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Friday
Jan042013

The Dog and Pony Show: Surveys and Time Vacuums

I’m not a huge fan of surveys. Whether by email or phone (I especially dislike phone surveys), I feel like they are an intrusion on my personal time. The only thing that might lessen my survey pain is if I feel like I’m going to get compensated for it. I’m not talking about the chance to win a prize, I’m talking about a guaranteed prize – like a dollar off on my next trip to Starbucks (my best survey experience to date.)

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Monday
Dec102012

Season's Greetings from dataMind

Friday
Nov092012

you might be a dataMind if...

Each month I write to you about using analytics in industry. I discuss something I've learned at Northwestern or how a specific technique can be applied to make an organization better. This month is a little different... instead of focusing on what we know, I chose to focus on who we are. This month also marks the one year anniversary of dataMind. I'd like to say a special thanks to all of you who helped me complete the following statement: "You might be a dataMind if..."

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Wednesday
Oct242012

polishing the big data cannonball

Earlier in my career I had the opportunity to serve on a nuclear powered submarine. My Captain always instilled in me the importance of making good decisions with limited information. He would say that "once you have 51% of the data you need, make your decision, and move on to the next problem." He emphasized that rarely do you need 100% of the data to make a decision, and the more time you spend trying to obtain the data is like spending countless hours "polishing a cannonball."

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Tuesday
Sep252012

Predictive Maintenance: Beware of Multiples of 7

Like many organizational policies and procedures, maintenance practices are inherited from previous generations. When to perform particular maintenance evolutions becomes part of the organizational culture. "Filters are changed every Tuesday." "Inspections are done every Friday." "Liners are always changed on day shift." In response to "this is the way it's always been done," dataMind asks the following question: Why?

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