my title more than yours though, it's more catchy!

Regarding your point however, I think you might have missed my point by a little bit, so I'll try to clarify.

I'm not advocating for existing tech. I love new technologies, and they're often a better fit than what came before. What I'm advocating for is that instead of spending time and money on investigating new technologies and trying to find problems you could solve with them, you instead spend time on finding and identifying problems that your organization is running into, or developing a vision on where you want your company to be in 5+ years.

I believe that the best and most suitable technology is always already out there, as long as you know what you need to build.

The thing is of course that looking at new technologies is easy, and identifying worthwhile problems to solve is very, very hard. But that shouldn't stop us from considering that way of thinking.