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On Do You Really Need a "Sexy" Data Scientist?
Well, you know what statistics is - a form of measurement used with noisy data. It's an element of thoughtful research, but not the end of the process. A kiss is just a kiss, a sigh is just a sigh, and a correlation is just a correlation.
On Do You Really Need a "Sexy" Data Scientist?
If I dredged up some data indicating that men are more successful in their careers than women, something indicating that they earn more, get hired sooner, get promoted quicker, and so forth, would that be a good case for hiring only men? How about if the data suggested that whites are more successful than blacks? Would it then make sense to recruit only white men? I'm certain you understand the flaws in that line of reasoning, and that you see the similarities to the attractiveness requirement.
On CEOs: Hold Your Team Accountable for Data Analysis
Is there actually evidence that JC Penney does not use data in its decision making?
On Big Data Success Stories: Take Them with a Grain of Salt
It's true, many analytics programs and projects fail. I've written and spoken on this topic often.
That's not to say that analytics, Big Data analytics or any other kind, can't be successful. The problem is almost always that the investment is made fast and furious without a decent plan for success. Often there are not even any success criteria established. How these investments get approved is a mystery to me.
Why would anyone pick on someone for talking honestly about the fact that failure is possible, and common? This is something we all need to acknowledge in order to help us plan and avoid pitfalls. Perhaps the word "thousands" was a bit of hyperbole, but is that a big deal? As for the criticism that the author works for Teradata - he's up front about that, it's in the profile right on the page. What exactly is suspicious about this message coming from someone who works for a vendor? Are people going to hand over their bank accounts because he warns them that projects often fail?
If we don't get the hype out of the analytics biz, then many businesses are going to waste money and conclude that analytics is just another meaningless business fad. Give Paul credit for having the guts to frankly admit that failure is possible.
On So, You Want To Be A Data Scientist?
Lillian, it's very interesting to hear from an environmental engineer, one specialty I don't think we've seen on Smart Data Collective before. I'm in total agreement withyou on the value of knowing something about the field you're investigating.
Here's my own take on the same topic:
So You Want to be a Data Analyst
http://bit.ly/smartdata021
On Technologies and Analyses in CBS’ Person of Interest
You know, Paul, there are police forces that actually do something similar to this today. Rather than predicting individual crimes and victims, police data miners have identified times and locations where the risk of crime is high. By targeting resources to those hot spots, crimes are prevented.
Here's an article on this topic from Police Chief magazine:
Colleen McCue, Ph.D., Program Manager, Crime Analysis Unit,and Colonel Andre' Parker, Chief of Police, Richmond Police Department, Richmond, Virginia
http://bit.ly/VIM6AJ
On Arguing for Increased Gut-feel in the Age of Analytics
Mike,
This is a great explanation of the limits of gut-feel decision making. I'm going to quote you left, right and sideways.
Meta Brown
On Big Data Blasphemy: Why Sample?
Paige,
It is intriguing that you remark on my vendors having me well-trained. Let's make sure that we have full disclosure for readers here on background and connections to vendors.
I am currently employed as General Manager of Analytics at LinguaSys, a software vendor specialized in language processing such as text analytics, including Big Data applications. In the past, I have been an independent analytics consultant (and continue to do a limited amount of consulting), an in-house statistician and an engineer (not a software engineer, a pipes, valves and steam engineer). For many years, I was employed at another software vendor, SPSS, which has since been acquired by IBM. In addition to the products developed by my employers, I have used a variety of other statistical analysis tools.
My training in sampling methods came not from any vendor, but from a university. I completed around a dozen classes in statistical theory and applications as part of my training for a BS in mathematics, and later expanded on that while earning a Masters in Engineering. This training was not heavily influenced by software vendors as academics rarely touched software in those days. It was done the old fashioned way with detailed discussion of theorems, derivation of proofs, and lots of manual calculations. Other than the occasional use of a hand calculator, we did not use automated computing for statistics at all.
Now, Paige, would you care to make similar disclosures about your own loyalties and training?
My current and past employers could sometimes make a heap more money in the short term if I advised prospective customers to do things your way. I'm proud to say that no employer of mine has ever encouraged such behavior. They don't encourage it because they are smart enough to know that there are better strategies for the customer, and that it's good business to offer solid, cost-effective, long-term solutions.
On Text Analytics Is Hard (That’s What She Said)
You just made my day, Stephen.
Please share stories of your progress with others - we can all learn from them!
On Text Analytics, Big Data and the Keys to ROI
Glad to know you liked it, Doug.
As for it taking a while for the 3 Vs to catch on, don't feel bad. Think of all the great artists who didn't get popular until they weren't around to enjoy it!
On Text Analytics Is Hard (That’s What She Said)
Remarkable how what seems like just an academic exercise can offer practical lessons with immediate applications.
And laughs, too. What more could we ask?
On Do You Really Need a "Sexy" Data Scientist?
You may dare, but you'd prove nothing. Human bias toward attractive people doesn't equate to causation of success for an analyst. You could say the same for gender or race. That doesn't make gender or race a cause of success in itself - bias, cultural behaviors are major determinants of things like hiring and promotions.
Indeed, hiring, promotions and so forth tell us little about successful data [science, analysis, or whatever you may call it]. The object of the job is to produce actionable analysis that yields good returns for the employer. Measure that.