Why build models?

why2We are all modelers.  Whenever you plan, you are building a model.  Whenever you imagine, you are building a model. When you create, write, paint or speak, you first build in your head a model of what you want to accomplish, and then fill in the details with words, movements or other actions in order to realize that model.

Models work via a three-part structure:  Input, Mechanism, and Output.  When we use models, we are generally confident in only two of the three stages, and we use the process to determine the unknown stage.

The most familiar construction is where we know, or have confidence in, our inputs and our mechanism (the mechanism being the rules or algorithm that generates output from inputs), which we call “prediction”.  We use this structure to predict, or forecast, a wide variety of outputs, from tomorrow’s weather to next month’s sales to next year’s election.

But prediction is not the only available model structure.  Read More »

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Big data demands Big quality

data qualityUnlike data acquisition, which can accumulate exponentially, we generally address data error correction on an exception basis, using manual, linearly-scaled resources.  We cannot possibly scale manual data correction to keep up with our increased data volumes, which means we must automate our data quality processes with tools at least as robust as our data collection and storage resources.  We cannot afford to scale up the human resources that today correct perhaps 100 customer name, address, part number or shipment date errors per week, to handle thousands or tens of thousands of such errors.  Our only alternative is to automate and catch / fix those errors up front.

In a previous post (“Big Silos”) I posited three primary sources of big data:  visually dense (e.g. video, satellite), temporally dense (e.g. audio, sensor), and transactions (e.g. POS, SKUs).  I would like to amend that classification to now include a fourth primary source of big data that I had initially overlooked: unstructured data, including social media data.

80% of corporate data is unstructured, making it perhaps the single largest potential source of big data.  As we start to process, structure and store that unstructured data, through techniques such as text analytics and content categorization, we need to remember to apply the same data quality standards as we do to more traditional transactional data. Read More »

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Homo habilis to Homo sapiens – From tool user to data user

Are robots going to put us all out of work?  That seems to be the question on every click-bait economic article’s mind.   On this particular issue I’m taking a firm position:  NO! Emphatically, NO!  Robots are not going to take our jobs in any permanent sense that would entail a total reworking of basic human economics.

How can I be so certain?  Because we’ve been through this before on an even greater scale and not only survived but largely thrived.  I am of course talking about the industrial revolutions, both first and second, and whatever atomic / space / computer / information revolution we might be in right now.

tools1-olduvaiWhen speaking of the first and second Industrial Revolutions, we don’t refer to them as “robots” but instead call them machines and equipment.  Still, they are all kith and kin, part of the larger category we refer to as “tools”.  And when it comes to tools, we’ve been at this for 2.8 million years, so chill out everybody.  In fact, the first in line in the genus ‘homo’ was Homo habilis, “handy man/woman”, or ‘tool user’.

However, it’s not the Olduwan flaked stone tools that I’m entering into evidence here, but instead the machinery of every shape and kind that filled the first factories and furnaces of the industrializing West, displacing and disrupting the craft guilds and the farmers, but not resulting in the economic extinction of the species. Prior to the start of the Industrial revolution roughly 80% of Europe’s economy was agrarian.  By 1900 agriculture still accounted for 40-50% of the West’s GDP, and by WWII it was down to 15%.  Today those numbers hover around 2% for Europe and less than 1% for the U.S.

When it comes to the trade guilds, yes, the coopers, cobblers and colliers are long gone, and slightly more recently we’ve seen the demise of lamplighters, ice cutters, milkmen, bowling pin setters and switchboard operators.  And yet here we are. Read More »

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Is Data an Asset? The importance of the metaphors we use for data

The metaphors we choose to describe our data are important, for they can either open up the potential for understanding and insight, or they can limit our ability to effectively extract all the value our data may hold.  Consisting as it does of nothing but electric potentials, or variations in voltages and frequencies, when speaking about data we are forced out of necessity to speak in metaphor.  Even our cherished 1’s and 0’s are metaphors for the underlying electromagnetic reality, and in going from bits to bytes we’re already stacking metaphors on top of metaphors.

Let’s start with a metaphor I must see at least once a week in articles, blogs, white papers and webinars: data as an asset.  As a starting point, I’m okay with this, it seems an apt and accurate metaphor for data.  But let’s nail this “asset” terminology down a bit more.  If data is an asset, what sort of asset might that be?

big_data_linkedin3Is it a fixed asset like plant, property and equipment?  Meaning, is it like the other productive operational assets that can be leveraged as tools?  I suppose one could see it that way, but I find that particular metaphor hard to work with.

Moving higher up the balance sheet – is data like inventory?  As with inventory, we most certainly warehouse data, in, what else, data warehouses and data marts.  I’m even willing to take this inventory analogy one step further and say that we can subdivide our data-inventory into ‘raw material’, ‘work-in-process’ (WIP), and ‘finished goods’ data, adding value at each step as we integrate our data silos, sort, score and store it, or extract, transform, and load (ETL) the data into ever more accessible and valuable formats.  Yes, I think inventory might be a good one.

But why leave out the King of Assets – Cash; can data be like cash, like a currency?  Read More »

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Agile strategy, revisited

You know that feeling when all your ducks appear to be in a row, all the numbers add up, all the boxes have been checked, but you’ve still got a sneaking suspicion that something is wrong?  I’m not talking just gut feel here, more along the lines of, “The logic is impeccable, so one of the premises must be wrong, but who am I to question any of these accepted, time-worn, management truths?”   In small quantities, the exception(s) prove the rule, but when the exceptions start piling up sufficiently to become a recognizable category of their own, you are surely justified in calling the conventional wisdom into question.

That was precisely the feeling I had as I closed out this Value Alley post from early last year, “Agile Strategy”, which focused on Columbia Business School professor Rita Gunther McGrath’s article and interview in Strategy + Business: “It’s time for most companies to give up their quest to attain strategy’s holy grail: sustainable competitive advantage. The era of sustainable competitive advantage is being replaced by an age of flexibility”.

I concluded the post by hedging my bets: “I have not yet rendered judgment on the impending demise of competitive advantage.  My current bias is that firms will still have opportunities for competitive advantage through focus and development of core competencies in a particular Value Discipline, and that firms of the future will be successful by becoming agile experts within their chosen discipline.”

(see the full BCG article for the classification of industries within each style)

(see the full BCG article for the classification of industries within each style)

Coming to my attention, my rescue, and easing my angst, was this study from the Boston Consulting Group (BCG) – “Your Strategy needs a Strategy” – well worth the read and critical to my argument below.   In it, they expand the concept of strategy from a monolithic, one-size-fits-all approach to suggest that there are at least two dimensions across which strategy setting needs to be evaluated:  predictability and malleability.  In doing so, they provide a solid framework in which it is possible for both McGrath and her critics to be right, they provide the premises through which the exceptions can be properly understood not as exceptions, but simply as one of several available strategic options.

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Transparency and the Information arms race

You are going to be spending proportionately more of your IT budget on security than you have previously spent or ever wanted to spend.  Why?  Because you and everyone else on this planet is engaged in the still early stages of an escalating information arms race, that, while you didn’t ask for it, neither can you avoid.  This escalation is being driven by two recent IT phenomena:  exploding connectivity and transparency.

An article in the March 2015 edition of Scientific American by Daniel Dennett, professor of cognitive science at Tufts, and Deb Roy, director of the Laboratory for Social Machines at MIT, expounds on this idea by linking it in a wonderful analogy to a hypothesis regarding the Cambrian explosion which occurred on this planet 542 million years ago.

fieldmuseumchicago_fullsize_story2The Cambrian explosion saw, in a mere geological instant, life on Earth move from single to multicellular organisms, and with it the emergence of all the varied body plans (phyla) we see today, from arthropods (lobsters) to mollusks (clams) to echinoderms (starfish) to chordates (sharks).  Prior to this, life had existed on Earth for over 3 billion years as nothing more complex than single-celled bacteria or algae, and then, in the blink of an eye over a span of only a few tens of millions of years, 34 of the 35 separate animal phyla arose from the relatively simple amoeba-like creatures of the day.  Why the sudden change?

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The new map of global manufacturing

Many factors go into your strategic global business decisions, from the physical placement of factories and distribution centers, to your choice of suppliers and partners, to your target markets and the business model itself. Businesses have a choice of fundamental global go-to-market investment strategies, from direct foreign investment on the one end, to export through distributors on the other, and a variety of joint ventures, licensing and partnerships in between, with many firms adopting the entire range depending on the maturity of their relationship to the target market. These are typically decade-long commitments to hard-to-unwind investments in not just plant, property and equipment, but talent, training, infrastructure and relationships as well.


For several decades now we have operated under a simplified conception of low-wage versus high-wage countries and labor markets, typically with Latin America, Eastern Europe and most of Asia lumped into the low-wage category.

This worldview, however, is now out of date.  Would it surprise you to know that China is now roughly on par with the U.S. when it comes to total manufacturing costs, to include not just wages but productivity, energy costs and currency values as well?  That Brazil is now one of the higher cost countries, or that Mexico could be cheaper than China? Read More »

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Ye Olde information overload

There’s no such thing as information overload - there is only filter failure”.  ~ Internet scholar Clay Shirky

Information overload is not just a recent phenomenon, it entered into human experience in the middle of the 15th century with Gutenberg and his printing press, and we’ve been devising ways to cope ever since.  Today, more books are printed in a month than can be read in a lifetime.

filter 2And that’s just books – every day we create approximately 3 exabytes of data (that’s 3 million terabytes for those of you keeping score using last year’s counting system).  Every second, 3 million emails are sent, 50,000 Tweets are tweeted, and 2 hours of cat videos are uploaded. Every.  Single.  Second.  (in the time it took to read this far, you just missed 3,000 cat videos)  If you’ve only got 100 unread emails you’re still an amateur.

So how do we cope?  We do what nature does – we filter.

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Good habits for big data

Begin with the end in mind” - Habit #2 from Stephen Covey’s ‘Highly Effective People’.

thisideamustdie_brockmanThe Edge Foundation is based on the premise of: “To arrive at the edge of the world's knowledge, seek out the most complex and sophisticated minds, put them in a room together, and have them ask each other the questions they are asking themselves.”  Each year they pose to their illustrious contributors their annual “Edge Question”, after which John Brockman, editor and publisher of Edge, gathers together the various responses and publishes them in book form.  The question for 2014 was “What scientific idea is ready for retirement?”, with the replies recently published as “This Idea must Die”.

The nomination from Gary Marcus, cognitive scientist at NYU, for an idea whose time has come was ‘"big data" (Already? We hardly got to know you, big data).  Marcus' argument wasn’t that big data has become unnecessary, but that it’s quickly become a case of putting the cart before the horse.  Data has its place, but that place is AFTER you have formulated a hypothesis or theory about a problem you are trying to address.   With a theory in place, you then devise an experiment to test that hypothesis.  The most important property of the data at this stage is that it be relevant to the problem / experiment at hand, and if so, then the more the merrier.  But if not, well, as Marcus puts it, “Big data should not be the first port of call; it should be where we go once we know what we’re looking for”. Read More »

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Big Variety: The real value in Big Data

Forget about Big Volume, for my money the real value in Big Data comes from its variety.  Why? Because just as there is “Value in the Network” when it comes to your business ecosystem, your data can be "networked" for value in much the same way.

Before we dive into the business implications of Big Variety, consider this case from the natural sciences – the discovery, development and eventual acceptance of plate tectonics.   First proposed as the theory of Continental Drift by Alfred Wegener in 1912, it was not until the 1960’s that it was fully accepted based on the overwhelming data-driven evidence acquired across a wide variety of fields:

  • FossilsGeography – As early as 1596 the Dutch cartographer Abraham Ortelius noted the remarkable fit of the South American and African continents, and even suggested that “the Americas were torn away from Europe and Africa . . . by earthquakes and floods”.
  • Geology – In direct support of his theory, Wegener remarked that the locations of several unusual geologic structures could be found on matching coastlines of South America and Africa.
  • Paleontology - Snider-Pellegrini noted that the locations of certain fossil plants and animals on present-day, widely separated continents would form definite patterns (shown by the bands of colors on the above map). Wegener further highlighted the discovery of fossils of tropical plants (in the form of coal deposits) in Antarctica led to the conclusion that this frozen land previously must have been situated closer to the equator. Other mismatches of geology and climate included the occurrence of glacial deposits in present-day arid Africa.
  • Bathygraphy – Not only did post-WW II sonar reveal the extent of the Mid-Atlantic ridge circling the planet under the ocean surface, detailed analysis of the data indicated a narrow gorge precisely along the center crest of the mountain range where the plates were separating.
  • Oceanography – Seafloor mapping using magnetic instruments revealed a pattern of alternating magnetic striping on either side of the mid-ocean ridge.
  • Seismology - Earthquake-recording instruments enabled scientists to learn that earthquakes tend to be concentrated in along the oceanic trenches and spreading ridges.

It took putting all this data from these disparate fields together to develop a plausible mechanism for what came to be known as plate tectonics and thus finally vindicating Wegener.  Big Variety preceded Big Volume by half a century. Read More »

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