Generally speaking, information asymmetry is advantageous for the retailer, allowing them to manipulate the customer into purchasing products that benefit the retailer’s goals (e.g., maximizing profit margins or unloading excess inventory) more than the customer’s goals (e.g., paying a fair price or buying the product that best suits their needs).  I don’t mean to demonize the retail industry, but for a long time, I’m pretty sure its unofficial motto was: “An uninformed customer is the best customer.”

Let’s consider the example of purchasing a high-definition television (HDTV) since it demonstrates how information asymmetry is not always about holding back useful information, but also bombarding customers with useless information.  In this example, it’s about bombarding customers with useless technical jargon, such as refresh rate, resolution, and contrast ratio.

To an uninformed customer, it certainly sounds like it makes sense that the HDTV with a 240Hz refresh rate, 1080p resolution, and 2,000,000:1 contrast ratio is better than the one with a 120Hz refresh rate, 720p resolution, and 1,000,000:1 contrast ratio.

After all, 240 > 120, 1080 > 720, and 2,000,000 > 1,000,000, right?  Yes — but what do any of those numbers actually mean?

The reality is that refresh rate, resolution, and contrast ratio are just three examples of useless HDTV specifications because they essentially provide no meaningful information about the video quality of the television.  This information is advantageous to only one party involved in the transaction — the retailer — since it appears to justify the higher price of an allegedly better product.

But nowadays fewer customers are falling for these tricks.  Performing a quick Internet search, either before going shopping or on their mobile phone while at the store, is balancing out some of the information asymmetry in retail sales and empowering customers to make better purchasing decisions.  With the increasing availability of broadband Internet and mobile connectivity, today’s empowered customer arrives at the retail front lines armed and ready to do battle with information asymmetry.

The empowered customer changes the balance of power in the retail industry.  Is your business ready for Smarter Commerce? Join the conversation on the IBM Mid-Market Smarter Commerce Twitter chat on Wednesday, May 23 at 2:00PM EST.  Panelists will include some of the top Mid-Market influencers in the industry.  IBM Experts, business partners, business owners and managers are all encouraged to join in, ask questions, and share their knowledge in a relaxed atmosphere.  The chat can be followed on Twitter using the hashtag #mmSCchat or log on and access the chat on twebevent: twebevent.com/mmSCchat

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.

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Inspired by a recent Boing Boing blog post

Are you suffering from persistent and annoying data quality issues?  Or are you suffering from the persistence of data quality tool vendors and consultants annoying you with sales pitches about how you must be suffering from persistent data quality issues?

Either way, the Data Division of Prescott Pharmaceuticals (trusted makers of gastroflux, datamine, selectium, and qualitol) is proud to present the perfect solution to all of your real and/or imaginary data quality issues — The Data Quality Placebo.

Simply take two capsules (made with an easy-to-swallow coating) every morning and you will be guaranteed to experience:

“Zero Defects with Zero Side Effects” TM

(Legal Disclaimer: Zero Defects with Zero Side Effects may be the result of Zero Testing, which itself is probably just a side effect of The Prescott Promise: “We can promise you that we will never test any of our products on animals because . . . we never test any of our products.”)

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Essentially, there’s only two ways you can complete the Q Test, which are differentiated by how you trace the tail of the Q.  Most people start by tracing a letter O, and then complete the Q by tracing its tail either toward their right eye or toward their left eye.

If you trace the tail of the Q toward your right eye, you’re imagining what a letter Q would look like from your perspective.  But if you trace the tail of the Q toward your left eye, you’re imagining what it would look like from the perspective of another person.

Basically, the point of the Q Test is to determine whether or not you have a natural tendency to consider the perspective of others.

Although considering the perspective of others is a positive under different circumstances, if you traced the letter Q with its tail toward your left eye, psychologists say that you failed the Q Test since it reveals a negative — you’re a good liar.  The reason why is that you have to be good at considering the perspective of others in order to be good at deceiving them with a believable lie.

So, as I now consider your perspective, dear reader, I bet you’re wondering: What does the Q Test have to do with data quality?

Like truth, beauty, and art, data quality can be said to be in the eyes of the beholder, or when data quality is defined, as it most often is, as fitness for the purpose of use — the eyes of the user.  But since most data has both multiple uses and users, data fit for the purpose of one use or user may not be fit for the purpose of other uses and users.  However, these multiple perspectives are considered irrelevant from the perspective of an individual user, who just needs quality data fit for the purpose of their own use.

The good news is that when it comes to data quality, most of us pass the Q Test, which means we’re not good liars.  The bad news is that since most of us pass the Q Test, we’re often only concerned about our own perspective about data quality, which is why so many organizations struggle to define data quality standards.

At the next discussion about your organization’s data quality standards, try inviting the participants to perform the Q Test.

Related Posts

The Point of View Paradox

You Say Potato and I Say Tater Tot

Data Myopia and Business Relativity

Beyond a “Single Version of the Truth”

DQ-BE: Single Version of the Time

Data and the Liar’s Paradox

The Fourth Law of Data Quality

Plato’s Data

Once Upon a Time in the Data

The Idea of Order in Data

Hell is other people’s data

Song of My Data

Related OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

• Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality, as well as his perspective on the relationship of data quality to master data management and data governance.

• Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses how your organization should approach data quality, including his call to action for your role in the data revolution.

• Data Driven — Guest Tom Redman (aka the “Data Doc”) discusses concepts from one of my favorite data quality books, which is his most recent book: Data Driven: Profiting from Your Most Important Business Asset.

• The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.

• Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”

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However, I have often experienced what I see as two fundamental flaws in the “fail faster” philosophy:

1. It requires that you define failure
2. It requires that you admit when you have failed

Most people — myself included — often fail both of these requirements.  Most people do not define failure, but instead assume that they will be successful (even though they conveniently do not define success either).  But even when people define failure, they often refuse to admit when they have failed.  In the face of failure, most people either redefine failure or extend the deadline (perhaps we should call it the fail line?) for when they will have to admit that they have failed.

We are often regaled with stories of persistence in spite of repeated failure, such as Thomas Edison’s famous remark:

“Many of life’s failures are people who did not realize how close they were to success when they gave up.”

Edison also remarked that he didn’t invent one way to make a lightbulb, but instead he invented more than 1,000 ways how not to make a lightbulb.  Each of those failed prototypes for a commercially viable lightbulb was instructive and absolutely essential to his eventual success.  But what if Edison had refused to define and admit failure?  How would he have known when to abandon one prototype and try another?  How would he have been able to learn valuable lessons from his repeated failure?

Josh Linkner recently blogged about failure being the dirty little secret of so-called overnight success, citing several examples, including Rovio (makers of the Angry Birds video game), Dyson vacuum cleaners, and WD-40.

Although these are definitely inspiring success stories, my concern is that often the only failure stories we hear are about people and companies that became famous for eventually succeeding.  In other words, we often hear eventually successful stories, and we almost never hear, or simply choose to ignore, the more common, and perhaps more useful, cautionary tales of abject failure.

It seems we have become so obsessed with telling stories that we have relegated both failure and success to the genre of fiction, which I fear is preventing us from learning any fact-based, and therefore truly valuable, lessons about failure and success.

Related Posts

The Winning Curve

Persistence

Mistake Driven Learning

The Fragility of Knowledge

The Wisdom of Failure

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