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On 3 Tips to Make You a Genius Forecaster

Richard, appreciate your insights and that you have added your practical experience to the discussion. Bad forecasting is easy, good forecasting is incredibly hard. And you're spot on about the need to "bring your data to the discussion", however I have found the hard part is first understanding what data sets you have available, diagnosing what you're missing to solve your challenge and then sourcing them. Of course, once you source them, the hard work begins (thank God for data scientists). It takes a village to perform good forecasting--and a healthly dose of technology know-how.

Thanks for commenting!


October 14, 2014    View Comment    

On CAPEX Deferred Eventually Makes the Company Sick

For further reading on this topic, here's a recent Financial Times article (free to read but registration required):


September 24, 2014    View Comment    

On Analytics, Semantics & Sense: Q&A with Marie Wallace, IBM

Terrific post Seth, thanks for sharing your expertise and this interview! 

May 3, 2014    View Comment    

On Big Data's Big Flip-Flop

Nice post Bill. For the most part, we humans hate change. Especially when what we have or are using works so well. Nice to see things come full circle with SQL.



March 18, 2014    View Comment    

On Technology Training Needs a Hands-On Approach

Excellent Rajeev - just what the doctor ordered. Thanks for taking the time to comment!

March 12, 2014    View Comment    

On Technology Training Needs a Hands-On Approach

Thanks for commenting Meta! Indeed, it's a poor assumption to think because an individual does not have a college degree in technology X, they can't perform that function. Or because they don't have a MIS degree, they can't be a business analyst.

Granted, it's probably easier to plug in a person with training or experience in techology X. But let's not pass over bright and talented people because they don't have skillset X on their resume - especially a person that can excel in a given role with a bit more training.

March 6, 2014    View Comment    

On When is CAPEX Coming Back?

John, thanks for commenting. You're right, in the past 3-5 years, we've been conditioned to look for and do things on the cheap.  And we're constantly reminded that "less is more", when in reality, "less is often just plain less".

Sometimes we can get away with cheap (talent, HW/SW, development efforts etc), but in most cases it's going to come back to bite us.

February 20, 2014    View Comment    

On When is CAPEX Coming Back?

Kate- thanks for commenting and adding a valuable link.

I have no doubt companies in SMB space will be looking intensely at cloud options, especially since CAPEX is at a premium. That said, for larger companies I actually think CAPEX will return in a big way in the next 4-5 years, especially with the rise of robotics. This has the potential for drastically changing the labor/capital mix—with interesting implications for our global economy at large.  

February 18, 2014    View Comment    

On Debunking Five Cloud Computing Myths

James, thank you for the kind words and commenting on this article!

January 6, 2014    View Comment    

On Will 2014 Bring the Death of the IT Manager?

Nice article Danny. Contrast your post with another from the FT from Luke Johnson. (free but registration required to read)

His opening line sums it up for me; "It is only when a system suffers a failure that techies come into their own." Revenge of the nerds?


December 16, 2013    View Comment    

On Be Wary of the Science of Hiring

Thanks for commenting Mike! I can see that you have a "stake" in the conversation! :)

I am interested in getting some more information, especially case studies. I do believe that "people analytics" will help --as you say--remove some of the bias in hiring decisions, and on the whole help improve the hiring, and talent development process. Removing pre-conceived notions such as these can improve the HR process:

That said, much like anything else, I think we need to be careful and strike the right balance between executive intution and balance in HR analytics.  Examples of getting it wrong --IMO--where you have recruiters/hiring managers solely qualifying candidates by the number of Twitter followers:

Will reach out to connect on Twitter...

December 13, 2013    View Comment    

On Be Wary of the Science of Hiring

Tadd, when you said, “I don't think you can't expect subjective interviewing practices or simply data science by themselves to give you the best solution here.  It has to be a mixture of both." I thought - Bingo!

Sadly, while I think we'd both agree this is common sense, I was disturbed by one recruiter in the article that said his managers want to hire based on scores. I do realize that this is N=1 scenario, but I do think we need to be careful in understanding the algorithm's output presents an incomplete picture of a candidate.  

That said, I'm not convinced that data science can weed out 60-80% of candidates not worth interviewing. In fact, I’m on the fence as to whether this is just too complex a problem for algos. Too many attributes, too many assumptions, and too little understanding of the weighting factors that make up an ideal candidate (of course, my statement depends on the job role). Plus, as one person on Twitter wrote to me; “How do you calculate character?”


 I suppose time will tell if people analytics are more help than hindrance. Thanks for commenting! I appreciate your input/feedback!

December 12, 2013    View Comment