Jim Harris

An Issue with Reporting Data Quality Issues

Blog posts that are both witty and wise are Jim’s signature stamp – his upbeat writing style never fails to ask big questions or inspire interesting debate.

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An Issue with Reporting Data Quality Issues

An Issue with Reporting Data Quality Issues

Jun 12, 2013 by

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An organization’s perspective on data quality is often revealed by its data auditing practices. Some organizations practice data quality ignorance by not performing data audits, assuming that if they don’t check it or hear anyone screaming about it, their data quality must be good enough.

 

The Fallacy of Defect Prevention

The Fallacy of Defect Prevention

Jun 05, 2013 by

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One of my least favorite phrases in the data quality industry is “getting data right the first time, every time.” It’s not that I disagree with the premise of defect prevention. Even though it’s impossible to truly prevent every defect before it happens, defect prevention is highly recommended because the more control enforced where data originates, the better the overall quality will be for enterprise information.

 

The IKEA Effect and Change Management

The IKEA Effect and Change Management

May 22, 2013 by

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IKEA’s success of selling furniture (e.g., beds, chairs, desks, bookshelves) with “some assembly required” by the buyer inspired what’s known in psychology as the IKEA Effect, which is a cognitive bias where labor enhances affection for its results, resulting in an excessive admiration of a particular item you put together on your own.

 

The Decision Wobegon Effect

The Decision Wobegon Effect

May 15, 2013 by

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In his book The Most Human Human, Brian Christian discussed what Baba Shiv of the Stanford Graduate School of Business called the decision dilemma, “where there is no objectively best choice, where there are simply a number of subjective variables with trade-offs between them. The nature of the situation is such that additional information probably won’t even help. In these cases – consider the parable of the donkey that, halfway between two bales of hay and unable to decide which way to walk, starves to death – what we want, more than to be correct, is to be satisfied with our choice (and out of the dilemma).”

 

Change = WIIFM > WMETP

Change = WIIFM > WMETP

May 08, 2013 by

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My previous post about change management, which advocated nudges not mandates, received an excellent comment from Karen Way: “What I’ve found that works to nudge people into accepting data quality as part of their norm is to demonstrate the benefit to them, the WIIFM (what’s in it for me) factor. This is especially true…

 

Getting Schooled on Measurement

Getting Schooled on Measurement

May 01, 2013 by

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In previous posts, I explained how measuring is intrinsically fuzzy and what is being measured is intrinsically fuzzy. In this post, I want to take on the common adage: “you can’t manage what you can’t measure.”

 

Bursting Your Filter Bubble

Bursting Your Filter Bubble

Apr 24, 2013 by

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In a previous post about data visualization, I discussed how our expectations can distort the data we visualize a lot more than we may realize, causing us to mistake dashboards for magic mirrors reflecting back our own image of what we want our data to show us.

 

Use a No Brown M&M’s Clause

Use a No Brown M&M’s Clause

Apr 17, 2013 by

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There is a popular story about David Lee Roth exemplifying the insane demands of a power-mad celebrity by insisting that Van Halen’s contracts with concert promoters contain a clause that a bowl of M&M’s has to be provided backstage with every single brown candy removed, upon pain of forfeiture of the show, with full compensation to the band.

 

Cargo Cult Data Science

Cargo Cult Data Science

Apr 10, 2013 by

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Last week, Phil Simon blogged about being wary of snake oil salesman who claim to be data scientists.  In this post, I want to explore a related concept, namely being wary of thinking that you are performing data science by mimicking what data scientists do.

 

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