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Hear from analytics thought leaders (and get their books too)

J_Marquardt

Analytics thought leaders are busy folks these days as more organizations are finding analytics are key to their success. But once a month, a different analytics expert makes time to have a conversation with my colleague Anne Milley for the Analytically Speaking webcast series. Their discussions cover statistics, design of experiments, quality engineering, data visualization, and consumer and market research.

We’ve compiled some of the highlights from the series in a webcast we're calling Analytically Speaking Featuring the Best in Show. ~~It will be shown Wednesday, Dec. 10, 1 – 2:30 p.m. ET.~~ This webcast premiered on December 10. It is now available on demand. The best in show includes:

SAS co-founder John Sall on the importance of the statistical discipline.

Behavioral economist and author Dan Ariely on why more businesses don’t experiment.

Statistical Thinking authors Ronald Snee and Roger Hoerl on the relationship between quality and reliability.

Popular bloggers Kaiser Fung and Alberto Cairo on the elements of good data visualization.

Words of wisdom for aspiring data analysts from esteemed statisticians David Salsburg and Stu Hunter.

And, of course, Professor Dick De Veaux on how he became the official statistician of The Grateful Dead!

There were so many good moments that this webcast runs longer than the usual hour. You have the option to watch the webcast in its entirety or segment by segment, as your time permits.

If you do watch, we hope you’ll leave a thoughtful comment below about your favorite moment from the webcast. The first 16 to participate will qualify to receive a free book from the following selection of titles (some of which are signed by the authors):

Numbersense: How to Use Big Data to Your Advantage, Kaiser Fung

Optimal Design of Experiments: A Case Study Approach, Peter Goos and Bradley Jones

The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century, David Salsburg

Statistics for Experimenters: Design, Innovation, and Discovery, George E.P. Box, J. Stuart Hunter and William H. Hunter

The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day, David J. Hand

The Innovator’s Hypothesis, Michael Schrage

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Michael J.A. Berry and Gordon S. Linoff

Statistics for Business: Decision Making and Analysis, Robert A. Stine and Dean Hunter

Here's how the book giveaway works: After watching the Best in Show webcast, tell us your favorite moment from the webcast, here in a comment. Your comment must be between 50 and 75 words long. Be sure to enter your e-mail address when you write your comment so we can contact you if you are a winner. Only one book per commenter. Commenters must reside in the US to be eligible to receive a book. The first 16 eligible commenters will win a book!

Comments

WP_Comment

Thomas Shields wrote:

Analytically Speaking Featuring the Best in Show

My favorite paraphrased statement came from Dan Ariely during introduction: â a reduction in performance after receiving a positive comment on oneâ s performance is merely a regression to the meanâ .

The conversation in the webcast concerning Definitive Screening DOEâ s has sparked a movement to start teaching this method of DOE within our organization.

I just want to say â Statistical Thinking!â

J_Marquardt

Jessica Marquardt wrote:

Thank you, Thomas. You're our first commenter. I'll be in touch via email.

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James Kennady wrote:

Data Visualization was my favorite topic. The quote â have something to say, say it and stopâ it a great reminder on visualization. Visualization is about telling a story with data and empowering the audience to arrive at a data based discussion. I often see and have presented conclusions in a visual format. I think the real goal is to tour the audience through the data visually. james.m.kennady@gsk.com

WP_Comment

Tao Y wrote:

Roger Hoerl and Ronald Snee mentioned how important experimental design could be in the service industry. In manufacturing industry, people are thinking how to improve the process everyday but in service industry, there is a lot of room to improve because most of the time, people are following the protocols. It is interesting to see some examples of DOE from service industry.

WP_Comment

Caroll Co wrote:

One of the most memorable moments in the webcast to me was Kaiser Fung's comment on 'Translation' being the key to good data visualization. As a data analyst, when creating graphics, it's not just about what makes sense to me, but more importantly, it's about being able to reach out to the intended audience in a language that they speak.

WP_Comment

Al Zayas wrote:

It's difficult to select a favorite moment from amongst such excellent insight and commentary.

What struck me, personally, was the quote from Hunter, read by Roger Hoerl (I think): "data have no meaning in themselves, they are meaningful only in relation to a conceptual model of the phenomenon studied". This caught my attention simply because too often I see reports and presentations of data in formats that don't clearly convey an intention. It appears the presenter is unclear about why they are running an experiment and unable to distill the information to an essential message.

I also liked the statement by Ronald Snee in regards to "work not documented is work not done". Engineers seem to loathe summarizing their findings for others, but what they don't realize is that their work, no matter how excellent, is quickly forgotten. Documentation provides a record of accomplishment.

WP_Comment

Sean Schubert wrote:

Dan Ariely's comments on the reluctance to experiment in business are quite germane to our daily lives in business. I liked how he called it 'experimental science' and how an experiment is a statement that we might not be correct, which some people in leadership are not used to admitting.

- Sean

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Dave Olson wrote:

I loved Dan Ariely's comments on how human beings get confused about noise in data and make nonsensical inferences based on that - his weight loss example was hilarious, but not all that much of an exaggeration of what can happen sometimes if we just observe things happening to us without being analytic in our approach and thinking.

He makes a point about baseball players and how superstitions get started - is that so much different than how some manufacturing processes are "grandfathered" in and never properly characterized with good DOEs? Time and again, I've seen a good DOE overturn "what is known" about a process.

As an aside, I highly recommend Dan's Coursera course titled "A Beginner's Guide to Irrational Behavior." I did this course when it was offered the first time, and I think it's likely that they offer it again (www.coursera.org/course/behavioralecon).

WP_Comment

Bob Psencik wrote:

Data visualization (section 5) gives me some great ideas on how best to convey the conclusions provided by JMP. If I canâ t get my idea across to my management, all the analytical insights provided by JMP become nothing more than another â science experiment.â Those who could have used my ideas go away confused and end up using another solution that is inferior to my findings. They use the inferior solution not because they think itâ s better but because they will always select a solution they can understand. If I canâ t bridge the communication gap between the rest of the world and my scientific discoveries (provided by JMP), I am just wasting time â ¦both my time and their time.

We need to tell the audience what they want to know â ¦not what I want to tell them. We need to point out the specific conclusions that we have discovered and not just drown them in a vortex of tables, graphs, and words.

Great information, truly the Best of the Best.