Posts about Data

Prophet Unheard: Dr. W. Edwards Deming – 1992

This is an interesting video on Deming and American management (by the BBC in 1992): Prophet Unheard. It includes some nice old footage of Deming in Japan. The importance of respect for people is clear and the video also touches on the idea the danger of relying on data (when you do not understand variation and that many important matters and unmeasurable). The video features many snippets of Dr. Deming speaking and includes Don Peterson, Ford CEO; Clare Crawford Mason, If Japan Can, Why Can’t We producer; and Myron Tribus.

Related: Dr. Deming Webcast on the 5 Deadly DiseasesRed Bead Experiment WebcastPerformance without Appraisalmanagement webcasts

Part two of the documentary explores the Deming Prize, understanding data and the PDSA cycle:
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The Biggest Manufacturing Countries in 2008 with Historical Data

Once again the USA was the leading country in manufacturing for 2008. And once again China grew their manufacturing output amazingly. In a change with recent trends Japan grew output significantly. Of course, the 2009 data is going to show the impact of a very severe worldwide recession.

Chart showing percent of output by top manufacturing countries from 1990 to 2008Chart showing the percentage output of top manufacturing countries from 1990-2008 by Curious Cat Management Blog, Creative Commons Attribution.

The first chart shows the USA’s share of the manufacturing output, of the countries that manufactured over $185 billion in 2008, at 28.1% in 1990, 27.7% in 1995, 32% in 2000, 28% in 2005, 28% in 2006, 26% in 2007 and 24% in 2008. China’s share has grown from 4% in 1990, 6% in 1995, 10% in 2000, 13% in 2005, 14% in 2006, 16% in 2007 to 18% in 2008. Japan’s share has fallen from 22% in 1990 to 14% in 2008. The USA has about 4.5% of the world population, China about 20%. See Curious Cat Investment blog post” Data on the Largest Manufacturing Countries in 2008.

Even with just this data, it is obvious the belief in a decades long steep decline in USA manufacturing is not in evidence. And, in fact the USA’s output has grown substantially over this period. It has just grown more slowly than that of China (as has every other country), and so while output in the USA has grown the percentage with China has shrunk. The percentage of manufacturing output by the USA (excluding output from China) was 29.3% in 1990 and 29.6% in 2008. The second chart shows manufacturing output over time.

charts showing the top manufacturing countries output from 1990-2008Chart showing the output of the top manufacturing countries from 1990-2008 by Curious Cat Management Blog, Creative Commons Attribution.

The 2008 China data is not provided for manufacturing alone (the latest UN Data, for global manufacturing, in billions of current USA dollars). The percentage of manufacturing (to manufacturing, mining and utilities) was 78% for 2005-2007 (I used 78% of the manufacturing, mining and utilities figure provided in the 2008 data). There is a good chance this overstates China manufacturing output in 2008 (due to very high commodity prices in 2008).

Hopefully these charts provide some evidence of what is really going on with global manufacturing and counteracts the hype, to some extent. Global economic data is not perfect. These figures are an attempt to capture the economic reality in the world but they are not a perfect proxy. This data is shown in 2008 USA dollars which is good in the sense that it shows all countries in the same light and we can compare the 1995 USA figure to 2005 without worrying about inflation. However foreign exchange fluctuations over time can show a country, for example, having a decline in manufacturing output in some year when in fact the output increased (just the decline against the USA dollar that year results in the data showing a decrease – which is accurate when measured in terms of USA dollars).

If the dollar declines substantially between when the 2008 data was calculated and the 2009 data is calculated that will give result in the data showing a substantial increase in those countries that had a currency strengthen against the USA dollar. At this time the Chinese Renminbi has not strengthened while most other currencies have – the Chinese government is retaining a peg to a specific exchange rate.

Korea (1.8% in 1990, 3% in 2008), Mexico (1.7% to 2.6%) and India (1.4% to 2.5%) were the only countries to increase their percentage of manufacturing output (other than China, of course, which grew from 3.9% to 18.5%).

Related: posts on manufacturingGlobal Manufacturing Data 2007Global Manufacturing Employment Data – 1979 to 2007Top 10 Manufacturing Countries 2006Top 10 Manufacturing Countries 2005lean manufacturing resources

Making Better Decisions

I think the most important thing you can do to make better decisions is to learn from the decisions you make. It sounds easy, but very few people do so effectively.

The best strategy to learn from decisions is to:

Highlights from Recent George Box Speech

The JMP blog has posted some highlights from George Box’s presentation at Discovery 2009

Infusing his entire presentation with humor and fascinating tales of his memories, Box focused on sequential design of experiments. He attributed much of what he knows about DOE [design of experiments] to Ronald A. Fisher. Box explained that Fisher couldn’t find the things he was looking for in his data, “and he was right. Even if he had had the fastest available computer, he’d still be right,” said Box. Therefore, Fisher figured out how to study a number of factors at one time. And so, the beginnings of DOE.

Having worked and studied with many other famous statisticians and analytic thinkers, Box did not hesitate to share his characterizations of them. He told a story about Dr. Bill Hunter and how he required his students to run an experiment. Apparently a variety of subjects was studied [see 101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments]

According to Box, the difficulty of getting DOE to take root lies in the fact that these mathematicians “can’t really get the fact that it’s not about proving a theorem, it’s about being curious about things. There aren’t enough people who will apply [DOE] as a way of finding things out. But maybe with JMP, things will change that way.”

George Box is a great mind and great person who I have had the privilege of knowing my whole life. My father took his class at Princeton, then followed George to the University of Wisconsin-Madison (where Dr. Box founded the statistics department and Dad received the first PhD). They worked together building the UW statistics department, writing Statistics for Experimenters and founding the Center for Quality and Productivity Improvement among many other things.

Statistics for Experimenters: Design, Innovation, and Discovery shows that the goal of design of experiments is to learn and refine your experiment based on the knowledge you gain and experiment again. It is a process of discovery. If done properly it is very similar to the PDSA cycle with the application of statistical tools to aid in determining the impact of various factors under study.

Related: Box on QualityGeorge Box Quotationsposts on design of experimentsUsing Design of Experiments

Deming: There is No True Value

There is no true value of anything: data has meaning based on the operational definition used to calculate the data.

Walter Shewhart’s Statistical Method from the Viewpoint of Quality Control, forward by W. Edwards Deming:

There is no true value of anything. There is instead a figure that is produced by application of a master or ideal method of counting or measurement… no true value of the number of inhabitants within the boundaries of (e.g.) Detroit. A count of the number of inhabitants of Detroit is dependent upon the application of arbitrary rules for carrying out the count. Repetition of an experiment or of a count will exhibit variation.

Dr. Deming’s ideas on the theory of knowledge are the least understood and least seen in other management systems. The importance of understanding what data does, and does not tell you, is at least somewhat acknowledged in other management system but is often not found much in the actual practice of management. The execution often glosses over the importance of actually understanding statistics versus using formulas. Just using formulas is dangerous. It may be inconvenient but learning about the traps we can fall into in using data is important.

How often do you see the operational definition used to calculate the data you see with the data you are provided?

via: Shewhart, Deming and Data by Malcolm Chisholm

Related: How We Know What We KnowPragmatism and Management KnowledgeMeasuring and Managing Performance in OrganizationsDangers of Forgetting the Proxy Nature of Data

Communicating with the Visual Display of Data

graphs showing data sets with different looks even though some statistical characteristics are the same
Anscombe’s quartet: all four sets are identical when examined statistically, but vary considerably when graphed. Image via Wikipedia.

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Anscombe’s quartet comprises four datasets that have identical simple statistical properties, yet are revealed to be very different when inspected graphically. Each dataset consists of eleven (x,y) points. They were constructed in 1973 by the statistician F.J. Anscombe to demonstrate the importance of graphing data before analyzing it, and of the effect of outliers on the statistical properties of a dataset.

Of course we also have to be careful of drawing incorrect conclusions from visual displays.

For all four datasets:

Property Value
Mean of each x variable 9.0
Variance of each x variable 10.0
Mean of each y variable 7.5
Variance of each y variable 3.75
Correlation between each x and y variable 0.816
Linear regression line y = 3 + 0.5x

Edward Tufte uses the quartet to emphasize the importance of looking at one’s data before analyzing it in the first page of the first chapter of his book, The Visual Display of Quantitative Information.

Related: Great ChartsSimpson’s ParadoxSeeing Patterns Where None ExistsVisible DataControl ChartsEdward Tufte’s: Beautiful Evidence

Managing to Test Result Instead of Customer Value

Computer hardware and software creators use benchmarks as one tool to compare the performance of alternative products. At times this can be very useful. You can learn what software of hardware is faster and that may be a very valuable factor. However, any measure is determined by the operational definitions used in collecting the measure. And if people have incentives to improve the measured number they often will do just that (improving the measure) rather than improving the system (the measure is meant to serve as a proxy for some function of that system).

Information technology people actually understand this much better than most mangers (who also rely on measures for many things like return on equity, profit growth, productivity of various plants…) – so actually I find they are not nearly as fooled by measures compared to managers. On Reddit there is an interesting discussion on coding the product to provide good benchmark results [in this context benchmarking has to do with measured results on standard performance tests – not TQM style benchmarking). The technical details in this case don’t matter so much to my point, which is just that when people treat the measure as the true value instead of a proxy for the true value it is risky.

Technology companies compete fiercely and claiming the software or hardware is faster is one big area of competition. And the comment on Reddit is claiming one competitor changed some code only to get a better measure (that provides no benefit to customers). The problem with such actions, is they provide no actual value: all they do is make the measure less meaningful as a proxy.

Now it is also perfectly understandable why it would be done – when you are focused on improving the number, it might well be easier to distort the system to provide a better number (used by to measure performance) instead of actual improve the performance. It is easy to see why a company would do this if they want to have marketing claim their products are the fastest.
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YouTube Uses Multivariate Experiment To Improve Sign-ups 15%

Google does a great job of using statistical and engineering principles to improve. It is amazing how slow we are to adopt new ideas but because we are it provides big advantages to companies like Google that use concepts like design of experiments, experimenting quickly and often… while others don’t. Look Inside a 1,024 Recipe Multivariate Experiment

A few weeks ago, we ran one of the largest multivariate experiments ever: a 1,024 recipe experiment on 100% of our US-English homepage. Utilizing Google Website Optimizer, we made small changes to three sections on our homepage (see below), with the goal of increasing the number of people who signed up for an account. The results were impressive: the new page performed 15.7% better than the original, resulting in thousands more sign-ups and personalized views to the homepage every day.

While we could have hypothesized which elements result in greater conversions (for example, the color red is more eye-catching), multivariate testing reveals and proves the combinatorial impact of different configurations. Running tests like this also help guide our design process: instead of relying on our own ideas and intuition, you have a big part in steering us in the right direction. In fact, we plan on incorporating many of these elements in future evolutions of our homepage.

via: @hexawiseMy brother has created a software application to provide much better test coverage with far fewer tests using the same factorial designed experiments ideas my father worked with decades ago (and yet still far to few people use).

Related: Combinatorial Testing for SoftwareStatistics for ExperimentersGoogle’s Website Optimizer allows for multivariate testing of your website.Using Design of Experiments

Don’t Hide Problems in Computers

Making things visible is a key to effective management. And data in computers can be easy to ignore. Don’t forget to make data visible. Paul Levy, CEO of Beth Israel Deaconess Medical Center in Boston recently hosted Hideshi Yokoi, president of the Toyota Production System Support Center and wrote this blog post:

Together, we visited gemba and observed several hospital processes in action, looking for ways to reduce waste and reorganize work. It was fascinating to have such experts here and see things through their eyes. Mr. Yokoi’s thoughts and observations are very, very clear, notwithstanding a command of English that is still a work in progress.

The highlight? At one point, we pointed out a new information system that we were thinking of putting into place to monitor and control the flow of certain inventory. Mr. Yokoi’s wise response, suggesting otherwise, was:

“When you put problem in computer, box hide answer. Problem must be visible!”

The mission of the Toyota Production System Support Center to share Toyota Production System know-how with North American organizations that have a true desire to learn and adopt TPS.

Related: The Importance of Making Problems VisibleGreat Visual Instruction ExampleHealth Care the Toyota Way

Google’s Innovative Use of Economics

Secret of Googlenomics: Data-Fueled Recipe Brews Profitability

Google depends on economic principles to hone what has become the search engine of choice for more than 60 percent of all Internet surfers, and the company uses auction theory to grease the skids of its own operations. All these calculations require an army of math geeks, algorithms of Ramanujanian complexity, and a sales force more comfortable with whiteboard markers than fairway irons.

Varian tried to understand the process better by applying game theory. “I think I was the first person to do that,” he says. After just a few weeks at Google, he went back to Schmidt. “It’s amazing!” Varian said. “You’ve managed to design an auction perfectly.” To Schmidt, who had been at Google barely a year, this was an incredible relief. “Remember, this was when the company had 200 employees and no cash,” he says. “All of a sudden we realized we were in the auction business.”

Google even uses auctions for internal operations, like allocating servers among its various business units. Since moving a product’s storage and computation to a new data center is disruptive, engineers often put it off. “I suggested we run an auction similar to what the airlines do when they oversell a flight. They keep offering bigger vouchers until enough customers give up their seats,” Varian says. “In our case, we offer more machines in exchange for moving to new servers. One group might do it for 50 new ones, another for 100, and another won’t move unless we give them 300. So we give them to the lowest bidder—they get their extra capacity, and we get computation shifted to the new data center.”

Google continues to make bold moves putting faith in their ability to find innovative solutions that others reject as impossible. It is a challenging but interesting path to success, for them, at least.

Related: Google Should Stay True to Their Management PracticesGoogle’s Answer to Filling Jobs Is an AlgorithmThe Google Way: Give Engineers RoomGoogle Website OptimizerGoogle: Experiment Quickly and Oftenposts on innovation in management

Revealed Preference

Revealed Preference: the preference consumers display by their action, in contrast to what they may say they prefer. While surveys may be useful people often say they will do one thing and actually when given the choice to do so, don’t.

Normally what matters is not what people say they want but what they actually will choose. For that reason revealed preference is a better measure than stated preference. Stated preference is often used as a proxy for actual preference (which may be fine) but it is important to understand that it is just a proxy for actual preference.

See more explanations from the Curious Cat Management Dictionary.

Related: Packaging ImprovementAll Models Are Wrong But Some Are UsefulDangers of Forgetting the Proxy Nature of DataConfirmation BiasBe Careful What You Measure

Red Bead Experiment Webcast

Dr. Deming used the red bead experiment to present a view into management practices and his management philosophy. The experiment provides insight into all four aspects of Dr. Deming’s management system: understanding variation, understanding psychology, systems thinking and the theory of knowledge.

Red Bead Experiment by Steve Prevette

Various techniques are used to ensure a quality (no red bead) product. There are quality control inspectors, feedback to the workers, merit pay for superior performance, performance appraisals, procedure compliance, posters and quality programs. The foreman, quality control, and the workers all put forth their best efforts to produce a quality product. The experiment allows the demonstration of the effectiveness (or ineffectiveness) of the various methods.

Related: Fooled by RandomnessPerformance Measures and Statistics CoursePerformance without AppraisalExploring Deming’s Management IdeasEliminate Slogans

How to Create a Control Chart for Seasonal or Trending Data

Lynda Finn, President of Statistical Insight, has written an article on how to create a control chart for seasonal or trending data (where there is an underlying structural variation in the data). Essentially you need to account for the structural variation to create the control limits for the control chart. She also provides a Minitab project file. Both are available for download from the Curious Cat Management Improvement Library.

Related: Control Charts in Health CareCommon Cause VariationManaging with Control ChartsMeasurement and Data CollectionFourth Generation Management

Harvard’s Masters of the Apocalypse

This article makes some good points, even if it is a bit sensationalist, and intentionally so: Harvard’s masters of the apocalypse by Philip Delves Broughton

Business schools have shown a remarkable ability to miss the economic catastrophes unfolding before their eyes.

In the late 1990s, their faculties rushed to write paeans to Enron, the firm of the future, the new economic paradigm. The admiration was mutual: Enron was stuffed with Harvard Business School alumni, from Jeff Skilling, the chief executive, down. When Enron, rotten to the core, collapsed, the old case studies were thrust in a closet and removed from the syllabus, and new ones were promptly written about the ethical and accounting issues posed by Enron’s misadventures.

Is there a pattern here? Go back to the 1980s, and you find that Harvard MBAs played a big enough role in the insider trading scandals that washed through Wall Street for a former chairman of the SEC to consider it a good move to donate millions of dollars for the teaching of ethics at the school.

Time after time, and scandal after scandal, it seems that a school that graduates just 900 students a year finds itself in the thick of it. Yet there is remarkably little contrition.

Last October, Harvard Business School celebrated its 100th birthday with a global summit in Boston. While Wall Street and Washington descended into an economic inferno, Jay Light, the dean of the school and a board member at the Black-stone private equity group, opened the festivities by shrugging off any responsibility.

“We all failed to understand how much [the financial system] had changed in the past 15 years or so, and how fragile it might be because of increased leverage, decreased transparency and decreased liquidity: three of the crucial things in the world of financial markets,” he said.

You can draw up a list of the greatest entrepreneurs of recent history, from Larry Page and Sergey Brin of Google and Bill Gates of Microsoft, to Michael Dell, Richard Branson, Lak-shmi Mittal – and there’s not an MBA between them.

Yet the MBA industry continues to grow, and business schools provide vital income to academic institutions: 500,000 people around the world now graduate each year with an MBA, 150,000 of those in the United States, creating their own management class within global business.

Given the present chaos, shouldn’t we be asking if business education is not just a waste of time, but actually damaging to our economic health?

Business schools unfortunately continue to take a heavily simplistic number (without an understanding of variation) and fad driven approach to management. W. Edwards Deming was against the damage they were causing decades ago, and I see little evidence they have learned from their failures.

Schools are good for making connections and getting a piece of paper. Some companies won’t consider you for some jobs unless you have an document saying you have an MBA. I strongly question the wisdom of only hiring an MBA to do some job. But many companies like to use simple criteria like – without a piece of paper saying you have an MBA we won’t consider you for this job. So if you want a job from them getting that piece of paper is important.

Related: What is Wrong with MBA’sManagement Training ProgramManagement Advice FailuresThe Lean MBA

Friday Fun: Correlation

Correlation doesn't imply causation

From the excellent xkcd comic.

Related: Correlation is Not CausationDoes the Data Deluge Make the Scientific Method Obsolete?Understanding DataTheory of KnowledgeWhat Makes Scientists Different :-) Dangers of Forgetting the Proxy Nature of DataSeeing Patterns Where None Exists

Statistics for Experimenters in Spanish

book cover of Estadística para Investigadores

Statistics for Experimenters, second edition, by George E. P. Box, J. Stuart Hunter and William G. Hunter (my father) is now available in Spanish.

Read a bit more can find a bit more on the Spanish edition, in Spanish. Estadística para Investigadores Diseño, innovación y descubrimiento Segunda edición.

Statistics for Experimenters – Second Edition:

Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.

* Graphical Analysis of Variance
* Computer Analysis of Complex Designs
* Simplification by transformation
* Hands-on experimentation using Response Service Methods
* Further development of robust product and process design using split plot arrangements and minimization of error transmission
* Introduction to Process Control, Forecasting and Time Series

Book available via Editorial Reverte

Related: Statistics for Experimenters ReviewCorrelation is Not CausationStatistics for Experimenters Dataposts on design of experiments

What’s the Value of a Big Bonus?

What’s the Value of a Big Bonus? by Dan Ariely

To look at this question, three colleagues and I conducted an experiment. We presented 87 participants with an array of tasks that demanded attention, memory, concentration and creativity. We asked them, for instance, to fit pieces of metal puzzle into a plastic frame, to play a memory game that required them to reproduce a string of numbers and to throw tennis balls at a target. We promised them payment if they performed the tasks exceptionally well. About a third of the subjects were told they’d be given a small bonus, another third were promised a medium-level bonus, and the last third could earn a high bonus.

So it turns out that social pressure has the same effect that money has. It motivates people, especially when the tasks at hand require only effort and no skill. But it can provide stress, too, and at some point that stress overwhelms the motivating influence.

When I recently presented these results to a group of banking executives, they assured me that their own work and that of their employees would not follow this pattern. (I pointed out that with the right research budget, and their participation, we could examine this assertion. They weren’t that interested.)

This is an interesting look at an effect of bonuses. We all know monetary bonuses can influence behavior. The problem is the type of behaviors that result. Huge bonuses, for example, create huge incentives to risk the future of the company for the chance at a huge bonus for the executive. Extrinsic motivation leads to many problems.

Problems with bonuses: Losses Covered Up to Protect Bonuses“Pay for Performance” is a Bad IdeaProblems with BonusesBook: Punished By Rewards: The Trouble With Gold Stars, Incentive Plans, A’s, Praise, and Other Bribes by Alfie Kohn – posts on executive pay

Easiest Countries for Doing Business 2008

Singapore is again ranked first for Ease of Doing Business by the World Bank. For some reason they call the report issued in any given year as the report for the next year (which makes no sense to me). The data shown below is for the year they released the report.

Country 2008 2007 2006 2005
Singapore 1 1 1 2
New Zealand 2 2 2 1
United States 3 3 3 3
Hong Kong 4 4 5 6
Denmark 5 5 7 7
United Kingdom 6 6 6 5
other countries of interest
Canada 8 7 4 4
Japan 12 12 11 12
Germany 25 20 21 21

The rankings include ranking of various aspects of running a business. Some rankings for 2008: Dealing with Construction Permits (Singapore and New Zealand 2nd, USA 26th, China 176th), Employing Workers (Singapore and the USA 1st, Germany 142nd), protecting investors (New Zealand 1st, Singapore 2nd, Hong Kong 3rd, Malaysia 4th, USA 5th), enforcing contracts (Singapore 1st, Hong Kong 2nd, USA 6th, China 18th), getting credit (Malaysia 1st; UK and Hong Kong 2nd; Singapore, New Zealand and USA 5th), paying taxes (Hong Kong 3rd, USA 46th, Japan 112th, China 132nd).

These rankings are not the final word on exactly where each country truly ranks but they do provide a interesting view. With this type of data there is plenty of room for judgment and issues with the data. Several of my posts, from my other blogs, that I recommend on this topic: The Future is Engineering, Science and Engineering in Global Economics and Intellectual Property Rights and Innovation.

Related: Easiest Countries from Which to Operate Businesses 2007Countries Which are Easiest for Doing Business 2006New Look American ManufacturingTop Manufacturing Countries (2007)Oil Consumption by CountryInternational Health Care System PerformanceEconomics, America and China

Does the Data Deluge Make the Scientific Method Obsolete?

The End of Theory: The Data Deluge Makes the Scientific Method Obsolete by Chris Anderson

“All models are wrong, but some are useful.”

So proclaimed statistician George Box 30 years ago, and he was right. But what choice did we have? Only models, from cosmological equations to theories of human behavior, seemed to be able to consistently, if imperfectly, explain the world around us. Until now. Today companies like Google, which have grown up in an era of massively abundant data, don’t have to settle for wrong models. Indeed, they don’t have to settle for models at all.

Speaking at the O’Reilly Emerging Technology Conference this past March, Peter Norvig, Google’s research director, offered an update to George Box’s maxim: “All models are wrong, and increasingly you can succeed without them.”

There is now a better way. Petabytes allow us to say: “Correlation is enough.” We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

see update, below. Norvig was misquoted, he agrees with Box’s maxim

I must say I am not at all convinced that a new method without theory ready to supplant the existing scientific method. Now I can’t find peter Norvig’s exact words online (come on Google – organize all the world’s information for me please). If he said that using massive stores of data to make discoveries in new ways radically changing how we can learn and create useful systems, that I believe. I do enjoy the idea of trying radical new ways of viewing what is possible.

Practice Makes Perfect: How Billions of Examples Lead to Better Models (summary of his talk on the conference web site):

In this talk we will see that a computer might not learn in the same way that a person does, but it can use massive amounts of data to perform selected tasks very well. We will see that a computer can correct spelling mistakes, translate from Arabic to English, and recognize celebrity faces about as well as an average human—and can do it all by learning from examples rather than by relying on programming.

Related: Will the Data Deluge Makes the Scientific Method Obsolete?Pragmatism and Management KnowledgeData Based Decision Making at GoogleSeeing Patterns Where None ExistsManage what you can’t measureData Based BlatheringUnderstanding DataWebcast on Google Innovation
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Management Blog Posts From September 2005

photo of North Cascades National Park

Here are some posts from the blog 3 years ago, this month. I took the photo on my visit to North Cascades National Park.

I have added a page to my personal web site with links to my pages on social web sites: LinkedIn, Reddit, Kiva…).

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