Category Archives: Miscellaneous

Keep Your Big Data. Big Brains Will Win in the End.

I have to admit that I’m sick of all this hype about big data and how it is the answer to all our problems. As I’ve said again and again, there’s no such thing as big data in business. Relative to our ability to process it, data has always been big. And, in business, big has always been meaningless. Furthermore, in business, we’ve always been able to process as much data as we need to in reasonable amounts of time if we made good technology decisions.

And I’m even sicker of the fact that some people think we can replace science with math and processes with computer programs. We never could, and for the foreseeable future, where AI (artificial intelligence) will not be a reality, we can’t. Thinking like this is what causes economists to latch onto, and promote, financial policies that, seem good in theory but, in practice, result in economic collapse when taken to extremes.

The reality is that science can never be replaced by math and automated prediction. Not only is the author of this HBR blog post on “why data will never replace thinking” right when he says that it’s only by trying to come up with our stories (hypothesis) beforehand, then testing them, that we can reliably learn the lessons of our experiences — and our data, but it’s only by coming up with hypothesis, and putting plans into actions that we can beat the competition and gain market share in the global market. Look at the giants of industry today. Did Apple become the dominant first in the e-Music industry by letting Microsoft, Sony, Samsung, etc. develop their music players and music stores first, analyzing customer responses, and then introducing their offering? Or did they become the dominant force by using their brains to try and figure out what the market, and customers, were missing, using the best creative and engineering talent to design a solution, and then releasing that product on the market? It was the latter solution — the solution that required big brains that won the market. Similarly, Walmart became the biggest retailer not by asking consumers want they wanted, but by predicting what the average consumer really wanted — a one-stop department store that met most of their basic needs at low prices with a consistent product and service offering across each store for the mobile consumer.

This isn’t to say that data isn’t important, it is, just that it won’t solve all your problems and that, beyond a certain point, more data doesn’t help. Remember, statistically speaking, you only need 384 data points to have 95% confidence with a confidence interval of 5 on a population of 1,000,000. If you want a confidence interval of 3, you only need 1,066 data points, and if you want a confidence interval of 1, you only need 9,513. Beyond a certain point, more data doesn’t add much confidence and the only way you’re going to get more insight is to see it inside your head.

So keep your big data. I’ll use my brain instead. How about you?

Why A True Supply Management Professional will Never be Replaced by Technology

As succinctly stated in this recent HBR headline, Algorithms Don’t Feel, People Do.

Also, algorithms don’t sense, read non-verbal cues, detect patterns in seemingly unrelated data, take risks, or form common bonds. They don’t feel, and they aren’t intelligent. And while their predictive capabilities are scary given enough data, they are not infallible, and when they do fail, they will fail in a big way. Let’s address these points one by one.

First of all, as noted by the author of the HBR article, algorithms don’t feel, and can’t predict how a person will respond to a message. Marshall McLuhan may have stated that the medium is the message, implying that the form of a medium embeds itself in the message and influences how a person will receive the message, but the reality is that, in today’s individualistic society, the message is what is interpreted by the recipient, and only someone with a shared understanding will be able to comprehend what that is and react accordingly. As a result, an algorithm can not negotiate.

Successful negotiation depends on a first party transmitting a message, agreeable to that first party, that the second party can accept, and, moreover, figuring out, of all of the possible messages that the second party might accept, which subset represent message that the second party are most likely to accept and which messages of the subset are the least distant from the desired message. An algorithm can compute which options are likely given certain assumptions, and which of these options are the least distance according to some metric, but cannot determine what assumptions to make. Only a person who can feel, and feel what the other party is feeling, can be the judge of what good assumptions are. And, secondly, algorithms cannot sense. They don’t feel, and they don’t have instinct — which requires real intelligence.

Thirdly, they can’t read non-verbal cues. Even if someone is stating that they may be agreeable to an offer, the reality is that they may have no intention of ever accepting the offer, and are only indicating the contrary to stall for more time. It’s often the case that such a person is not as good at masking their demeanor as they are at masking their words. It might be the case that their non-verbal cues give more away than they would like. Only a trained negotiator with years of experience and instinct can be the judge of this.

But even more importantly, they can’t detect patterns in unrelated data, as it’s typically the case they can only process specified data in specified ways. And a fixed data pool never tells the whole story. A fixed algorithm might not know that a fire today will impact resource availability in six months, that your main competitor is likely to go out of business do a massive loss in a patent infringement lawsuit, or that a new technology is going to make the current technology obsolete in 18 months, with prices and demand starting to plummet in six months. As a result, in each of these instances, the algorithm would buy (today) (at a much) higher (price) than it needs to.

Furthermore, algorithms don’t understand when to “trust your gut” and take a calculated risk such as betting the farm on a new technology or riding the spot-buy market when all signs point to locking in a price for three years. The reality is that real success often requires risk, and only a true pro will know when such a risk should be taken.

Finally, as algorithms are not intelligent, they don’t form common bonds with like-minded algorithms that would help them advance their company and their profession. Algorithms have their place, and properly used can take a great deal of tactical and low-value workload off of a Supply Management professional’s plate, but algorithms will never be smart enough to handle the strategic and high-value workloads without intelligent — human — supervision. Optimal is only optimal if all of the assumptions are valid and modelled. An expert will always be needed to define the assumptions, check the assumptions, verify the results, and tweak them according to an ever-changing Supply Management world.

In short, good technology can make you two, ten, and maybe even one hundred times more productive (depending on the metric), but it cannot replace you. So don’t be scared of new technology for your supply chain — embrace it. Given the ever-increasing demands being placed upon you, you will be glad that you did!

If You Really Want to Future-Proof Your Career

Join the world’s second oldest profession!

A recent guest post over on VentureBeat on your career, future-proofed notes that industries that once were dominant are long in decline and that nascent sectors are ascendant and will likely shape everything that lies ahead. This includes the careers that will be available to you. Given that you don’t want to be among the 23% of workers who are unemployed to some degree, according to the latest shadow statistics that includes long-term discouraged workers not included in the BLS U-6 unemployment rate that includes short-term discouraged and marginally-attached workers (which is close to 15%), it’s important to do what you can to “future-proof” your career. (Source: ShadowStats)

To this end, the author of the guest post gives you four simple rules that he believes will help you win your future. They are:

Take Risks – Big Ones

If you see your industry on the fast-track to the doghouse, try something new, even if it means working half a world away for a while.

Take All Opportunities

Never shy away from any event, trip, assignment, or project they want to give you that will expand your experience, horizon, and opportunity.

Go Where the Future Is

Even though we had the dot-com crash, the future still lies in tech and healthcare, but tech will be more than just over-hyped software companies. And it will be most prevalent in healthcare as the population continues to age and wants to live longer and better.

Think Beyond

Whatever we’re using today we won’t be using in 10 years, or at least not in the same form. Don’t get comfortable. Look to what is coming next and prepare for it.


These are great pieces of advice, but if you really want to future proof your career, all you have to do is take up the world’s second oldest profession – Procurement! Whether you call it purchasing, sourcing, or supply management, it’s based on buying, and there has been buying at least since the ancient Anatolians were trading obsidian circa the 9th century BC. We’re a consumer culture that constantly needs to trade (money, or equivalent) for what we need, so buying is never going to go away. Paper money might, as we replace it with digital bits, but the concept won’t.

Plus, being in Procurement implies that you will have the opportunity to take risks (and will have to if you want to stand out and advance your career quickly), expand your experience (as you will have lots of opportunities to travel to suppliers around the globe and work with them), go where the future is (as there is always a big focus on emerging markets), and think beyond — as the only real way to create lasting value is to continually innovate beyond the norm.