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<!--Generated by Squarespace V5 Site Server v5.13.159 (http://www.squarespace.com) on Sat, 25 May 2013 19:27:15 GMT--><rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:wfw="http://wellformedweb.org/CommentAPI/" xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0"><channel><title>NEWS</title><link>http://www.datamindco.com/news/</link><description></description><lastBuildDate>Mon, 07 Jan 2013 20:40:58 +0000</lastBuildDate><copyright></copyright><language>en-US</language><generator>Squarespace V5 Site Server v5.13.159 (http://www.squarespace.com)</generator><item><title>The Dog and Pony Show: Surveys and Time Vacuums</title><category>asking the right questions</category><category>customer experience</category><category>data</category><category>inefficiency</category><category>surveys</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Fri, 04 Jan 2013 16:23:01 +0000</pubDate><link>http://www.datamindco.com/news/2013/1/4/the-dog-and-pony-show-surveys-and-time-vacuums.html</link><guid isPermaLink="false">1177710:15545128:32382616</guid><description><![CDATA[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.)]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-32382616.xml</wfw:commentRss></item><item><title>Season's Greetings from dataMind</title><dc:creator>Kevin Henderson</dc:creator><pubDate>Mon, 10 Dec 2012 22:23:24 +0000</pubDate><link>http://www.datamindco.com/news/2012/12/10/seasons-greetings-from-datamind.html</link><guid isPermaLink="false">1177710:15545128:31827377</guid><description><![CDATA[<p><span class="full-image-block ssNonEditable"><span><img src="http://www.datamindco.com/storage/2012-dataMind-holiday-card.jpg?__SQUARESPACE_CACHEVERSION=1355178286290" alt="" /></span></span></p>]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-31827377.xml</wfw:commentRss></item><item><title>you might be a dataMind if...</title><category>#YouMightBeAdataMindIf...</category><category>Northwestern University</category><category>arithmetic</category><category>decision-making</category><category>intuition</category><category>skepticism</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Fri, 09 Nov 2012 16:05:48 +0000</pubDate><link>http://www.datamindco.com/news/2012/11/9/you-might-be-a-datamind-if.html</link><guid isPermaLink="false">1177710:15545128:30422661</guid><description><![CDATA[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..."]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-30422661.xml</wfw:commentRss></item><item><title>polishing the big data cannonball</title><category>Anthony Bastardi</category><category>Big Data</category><category>Eldar Shafir</category><category>Princeton</category><category>Stanford</category><category>decision-making</category><category>uncertainty</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Wed, 24 Oct 2012 14:14:12 +0000</pubDate><link>http://www.datamindco.com/news/2012/10/24/polishing-the-big-data-cannonball.html</link><guid isPermaLink="false">1177710:15545128:30038638</guid><description><![CDATA[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."]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-30038638.xml</wfw:commentRss></item><item><title>Predictive Maintenance: Beware of Multiples of 7</title><category>correlation</category><category>downtime</category><category>equipment failure</category><category>maintenance periodicity</category><category>maintenance scheduling</category><category>organizational culture</category><category>outliers</category><category>predictive maintenance</category><category>production volume</category><category>tree map</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Tue, 25 Sep 2012 16:16:04 +0000</pubDate><link>http://www.datamindco.com/news/2012/9/25/predictive-maintenance-beware-of-multiples-of-7.html</link><guid isPermaLink="false">1177710:15545128:29329865</guid><description><![CDATA[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?]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-29329865.xml</wfw:commentRss></item><item><title>Predictive Analytics at NU</title><category>Monty Hall Problem</category><category>Northwestern University</category><category>bell curve</category><category>causation</category><category>correlation</category><category>linear regression</category><category>normal distribution</category><category>predictive analytics</category><category>safety in numbers</category><category>sample size</category><category>statistics</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Thu, 09 Aug 2012 12:17:37 +0000</pubDate><link>http://www.datamindco.com/news/2012/8/9/predictive-analytics-at-nu.html</link><guid isPermaLink="false">1177710:15545128:22292078</guid><description><![CDATA[Last March I started taking classes at Northwestern University with the goal of obtaining a Master's in Predictive Analytics.  I've almost completed my third course.  This newsletter summarizes why I chose the program and a few of the topics I've explored to date.]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-22292078.xml</wfw:commentRss></item><item><title>So my kid wants to go to Harvard. Now what?</title><category>Dr. Eric Fredland</category><category>Harvard</category><category>The University of Texas</category><category>U.S. Naval Academy</category><category>brand</category><category>economics</category><category>private vs. public education</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Wed, 13 Jun 2012 14:56:41 +0000</pubDate><link>http://www.datamindco.com/news/2012/6/13/so-my-kid-wants-to-go-to-harvard-now-what.html</link><guid isPermaLink="false">1177710:15545128:16699285</guid><description><![CDATA[This is what Dr. Eric Fredland and I talked about over lunch in June of 2006.  We were trying to better understand the economics of private vs. public education by answering the following hypothetical question:  If my kid is accepted to both Harvard and The University of Texas, which should I recommend?]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-16699285.xml</wfw:commentRss></item><item><title>The Overtime vs. New Hire Dilemma</title><category>40-hour work week</category><category>idle labor</category><category>manufacturing</category><category>new hire</category><category>overstaffing</category><category>overtime</category><category>overtime vs. hiring break-even</category><category>predictive analytics</category><category>probability</category><category>productivity</category><category>worker fatigue</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Thu, 10 May 2012 13:43:49 +0000</pubDate><link>http://www.datamindco.com/news/2012/5/10/the-overtime-vs-new-hire-dilemma.html</link><guid isPermaLink="false">1177710:15545128:16207164</guid><description><![CDATA[Overtime policies are a good example of "gut" decisions trumping "logical" ones.  Intuitively, overtime seems bad.  Logically, it can make a lot of sense.  We visualize paying $30 per hour for overtime vs. $20 per hour for regular time.  In reality we are utilizing our existing labor resources during periods of peak demand.  Contrary to conventional wisdom, overtime is most valuable when you least need it.]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-16207164.xml</wfw:commentRss></item><item><title>The Importance of Outliers</title><category>Daniel Kahneman</category><category>Nobel Prize</category><category>data science</category><category>economics</category><category>outliers</category><category>predictive analytics</category><category>productivity</category><category>residual plot</category><category>shift change</category><category>shift work</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Fri, 06 Apr 2012 10:28:39 +0000</pubDate><link>http://www.datamindco.com/news/2012/4/6/the-importance-of-outliers.html</link><guid isPermaLink="false">1177710:15545128:15742782</guid><description><![CDATA[When I first started studying economics, I remember my professors cautioning us against discarding outliers.  They would say that the outlier you're about to omit actually happened.  Including it might not give you the model you're looking for, but it occurred, and there's probably a good reason.]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-15742782.xml</wfw:commentRss></item><item><title>The Science of Making Decisions</title><category>business</category><category>conventional wisdom</category><category>decision-making</category><category>housing prices</category><category>intuition</category><category>logic</category><category>predictive analytics</category><category>profit</category><category>rent vs. buy</category><category>status quo</category><dc:creator>Kevin Henderson</dc:creator><pubDate>Wed, 07 Mar 2012 11:40:57 +0000</pubDate><link>http://www.datamindco.com/news/2012/3/7/the-science-of-making-decisions.html</link><guid isPermaLink="false">1177710:15545128:15333493</guid><description><![CDATA[This month I had the opportunity to re-connect with you to explain the goal of dataMind and discuss how our services benefit business.  I've always been interested in how individuals and institutions make decisions, especially when there's a divergence between feeling and logic.  In my experience observing decision-making, "gut" feelings usually trump logic.]]></description><wfw:commentRss>http://www.datamindco.com/news/rss-comments-entry-15333493.xml</wfw:commentRss></item></channel></rss>