Blog posts on data

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• Factfulness – An Extremely Valuable Book

  Data is extremely valuable in helping us make decisions and evaluating the effectiveness of policy. However it is critical to be careful. It is very easy to focus on meeting targets that seem sensible – increasing the number of hospital beds – but that lead to less effective policy.

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  The book relentlessly points out the great progress that has been made globally over the last 50 years and how that progress continues today and looks to be set to continue in the future. We have plenty of areas to work on improving but we should be aware of how much progress we have been making. As he points out frequently he has continually seen huge underestimation of the economic conditions in the world today. This book does a great job of presenting the real success we have achieved and the progress we can look forward to in the future.

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• Applying W. Edwards Deming’s Ideas in Software Development

  In her presentation at the Deming Research conference, Poorani Jeyaseker explains how the management system drives behavior that is not useful to the organization. The business team asks for estimates for software development. Those estimates are treated as promises. The management system creates a punishment mechanism for missing estimates by over 10%. Of course this creates fear and pressure to make sure work can be completed within the 110% * estimate. So logically the estimates are padded (both to account for the natural variation in how close estimates are to final results and for the existing culture that means changes will be made to requirements without the estimate being adjusted)...

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• Understanding and Misunderstanding Variation

  ... one of the central aspects of Deming’s ideas on management: that we need to view the organization as a system. It is not as simple as adopting a couple practices and getting the same results another organization gets from those same practices. The interactions between the existing organization and the new practices will be different. This makes management challenging but it also makes our jobs of improving management continually interesting and full of opportunities to learn.

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• Applying Deming’s Management Ideas at the Great Plains Coca Cola Bottling Company

  The difficulty in changing is often mostly about our psychology (not the technical difficulty of operating under changed systems and processes after making adjustments to adapt to take advantage of new opportunities).

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  Results matter but within a context of the process. If I double profits by wagering all the cash we can borrow on the roulette wheel that result isn’t a sign that we are doing much better. Using data wisely requires understanding what the data tells you and what it does not tell you.

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• My Willing Worker Award

  Data very similar to that provided by the Red Bed Experiment is used everyday in businesses to reward and punish people. Data is used to blame those who fall short of expectations and reward those who have good numbers. In the Red Bead Experiment we know the numbers are not a sensible measure of value provided by the employee. But in our organizations we accept numbers that are just as unrelated to the value provided by the employe to rate and reward employees.

  There is a powerful need to improve the numeracy (literacy with numbers) in our organizations. It isn’t a matter of complex math. The concepts are fairly simple…

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• What Loss Will a Business Suffer Due to a Dissatisfied Customer?

  You can’t know how much a dissatisfied customer will cost your business in the long run. You can make statistical judgements about how costly dissatisfied customers are to a business but those are loaded with many guesses. They can give a general indication of the magnitude of the costs but they are largely guesses, not something you can measure.

  Sometimes a business largely gets away poor quality for a long time. The customer doesn’t change behavior, doesn’t complain to others and doesn’t punish the company in the long term. But you never know when one small failure will cause the luck to run out and turn a customer against the business and costing it dearly.

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• Understanding and Using Data: Waffle House Example

  ...The Waffle House closure data is based on actually closing based existing conditions while warnings and evacuation recommendations are based on predictions about the weather and the impacts those will have on locations.  The warnings are necessarily predictions (to be useful for the whole community they need lead times to take action) where the Waffle House has more flexibility and the organization has managed their system to be more capable of adapting to harsh conditions.  There is a real similarity with designing a agile software development process that is able to be more flexible and react quicker than old "waterfall" style organizations that have to predict far in advance and adapt slowly as conditions change...

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• Rethinking Statistics for Quality Control with George Box

  George Box shared a presentation on Rethinking Statistics for Quality Control at the 2008 Deming Institute Conference in Madison, Wisconsin.

  In the presentation George discusses how to look at data from a process. He mentions why it was so important to understand what Shewhart understood about process data: the order of the data is extremely important; which is why run charts and control (process behavior) charts are plotted in time order...

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• Factfulness – The Importance of Critical Thinking

  I have come to see a willingness to value critical thinking, even when it means forcing the organization to address tough issues, as one the differences between organizations that succeed in applying management improvement methods and those that fail. In many organizations that fail, more weight given to making things easy for your bosses versus continual improvement in providing value to customers (which often requires challenging existing processes, beliefs and power structures in the organization).

  Challenging the status quo is difficult and most organizations prefer to maintain a culture that takes an easier path. Management improvement often requires a willingness to encourage challenges to the status quo. The importance of challenging the status quo in your organization and in your own thinking is under appreciated.

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• Bill Hunter and the Quality Movement

  In the presentation George Box discusses interesting student design of experiments projects; read more on those efforts in 101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments.

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  George's closing statement:

  We carry in our hearts the inspiring memory of not only a scholar but a man that was resourceful, warm, skillful, courageous, optimistic, helpful, enterprising and generous. His career was whole and balanced in a way that is rare and as we grieve his death we also celebrate the life of a remarkable man.

   

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• Workplace Management by Taiichi Ohno

  Ohno focused a great deal on the faulty perceptions derived from cost accounting thinking. He discussed the importance of not letting your understanding be clouded by thinking with the accounting mindset.

  If you insist on blindly calculating individual costs and waste time insisting that this is profitable of that is not profitable, you will just increase the cost of your low volume products. For this reason there are many cases in this world where companies will discontinue car models that are actually profitable, but are money losers according to their calculations. Likewise, there are cases where companies sell a lot of model that they think is profitable but in fact are only increasing their loses.

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• Data Can’t Lie

  Many people don’t understand the difference between being manipulated because they can’t understand what the data really says and data itself “lying” (which, of course, doesn’t even make sense). The same confusion can come in when someone just draws the wrong conclusion from the data that exists (and them blames the data for “lying” instead of themselves for drawing a faulty conclusion).

  The data can be wrong (and the data can even be made faulty intentionally by someone). Or someone can draw the wrong conclusion from data that is correct. But in neither case is the data lying. It is also common to believe the data means something other than what it does (therefore leading to a faulty conclusion).

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  If all those involved understand how to draw conclusions from data it is not easy to mislead them.

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• Data Based Blathering

  I am tired of seeing the American Customer Satisfaction Index (ACSI) promoted as if it were some encouragement for better management when all it seems to do to me is encourage superficial, non data based claims. And since it my blog I can rant if I feel like it.

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  They think a flat American Customer Satisfaction Index (ACSI) reading is going to lead to weak consumer spending? I doubt it. I really doubt it. What data, or theory is that based on? Jeez this whole thing just makes me crazy. Trying to use a index to promote the “importance of quality principles” (ASQ is one of the “sponsors” of this effort) and customer focus in this way – ARGH. It does the opposite – showing people how to misuse numbers. How to overreact to variation. How to compare one dot to another dot and make claims from those 2 dots. I am sure I will make mistakes in my statements but the ACSI has bugged me since it was started with the way it ignores sound quality practices and promotes the opposite of what people like Dr. Deming taught.

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• Interview of Bill Hunter, Brian Joiner and Peter Scholtes on Better Management Practices

  That kind of experience could not have happened if management wasn’t willing to listen to the workers and wasn’t willing to say to the workers “you have brains and you have ideas and why don’t you go out and see if you can solve it and I will back you up. And that is what they did

  Bill on creating jobs people want to do:

  If they are going to work with the attitude that part of my job is to figure out how we can make things work better around here it adds another challenge to the job which makes the work more fun and more enjoyable. It all points in the same direction it seems to me. These methods do feed into making jobs more interesting and morale going up and the job being better.

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• Fooled by Randomness

  When people are asked to explain random variations in data they will make up special causes (that they often even believe are special causes even when they are not) but you can improve management a great deal by just stopping the requirement to “explain” common cause variation...

  Use that time instead to standardize processes. Create control charts for critical processes. Run experiments using PDSA cycle…

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