Race Against the Machine

  • April 24, 2013
  • I can’t tell you how many emerging analytics companies have contacted me since January.  Every day it seems there is another company — smelling blood in the proverbial water — wanting to brief me.

    I know why.  Now that Big Data questions have transitioned from “how do we store all this stuff?” and “what’s valuable in this pile?” to “how can we slice and dice this raw material,” everybody wants a piece of the action.

    I’ve been preparing you for this for the last couple of weeks. You may recall my recent post about the relentless doubling of Moore’s Law and a new book, Race Against the Machine that discusses how our world is changing now that we have significant computing power.

    You may also recall my last post about how knowledge is doubling at a ridiculous rate — by 2014 it’ll be doubling every 11 hours, as forecast by IBM. It’s no surprise, then, that analytics companies are as numerous as chocolate bunnies at Easter.

    Here’s the tricky part: A typical early market starts out with messaging about the technology. It lets you do this or that with the data. Later on the messaging includes real quantifiable benefits that you can literally take to the bank in the form of ROI.

    Then the ultimate validation numbers start rolling in and they are usually in the form of double-digit percentage increases and they don’t depend on return on investment because they are so fundamental that they stand on their own.

    From what I see, this isn’t going to happen for this analytics wave. I suspect that there might never be a roll out like that again.

    Analytics have been around for a long time and there have actually been several false starts. There have been times when analytics looked like a sure solution to many company ailments.

    Some analytics vendors did quite well in that era — keep in mind that SAS is a multibillion-dollar company and it is still privately held. Did I mention that it’s about 30 years old?

    Analytics, however, failed to break through because there was always something sexier and easier to understand coming out at the same time.

    Back then, analytics suffered from the reality that you needed a Ph.D. in statistics to really appreciate the stuff. Most companies have very limited supplies of doctoral candidates. They have many more sales people who need leads, so in the showdown between analytics and CRM, analytics always won.

    Then you should consider ROI and what SaaS computing has done to it. SaaS turns big investments into small numbers, so the division calculating savings per dollar invested no longer gets the attention it deserves. That means ROI as a driver just isn’t what it used to be.

    For analytics to be successful this time — and we really need it to be successful — several things have to fall into place.

    It has to be dead solid easy for mere mortals to use analytics. That means each of us needs to up our game. The jobs of tomorrow will be based on the ability to make sense of metrics and probability. That’s not a hard lift.

    We also have to get the messaging down, all the way to the fundamental numbers that define it as worth having — period. We need to do that post haste.

    Most vendors I’ve seen don’t have that picture in mind yet. They talk about their products like they are ends in themselves, instead of being means to the ends of profit and cost abatement. The best way to get the messaging right is to quit talking about ROI and begin talking about metrics — specifically the metrics that are germane to my business processes.

    By implication that also means getting comfortable enough with the idea of metrics — that each of us can easily come up with our own now and then because we are the ones closest to the reality that needs to be described.

    Again, that means we both need to be able to punch up an analysis, but we have to be fearless enough to take command and roll our own. Analytics won’t be mainstream until you can do this.

    I think some emerging vendors are going to do that and then we’ll have a horse race.

    This reminds me of the early days of SaaS computing and CRM. Back then there were multiple companies bringing SaaS SFA to market. Most thought about SaaS as simply another delivery method. One company, however, saw SaaS as a revolutionary approach to software and knew in its bones that it would disrupt the whole industry.

    Today there’s only one company from that era still standing and you know who it is.

    Analytics is at a similar tipping point because it’s no longer the thing that the data scientists use to discover interesting things about the business. Analytics is a seminal technology that harnesses Big Iron to digest Big Data to give us the insights we need to compete.

    As Brynjolfsson and McAfee write in Race Against the Machine, it’s one of the things that enable us to play on the second half of the chessboard.  It’s what we have to do — because that’s inevitably where we are going.

    Published: 5 years ago


    Erik Brynjolfsson and Andrew McAfee of the MIT Center for Digital Business and the Sloan School of Management have written an interesting book for our times — our economic times — with an appealing metaphor that any technologist will appreciate. Race Against The Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy, is short and to the point and it ought to be required reading.

    The subject matter is employment growth or its lack in this rather austere recovery and the effect on future employment and growth.  More specifically, it is about the changing relationship between humans and their creation, the computer — the almost-thinking-machine — and how it can out-compete its masters not only in routine manufacturing tasks but, increasingly, in jobs that were once thought to be the exclusive province of human thinking.

    The metaphor, from futurist Ray Kurzweil, holds the narrative together and is worth pondering before we continue.  It is told in the form of a story about the invention of chess.  The emperor, the story goes, was so delighted with the game that he gave its inventor a wish.  The sly inventor asked for a grain of rice to be placed on one square of the chessboard two on the second and double the prior square’s total on each succeeding square.

    It is the story of exponential growth.  Accumulating the sums of rice on the first half of the chessboard was manageable but the second half totals were truly significant, from small beginnings arose a mountain of rice that would dwarf Mr. Everest.  McAfee and Brynjolfsson apply Kurzweil’s story to another runaway exponential progression, Moore’s Law.  You may not need to be reminded that Moore suggested that computing power would double and its cost halve every 12 months or so.  With some fine-tuning the period was raised to 18 months and has continued for virtually the lives of all people in the technology industry today.

    But that’s not the crucial part of the story or the book.  The authors calculate that we have only recently (in the last few years) crossed over from the first 32 squares of the chessboard into the second half where a metaphorical Everest awaits us.  The gains in the second half of the chessboard will likely come from advanced software and algorithms and not hardware per se.  They point out that while computing power has increased one thousand fold on the first half of the chessboard, the power and quality of our algorithms has increased 43,000 fold.

    The chessboard’s second half is already giving us systems that can diagnose better than doctors, out lawyer lawyers and, of course, kick booty in Jeopardy!.  So what will happen next?  McAfee and Brynjolfsson are quick to point out that the thinking that machines do is not the thinking of humans.  It is often the lightening brute force effort of crunching a great deal of data to ferret out an answer.  Also, training a machine to pick up a pencil from a random table top, let alone use it, is still elusive.

    The major point of The Race Against the Machine, is that there really is no race, or if you think there is be prepared to lose.  But this book is fundamentally hopeful because it suggests that machines are tools that ought to off load people from their rote tasks to concentrate on the creative, entrepreneurial and innovative endeavors that only humans can engage in.

    What’s powerful about this argument is that it offers a prescription for a solution:  Leverage the machine rather than fight it.  Crunch big data in every way you can imagine, ask “What if” questions ad nauseam and above all, innovate.

    As I look at CRM and the broader technology world, it seems to me that the subdivided chessboard is visible everywhere.  What we once called innovation was surely innovative but it was really no more than automation of what existed before.  True innovation starts on square 33 when we realize that all the automation is mostly behind us and innovation means making totally new concepts.

    McAfee and Brynjolfsson speculate that we reached square 33 in the middle of the last decade.  If true, one of the first true innovations we all witnessed was the social media revolution.  Combined with the mobile revolution and today’s quest to master Big Data, we have a potent nucleus on which to invent new businesses, models, and processes.

    And if we combine what we know about the chessboard and where we are on it with what we know about our industry we can clearly see that some vendors are simply automating on the first half of the board while others are innovating on the second half.

    Let’s not fool ourselves though, simply innovating on the second half of the chessboard is no guarantee of success, just as always, bad ideas will still yield bad results.  But it is also true that failing to try, to enter the second half is a sure route to oblivion.  This is not simply a matter for the tech sector or even the nation.  It’s a large scale economic issue that will affect our species.

    Historians and others often debate when specific eras start, because they don’t often follow calendars and precise dates.  Some people say that the twentieth century started in 1890 with the closing of the American frontier, for instance.  With this as a guide and McAfee and Brynjolfsson’s fine and short book as context, I’d say the twenty-first century started in about 2006.

    Published: 5 years ago