Posted by George Huhn on Fri, Apr 30, 2010 @ 10:38 AM
This last paragraph caught my attention:
Karl Popper, the great philosopher of science, once divided the world into two categories: clocks and clouds. Clocks are neat, orderly systems that can be solved through reduction; clouds are an epistemic mess, “highly irregular, disorderly, and more or less unpredictable.” The mistake of modern science is to pretend that everything is a clock, which is why we get seduced again and again by the false promises of brain scanners and gene sequencers. We want to believe we will understand nature if we find the exact right tool to cut its joints. But that approach is doomed to failure. We live in a universe not of clocks but of clouds.1
Our businesses are mixtures of clocks and clouds, but it is often difficult to distinguish which parts are clouds and which parts are clocks. The clocks are things that we can measure and control but clouds are things that we can only try to predict.
Confusion begins when we try to measure and control clouds as if they are clocks or we focus only on trying to control clocks without trying to predict and mange the clouds.
Like Leher's comment on science, it is also a mistake of modern business to try to pretend everything is a clock. While I am a strong proponent of using business analytics, such as
simulation and
optimization in
project portfolio management tools, the analytical tools that we use should be able to manage data from both clocks and clouds. And when we're using these tools, we should be ever mindful of the inherent uncertainty (cloudiness!) in the data and the resulting predictions. Business analyses should rarely be
chiseled in stone. Too often, they are treated like they are.
Look around your business today. Where are your clocks? Where are your clouds?
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Posted by George Huhn on Thu, Apr 08, 2010 @ 10:42 AM
Not quite, although that was the promise in the title of an
article in the March 2010 "Wired" magazine about a recently invented algorithm called "Compressed Sensing" or CS. That isn't to say CS isn't a really cool algorithm for certain applications - it is and you should know about what it can do – but it can't make something out of nothing.
To understand the concept behind CS, think of a digital image. Different kinds of compression technologies (jpg, gif, png, etc.) are used to shrink the size of these images so they use less memory to store and process. Basically, these technologies work by using clever ways to reduce redundant or repetitive data points to a much smaller number of data points. For example, a large area of a single color can be saved without saving each data point since the color is identical. But even though compression can reduce an image size significantly, the compressed file still holds all of the essential information to display the image in its original detail and resolution.
Therefore, a digital image that is compressed to, say, 10% of its original size has essentially discarded 90% of its original data as unnecessary. So if 90% of the data that was originally collected by the image sensors is unnecessary, why collect it in the first place? Why not just collect the essential 10%?
The idea behind CS is that you can collect digital image data using far fewer physical sensors than would normally be used and then use the CS algorithm to reconstruct the digital image as if you had used a conventional number of sensors. So you lose the computationally expensive overhead of collecting all the data, analyzing it, and then discarding most of it. Instead, you only need to collect a small amount of it, and then use CS to reconstruct the rest. And CS can do a remarkable job of reconstructing an image from very little data.
The CS algorithm isn't just applicable to digital images. It can be used on all kinds of digital data processing from music to interstellar radio waves to scrambled radio communications.
CS works based on a concept called "sparsity," which describes the density of data. Conceptually, a floor that has a few balls spread out over it would be considered sparse whereas a floor covered with many balls of different colors all touching each other would not. It turns out that reconstructing an image using CS means finding the sparsest image that can be constructed from the dataset.
However, there is one key point that needs to be stressed: CS cannot reconstruct data that isn't there - you can't make something out of nothing. In other words, if you take a digital image using far fewer sensors than normal and there is a critical detail that is missed entirely by the sensors, it cannot be recovered using CS. Unlike CS, conventional image compression works well because it looks at all the detail first and throws away the data it doesn't need.
Nevertheless, the promise for CS is exciting, particularly in areas where full data collection can be difficult or impossible because of volume of data or physical constraints. These can be sampled using far fewer sensors than otherwise might be required and then reconstructed using CS to obtain a resolution that is adequate for extracting information. One of the major challenges in applying CS is determining the minimum number of sensors required to sample a given data set.
I have been thinking about how CS technology might be applied to business, process development, and manufacturing data. Can you think of any potential applications in your business?
Post script: The original title in Wired magazine was "F_ll _n T_e Bl__ks: A revolutionary algorithm can make something out of nothing." The on-line version's title was changed to "Fill in the Blanks: Using Math to Turn Lo-Res Datasets Into Hi-Res Samples." Much better.
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Posted by George Huhn on Mon, Jan 25, 2010 @ 12:54 PM
The Gantt chart was introduced to the world by Henry Laurence Gantt between 1910 and 1915. He described his invention quite simply in his book Organizing for Work published in 1919:
"…the following principles upon which this chart system is founded are easily comprehended:
First: The fact that all activities can be measured by the amount of time needed to perform them.
Second: The space representing the time unit on the chart can be made to represent the amount of activity which should have taken place in that time."
In addition to inventing this staple of project management, Organizing for Work shows that Gantt was a strong proponent of social responsibility for engineers and industry and the idea of an honest and democratic workplace:
"Industrial control is too often based on favoritism or privilege, rather than on ability. This hampers the healthy, normal development of industrialism, which can reach its highest development only when equal opportunity is secured to all, and when all reward is equitably proportioned to service rendered. In other words, when industry becomes democratic." (Organizing for Work, 1919)
"The business system must accept its social responsibility and devote itself primarily to service, or the community will ultimately make the attempt to take it over in order to operate it in its own interest. (Organizing for Work, 1919)
Doesn't that last quote sound a little bit like it came from the current healthcare reform debate?
I also thought that it would be great if these words from Gantt were hung in a prominent place in every project and project portfolio management office:
"First: We have no right morally to decide as a matter of opinion that which can be determined as a matter of fact.
Second: If we allow ourselves to be governed by opinion where it is possible to obtain facts, we shall lose in our competition with those who base their actions on facts.
The substitution of fact for opinion is the basis of modern industrial progress, and the rate of this progress is controlled by the extent to which the methods of scientific investigation supplant the debating society methods in determining a basis for action." (Organizing for Work, 1919)
The Henry Laurence Gantt Medal was established in 1929 by the American Society Of Mechanical Engineers is given for "distinguished achievement in management and for service to the community."
Mr. Gantt is one of those people that I like to imagine what more he might have done had computers been around when he was!
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Posted by George Huhn on Tue, Dec 22, 2009 @ 12:11 PM
Remember how mapping the human genome was going to lead to cures for different genetic diseases? The idea was pretty simple: compare the genes of healthy people to the genes of people with diseases ranging from cancers to allergies and – voila – fix the genes that were making them sick. Instant cures, right?
Well, maybe not.
It seems like things turned out to be a lot more complicated than that.
In
The Gene Bubble published in November's Fast Company, David Freedman explains that in spite of the billions of dollars poured into mapping the human gnome "with precious few exceptions virtually no promising new treatments or even highly useful diagnostics have emerged."
Why?
Because of "junk DNA." Apparently, junk DNA "accounts for 80% of a genes influence over disease and is incredibly difficult to sort out."
"It's very discouraging, but we don't have any kind of code for understanding junk DNA. I can find the switches, but I don't know what they do. There are switches for the switches, and switches for those switches. It's endless."
So remember: even in nature, how you fold your junk matters.
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