Kevin Meyer

Life and leadership at the nexus of lean and zen.

Big Data, Small Data

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I’ve been following the panting exuberance of big data apostles for the past few years, rolling my eyes at most of it. Sure, it can be interesting, but maybe my age showing when I say “so what?” to most of it.

Einstein - Not everything that can be counted, counts; not everything that counts can be counted.What finally pushed me over the edge enough to comment on it was a tweet I saw from none other than our friends at SAP, saying (I’m paraphrasing) “you have lots of data, let SAP analyze it.” But of course. There’s gold in them there data… at least to SAP.

Just because we have lots of data, does it mean it has to be analyzed? What is the question, what is the problem, what is the opportunity being addressed? As Einstein said, “not everything that can be counted, counts.

It has become easier and easier to create, capture, and store data. Just ask the NSA. Or Target. Sure, it doesn’t take up physical space, but it still takes time, equipment, and people to manage that inventory of data. And perhaps inventory it is… excess, over-produced inventory. What does lean tell us about inventory…?

Sure, there is potential value in linking disparate datasets to come up with interesting and perhaps profitable conclusions. Unfortunately the most common outcome I’ve seen are an increasing number of KPIs, dashboard indicators, and the like tied to data. And once again, an increasing number usually creates a dilution of focus. Just ask those companies that have a two inch thick strategic plan.

A year ago Rufus Pollock penned an article for The Guardian that described how the real revolution isn’t big data, but the increased accessibility to small data.

Just as we now find it ludicrous to talk of “big software” – as if size in itself were a measure of value – we should, and will one day, find it equally odd to talk of “big data”. Size in itself doesn’t matter – what matters is having the data, of whatever size, that helps us solve a problem or address the question we have.

Bingo. Just like the 5S of lean or micro meditation of mindfulness, data is just a tool. First identify the problem, question, or opportunity. Only then determine what data, and analysis, is necessary.

Meanwhile we risk overlooking the much more important story here, the real revolution, which is the mass democratisation of the means of access, storage and processing of data. This story isn’t about large organisations running parallel software on tens of thousand of servers, but about more people than ever being able to collaborate effectively around a distributed ecosystem of information, an ecosystem of small data.

Smaller data, more interconnected and accessible.

This next decade belongs to distributed models not centralized ones, to collaboration not control, and to small data not big data.

What size is your data?

Of Maps, ERP, and Basic Thinking

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By Kevin Meyer

A hat tip to Mark Graban for pointing out this article on a problem the Fairbanks airport has been experiencing with Apple Maps.  It would be funny if it wasn't so serious.  Ok, it's still funny.

There’s a way to get to the Fairbanks Airport, just don’t ask your iPhone for help.
Apple has disabled driving directions to the Fairbanks International Airport after a glitch in its maps app guided drivers to the edge of a runway instead of a terminal, airport spokeswoman Angie Spear said Friday.

Previous directions on newer iPhones and iPads guided drivers to the edge of the tarmac instead of the correct route to the terminal. In incidents Sept. 6 and Sept. 20, drivers went through a gate, past warning lights and signs, and then across an active runway, to reach the terminal.

Now there's obviously a bunch of bad things happening here.  The app, of course, but also a physical security system that let's folks just drive right onto active runways.

But what really struck me was the lack of extremely basic perceptual/analytical skills on the part of the drivers.  How can you drive past warning lights and signs, through a gate, and across runways without realizing that something might just be wrong?  That something doesn't fit conventional experience. Seriously?  Are we so entranced and trusting of our world of apps that we can no longer recognize a conflict with expected reality?  Apparently so.

I'm obviously dating myself, but I remember keeping real map books in the car, driving blindly around cities like Boston having to stop every couple blocks to check the book, and even ordering TripTiks from AAA for longer trips.  A drive, even into a new town, took a certain level of research, analysis, and writing.  That often lead to a certain level of getting lost – failure – and then learning from that failure.

No longer.  Now you just turn on the app, and if it seems strange to have to avoid a landing jet along the way you chalk it up to the vagarities of "the shortest route."  Common sense exempted.

And this is the exact reason why one of my hot buttons are systems like ERP, MRP, and the like where gigabytes of data are dumped into a machine and results are miraculously spit out and displayed on reports or monitors.  The results are taken for granted and assumed to be correct, even if they fly in the face of common sense.  $50 million in extra chocolate bars?  $1 billion in extra Blackberry phones?  The system said so, therefore it must be correct.

We have to learn to think again.  We have to allow, and encourage our people to think.  Training them to read the output on a monitor, or the results of an app, isn't thinking.

A few months ago I wrote a piece on Learning by Writing… by Hand.  

The process of writing by hand creates understanding, ownership, introspection, and thus learning.

This is also why I regularly harp on the advantages of scribbling on whiteboards over typing into "the machine" and then coercing that data onto reports or electronic displays.  When you write a production number, metric, or problem on a white board you own that number, you immediately see the relationship between it and the numbers next to it, you recognize patterns and trends, and you may have to even explain it to peers standing around you.  Action can be taken immediately to change an unfavorable situation.

Typing into a computer?  Not so much.  Somehow that data is mysteriously transformed into other numbers and analyses that you may see a week or even month later and the linkage, understanding, and ownership of that relationship is lost.  You end up with a bunch of folks trained to feed the machine, and a different bunch of folks trained to supposedly interpret what the machine spits out.  The problem – and opportunity – is obvious.

Every day I find more examples where this is true.  Whether it's managing a factory or using a map app.

Write. Think. Learn.  Please, before you hit a plane.

Data, Facts, and Truth

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By Kevin Meyer

I've been observing the growing love affair with "big data" with increasing skepticism, and perhaps even worry.  Long-time readers know I've always been this way; in fact, one of my first posts – seven years ago – was on the mis-use or over-use of data and algorithms: The False God of the Almighty Algorithm.  Later posts demonstrated how a simple white board can sometimes analyze and plan a situation – such as a factory floor – better than software.  Years later I believe that more than ever.

Recently Dr. Art Langer of Columbia University has written a couple of articles that align with my concerns over an obsession with big data.  In CIOs Shouldn't Let Big Data Rule Their Decision-Making he discusses how data can sometimes lead to the wrong conclusions.

Once you rely on big data, it tends to become omnipotent in the decision-making process.


We must remember that our markets are constantly changing and the
variables that dominate decision-making are extremely complex—and there
are many great decisions that are made by people that go on intuition
and “gut-feel.” And there are many CEOs that consistently state that
many of their decisions have little to do with logic.

 CIOs beware—don’t forget the power of the human factors.

Data is a tool, like many others.  Dr. Langer's most recent article, It's Not Just the Data, Stupid, expounds on this a bit more.

We are being overwhelmed with the use of digital data to make decisions.
You can’t read much these days without immediately seeing buzzwords
like “Big Data,” “Business Analytics,” or “Business Intelligence.”
Discussing process issues is passé; how to deal with data is the “in”
thing for business discussions, especially at board meetings.

More and more, I’m seeing other ways in which we’ve become increasingly reliant on data testing.

And once again we are losing sight of the context and perspective.  Dr. Langer quotes Ron Sentz, VP of EMSI:

“The biggest limit to big data is our ability to interpret it. People
need to understand why they are using data. What is the end goal?” Sentz
said. “Data is also like an assembly of facts, which aren’t necessary
the same thing as truth. If facts are poorly interpreted, it could lead
to the wrong conclusions.”

That last sentence needs to be repeated: Data is also like an assembly of facts, which aren't necessarily the same thing as truth.

We see that in so many areas.  Politics, climate change, marketing, and even when trying to schedule a complex factory floor.  We get so sucked into data that we assume it is fact.  We get so sucked into facts that we assume it is truth.

Our LeanBlog friend, Mark Graban, wrote about this a couple years ago, referencing a great photo from FailBlog.

Fail

Mark also references two quotes that I've both enjoyed and been troubled by:

"In God we trust, all others bring data." – Dr. W. Edwards Deming

"Data is of course important in manufacturing, but I place the greatest emphasis on facts." – Taiichi Ohno

The problems I see with people and organizations caught in big data, or just plain old data, are two-fold:

The desire if not requirement that data must be used with every decision creates paralysis.  In the lean world we are taught to seek perfection, but sometimes we forget that the one thing more important than perfection is simply progress.  I've been part of situations where the data is never quite good enough, the analysis of data never quite complete, the conclusions from analyses never quite solid.  So nothing happens.

Equally dangerous is an overconfidence on data, and subsequent analyses and conclusions.  Data becomes facts becomes truth.  In the two articles I referenced by Dr. Langer there are several examples where this is not the case.

Because truth requires perspective, context, reference, and understanding.  Knowing when outliers are relevant or irrelevant, when the dataset is complete or incomplete, and how to make the correct, or necessary, conclusions when data is incomplete or inappropriate.  Experience and intuition create relevance.  The ability to create relevance is a core component of leadership.

Facts are not just data.  Truth is not just facts.  Don't forget the human factors.

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