Bridging the Gap: The Economics of Data Science and Decisions
The official blog site of Incite! Decision Technologies. Thinking about thinking and deciding in a complex, uncertain, and risky world.
|The best laid schemes o’ Mice an’ Salesmen, Gang aft agley|
|No, no, boy, that's no way to make a plane. That'll, I say, that'll never...fly!|
|Although Nate Silver was leaning in the wrong direction for predicting the outcome, his odds were actually more realistic and informed than many other pollsters who were giving 19:1 odds or better for a Clinton win.|
Labels: decision analysis
Perhaps most importantly, I've also decided to give all proceeds to the Agape Girls Junior Guild, which is a group of middle-school girls who do fundraising for mitochondrial disorder research at Seattle Children's Research Institute and Seattle Children's Hospital. While the minimum price for this book will always be free, if you're the type who likes to "buy the author a coffee," know that your donation is supporting a better cause than my already out-of-control coffee habit. :-)
In the current rush to adopt data-driven analytics, discussions about algorithms, programming tools, and big data tend to dominate the practice of business analytics. But we are defined by our choices, our values, and preferences. Data and business analytics that do not start with this recognition actually fail to support the human-centered reason for decision making. This is the way of the Sith. A Jedi, however, knows that framing business analytics in terms of the values and preferences of decision makers, and the uncertainty of achieving those, employs the tools of decision and data science in the wisest way. In this discussion, we will think about the principles of high quality decisions, how to frame a business analytics problem, and learn how to use information in the most efficient way to create value and minimize risk.
|Curiosity photo by Rosemary Ratcliff, provided courtesy of FreeDigitalPhotos.net|
Over the holidays, the New York Times delivered an unusual juxtaposition of headlines and content, and apparent lack of self-awareness, to illicit such a hearty chuckle from its readers as to make the cheerful Old Saint jealous.
I copied the following nineteen zen-like koans from the website devoted to the Python programming language (don't leave yet...this isn't really going to be about programming!).
You've probably heard the saying, "It's better to be mostly accurate than precisely wrong." But what does that mean exactly? Aren't accuracy and precision basically the same thing?
|The canonical target pattern explanation of accuracy and precision.|
|[Image from FreeDigitalPhotos.net by Salvatore Vuono.]|
Mr. Patrick Burns at Burns Statistics (no, not that Mr. Burns) provides an excellent overview for the hidden dangers that lurk in your spreadsheets. Guess what. The problems aren't just programming errors and the potential for their harm, but are errors that are inherent to the spreadsheet software itself. That's right. Before your analysts even make an error, the errors are already built in. Do you know what's lurking in your spreadsheets? Well, do you?
This morning @WSJ posted a link to the story about Microsoft’s announcement of its plans to lay off 18,000 employees. This picture (as captured on my iPhone)...
During a recent market development planning exercise, my client recognized that his colleagues were making some rather dubious assumptions regarding the customers they were trying to address (i.e., acceptable price, adoption rate, lifecycle, market size, etc.), the costs of development, and costs of support. Although he frequently asked “How do you know that?”, he seemed to face irritation and mild belligerence in reaction from those he asked to justify their assumptions. So, together we devised a simple little routine to force the recognition that assumed facts might be shakier than previously thought.
|Before Western explorers proved that the Earth is round, ships used to sail right off the assumed edges.|
A friend on LinkedIn asks, “Can modeling a business work?” I respond:
This was a great article in The Wall Street Journal today.
The followings is the first chapter excerpt from my newly published tutorial.
In a previous post, I discussed the meaning of expected value (EV) and how it's useful for comparing the values of choices we could make when the outcomes we face with each choice vary across a range of probabilities. The discussion closed by comparing the choice to play two different games, each with different payoffs and likelihoods. Game 1 returns an EV of $5, even though it could never actually produce that outcome; and Game 2 returns an EV of $4, also being incapable of producing that outcome.