On March 13, 2009 I launched this blog and, just a month away from its 5th anniversary, this was its 500th post. For following Obsessive-Compulsive Data Quality for 5 years and 500 posts, I offer 5 words: Thank you all very much.
Read MoreData Quality By Example (DQ-BE) is an OCDQ regular segment that provides examples of data quality key concepts.
Over the weekend, in preparation for watching the Boston Red Sox, I bought some beer and pizza. Later that night, after a thrilling victory that sent the Red Sox to the 2013 World Series, I was cleaning up the kitchen and was about to throw out the receipt when I couldn’t help but notice two data quality issues.
First, although I had purchased Samuel Adams Octoberfest, the receipt indicated I had bought Spring Ale, which, although it’s still available in some places and it’s still good beer, it’s three seasonal beers (Summer Ale, Winter Lager, Octoberfest) old. This data quality issue impacts the store’s inventory and procurement systems (e.g., maybe the store orders more Spring Ale next year because people were apparently still buying it in October this year).
The second, and far more personal, data quality issue was that the age verification portion of my receipt indicated I was born on or before November 22, 1922, making me at least 91 years old! While I am of the age (42) typical of a midlife crisis, I wasn’t driving a new red sports car, just wearing my old Red Sox sports jersey and hat. As for the store, this data quality issue could be viewed as a regulatory compliance failure since it seems like their systems are set up by default to allow the sale of alcohol without proper age verification. Additionally, this data quality issue might make it seem like their only alcohol-purchasing customers are very senior citizens.
What examples (good or poor) of data quality have you encountered? Please share them by posting a comment below.
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DQ-BE: The Time Traveling Gift Card DQ-BE: Invitation to Duplication |
Sometimes Worse Data Quality is Better |
Inspired by the great Eagles song Hotel California, this DQ-Song “sings” about the common mistake of convening a council too early when starting a new data governance program. Now, of course, data governance is a very important and serious subject, which is why some people might question whether or not music is the best way to discuss data governance.
Although I understand that skepticism, I can’t help but recall the words of Frank Zappa:
“Information is not knowledge;
Knowledge is not wisdom;
Wisdom is not truth;
Truth is not beauty;
Beauty is not love;
Love is not music;
Music is the best.”
Down a dark deserted hallway, I walked with despair
As the warm smell of bagels rose up through the air
Up ahead in the distance, I saw a shimmering light
My head grew heavy and my sight grew dim
I had to attend another data governance council meeting
As I stood in the doorway
I heard the clang of the meeting bell
And I was thinking to myself
This couldn’t be heaven, but this could be hell
As stakeholders argued about the data governance way
There were voices down the corridor
I thought I heard them say . . .
Welcome to the Council Data Governance
Such a dreadful place (such a dreadful place)
Time crawls along at such a dreadful pace
Plenty of arguing at the Council Data Governance
Any time of year (any time of year)
You can hear stakeholders arguing there
Their agendas are totally twisted, with means to their own end
They use lots of pretty, pretty words, which I don’t comprehend
How they dance around the complex issues with sweet sounding threats
Some speak softly with remorse, some speak loudly without regrets
So I cried out to the stakeholders
Can we please reach consensus on the need for collaboration?
They said, we haven’t had that spirit here since nineteen ninety nine
And still those voices they’re calling from far away
Wake you up in the middle of this endless meeting
Just to hear them say . . .
Welcome to the Council Data Governance
Such a dreadful place (such a dreadful place)
Time crawls along at such a dreadful pace
They argue about everything at the Council Data Governance
And it’s no surprise (it’s no surprise)
To hear defending the status quo alibis
Bars on all of the windows
Rambling arguments, anything but concise
We are all just prisoners here
Of our own device
In the data governance council chambers
The bickering will never cease
They stab it with their steely knives
But they just can’t kill the beast
Last thing I remember, I was
Running for the door
I had to find the passage back
To the place I was before
Relax, said the stakeholders
We have been programmed by bureaucracy to believe
You can leave the council meeting any time you like
But success with data governance, you will never achieve!
Data Quality By Example (DQ-BE) is an OCDQ regular segment that provides examples of data quality key concepts.
As an avid reader, I tend to redeem most of my American Express Membership Rewards points for Barnes & Noble gift cards to buy new books for my Nook. As a data quality expert, I tend to notice when something is amiss with data. As shown above, for example, my recent gift card was apparently issued on — and only available for use until — January 1, 1900.
At first, I thought I might have encountered the time traveling gift card. However, I doubted the gift card would be accepted as legal tender in 1900. Then I thought my gift card was actually worth $1,410 (what $50 in 1900 would be worth today), which would allow me to buy a lot more books — as long as Barnes & Noble would overlook the fact the gift card expired 113 years ago.
Fortunately, I was able to use the gift card to purchase $50 worth of books in 2013.
So, I guess the moral of this story is that sometimes poor data quality does pay. However, it probably never pays to display your poor data quality to someone who runs an obsessive-compulsive data quality blog with a series about data quality by example.
What examples (good or poor) of data quality have you encountered in your time travels?
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DQ-BE: Invitation to Duplication |
Although I am an advocate for data science and big data done right, lately I have been sounding the Anti-Hype Horn with blog posts offering a contrarian’s view of unstructured data, forewarning you about the flying monkeys of big data, cautioning you against performing Cargo Cult Data Science, and inviting you to ponder the perils of the Infinite Inbox.
The hype of big data has resulted in a lot of people and vendors extolling its virtues with stories about how Internet companies, political campaigns, and new technologies have profited, or otherwise benefited, from big data. These stories are served up as alleged business cases for investing in big data and data science. Although some of these stories are fluff pieces, many of them accurately, and in some cases comprehensively, describe a real-world application of big data and data science. However, these messages most often lack a critically important component — applicability to your specific business. In Made to Stick: Why Some Ideas Survive and Others Die, Chip Heath and Dan Heath explained that “an accurate but useless idea is still useless. If a message can’t be used to make predictions or decisions, it is without value, no matter how accurate or comprehensive it is.”
Rowan & Martin’s Laugh-In was an American sketch comedy television series, which aired from 1968 to 1973. One of the recurring characters portrayed by Arte Johnson was Wolfgang the German soldier, who would often comment on the previous comedy sketch by saying (in a heavy and long-drawn-out German accent): “Very interesting . . . but stupid!”
From now on whenever someone shares another interesting story masquerading as a solid business case for big data that lacks any applicability beyond the specific scenario in the story, a common phenomenon I call The Laugh-In Effect of Big Data, my unapologetic response will resoundingly be: “Very interesting . . . but stupid!”
Occasionally it’s necessary to temper the unchecked enthusiasm accompanying the peak of inflated expectations associated with any hype cycle. This may be especially true for big data, and especially now since, as Svetlana Sicular of Gartner recently blogged, big data is falling into the trough of disillusionment and “to minimize the depth of the fall, companies must be at a high enough level of analytical and enterprise information management maturity combined with organizational support of innovation.”
I fear the fall may feel bottomless for those who fell hard for the hype and believe the Big Data Psychic capable of making better, if not clairvoyant, predictions. When, in fact, “our predictions may be more prone to failure in the era of big data,” explained Nate Silver in his book The Signal and the Noise: Why Most Predictions Fail but Some Don't. “There isn’t any more truth in the world than there was before the Internet. Most of the data is just noise, as most of the universe is filled with empty space.”
Proposing the 3Ss (Small, Slow, Sure) as a counterpoint to the 3Vs (Volume, Velocity, Variety), Stephen Few recently blogged about the slow data movement. “Data is growing in volume, as it always has, but only a small amount of it is useful. Data is being generated and transmitted at an increasing velocity, but the race is not necessarily for the swift; slow and steady will win the information race. Data is branching out in ever-greater variety, but only a few of these new choices are sure.”
Big data requires us to revisit information overload, a term that was originally about, not the increasing amount of information, but instead the increasing access to information. As Clay Shirky stated, “It’s not information overload, it’s filter failure.”
As Silver noted, the Internet (like the printing press before it) was a watershed moment in our increased access to information, but its data deluge didn’t increase the amount of truth in the world. And in today’s world, where many of us strive on a daily basis to prevent email filter failure and achieve what Merlin Mann called Inbox Zero, I find unfiltered enthusiasm about big data to be rather ironic, since big data is essentially enabling the data-driven decision making equivalent of the Infinite Inbox.
Imagine logging into your email every morning and discovering: You currently have (∞) Unread Messages.
However, I’m sure most of it probably would be spam, which you obviously wouldn’t have any trouble quickly filtering (after all, infinity minus spam must be a back of the napkin calculation), allowing you to only read the truly useful messages. Right?
As a kid, one of my favorite cartoons was Popeye the Sailor, who was empowered by eating spinach to take on many daunting challenges, such as battling his brawny nemesis Bluto for the affections of his love interest Olive Oyl, often kidnapped by Bluto.
I am reading the book The Half-life of Facts: Why Everything We Know Has an Expiration Date by Samuel Arbesman, who explained, while examining how a novel fact, even a wrong one, spreads and persists, that one of the strangest examples of the spread of an error is related to Popeye the Sailor. “Popeye, with his odd accent and improbable forearms, used spinach to great effect, a sort of anti-Kryptonite. It gave him his strength, and perhaps his distinctive speaking style. But why did Popeye eat so much spinach? What was the reason for his obsession with such a strange food?”
The truth begins over fifty years before the comic strip made its debut. “Back in 1870,” Arbesman explained, “Erich von Wolf, a German chemist, examined the amount of iron within spinach, among many other green vegetables. In recording his findings, von Wolf accidentally misplaced a decimal point when transcribing data from his notebook, changing the iron content in spinach by an order of magnitude. While there are actually only 3.5 milligrams of iron in a 100-gram serving of spinach, the accepted fact became 35 milligrams. Once this incorrect number was printed, spinach’s nutritional value became legendary. So when Popeye was created, studio executives recommended he eat spinach for his strength, due to its vaunted health properties, and apparently Popeye helped increase American consumption of spinach by a third!”
“This error was eventually corrected in 1937,” Arbesman continued, “when someone rechecked the numbers. But the damage had been done. It spread and spread, and only recently has gone by the wayside, no doubt helped by Popeye’s relative obscurity today. But the error was so widespread, that the British Medical Journal published an article discussing this spinach incident in 1981, trying its best to finally debunk the issue.”
“Ultimately, the reason these errors spread,” Arbesman concluded, “is because it’s a lot easier to spread the first thing you find, or the fact that sounds correct, than to delve deeply into the literature in search of the correct fact.”
What “spinach” has your organization been falsely consuming because of a data quality issue that was not immediately obvious, and which may have led to a long, and perhaps ongoing, history of data-driven decisions based on poor quality data?
Popeye said “I yam what I yam!” Your organization yams what your data yams, so you had better make damn sure it’s correct.
Recently on Twitter, Daragh O Brien and I discussed his proposed concept. “After Big Data,” Daragh tweeted, “we will inevitably begin to see the rise of MOData as organizations seek to grab larger chunks of data and digest it. What is MOData? It’s MO’Data, as in MOre Data. Or Morbidly Obese Data. Only good data quality and data governance will determine which.”
Daragh asked if MO’Data will be the Big Data Killer. I said only if MO’Data doesn’t include MO’BusinessInsight, MO’DataQuality, and MO’DataPrivacy (i.e., more business insight, more data quality, and more data privacy).
“But MO’Data is about more than just More Data,” Daragh replied. “It’s about avoiding Morbidly Obese Data that clogs data insight and data quality, etc.”
I responded that More Data becomes Morbidly Obese Data only if we don’t exercise better data management practices.
Agreeing with that point, Daragh replied, “Bring on MOData and the Pilates of Data Quality and Data Governance.”
To slightly paraphrase lines from one of my favorite movies — Airplane! — the Cloud is getting thicker and the Data is getting laaaaarrrrrger. Surely I know well that growing data volumes is a serious issue — but don’t call me Shirley.
Whether you choose to measure it in terabytes, petabytes, exabytes, HoardaBytes, or how much reality bites, the truth is we were consuming way more than our recommended daily allowance of data long before the data management industry took a tip from McDonald’s and put the word “big” in front of its signature sandwich. (Oh great . . . now I’m actually hungry for a Big Mac.)
But nowadays with silos replicating data, as well as new data, and new types of data, being created and stored on a daily basis, our data is resembling the size of Bob Parr in retirement, making it seem like not even Mr. Incredible in his prime possessed the super strength needed to manage all of our data. Those were references to the movie The Incredibles, where Mr. Incredible was a superhero who, after retiring into civilian life under the alias of Bob Parr, elicits the observation from this superhero costume tailor: “My God, you’ve gotten fat.” Yes, I admit not even Helen Parr (aka Elastigirl) could stretch that far for a big data joke.
Although Daragh’s concerns about morbidly obese data are valid, no superpowers (or other miracle exceptions) are needed to manage all of our data. In fact, it’s precisely when we are so busy trying to manage all of our data that we hoard countless bytes of data without evaluating data usage, gathering data requirements, or planning for data archival. It’s like we are trying to lose weight by eating more and exercising less, i.e., consuming more data and exercising less data quality and data governance. As Daragh said, only good data quality and data governance will determine whether we get more data or morbidly obese data.
Losing weight requires a healthy approach to both diet and exercise. A healthy approach to diet includes carefully choosing the food you consume and carefully controlling your portion size. A healthy approach to exercise includes a commitment to exercise on a regular basis at a sufficient intensity level without going overboard by spending several hours a day, every day, at the gym.
Swimming is a great form of exercise, but swimming in big data without having a clear business objective before you jump into the pool is like telling your boss that you didn’t get any work done because you decided to spend all day working out at the gym.
Carefully choosing the data you consume and carefully controlling your data portion size is becoming increasingly important since big data is forcing us to revisit information overload. However, the main reason that traditional data management practices often become overwhelmed by big data is because traditional data management practices are not always the right approach.
We need to acknowledge that some big data use cases differ considerably from traditional ones. Data modeling is still important and data quality still matters, but how much data modeling and data quality is needed before big data can be effectively used for business purposes will vary. In order to move the big data discussion forward, we have to stop fiercely defending our traditional perspectives about structure and quality. We also have to stop fiercely defending our traditional perspectives about analytics, since there will be some big data use cases where depth and detailed analysis may not be necessary to provide business insight.
Jim Ericson explained that your data is big enough. Rich Murnane explained that bigger isn’t better, better is better. Although big data may indeed be followed by more data that doesn’t necessarily mean we require more data management in order to prevent more data from becoming morbidly obese data. I think that we just need to exercise better data management.
Data Quality (DQ) View is an OCDQ regular segment. Each DQ-View is a brief video discussion of a data quality key concept.
In my experience, all organizations cycle through five stages while coming to terms with the daunting challenges of data quality, which are somewhat similar to The Five Stages of Grief. So, in this short video, I explain The Five Stages of Data Quality:
If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link:DQ-View on Vimeo
You can also watch a regularly updated page of my videos by clicking on this link:OCDQ Videos
Posts related to the Denial Stage of Data Quality:
Data Quality and Chicken Little Syndrome
The Illusion-of-Quality Effect
Perception Filters and Data Quality
“Some is not a number and soon is not a time”
Posts related to the Anger Stage of Data Quality:
Jack Bauer and Enforcing Data Governance Policies
Beware the Data Governance Ides of March
Aristotle, Data Governance, and Lead Rulers
Why isn’t our data quality worse?
Don’t Do Less Bad; Do Better Good
Posts related to the Bargaining Stage of Data Quality:
Data Quality and Miracle Exceptions
Do you believe in Magic (Quadrants)?
Which came first, the Data Quality Tool or the Business Need?
Posts related to the Depression Stage of Data Quality:
There is No Such Thing as a Root Cause
The Dichotomy Paradox, Data Quality and Zero Defects
Data Quality and the Bystander Effect
Posts related to the Acceptance Stage of Data Quality:
You only get a Return from something you actually Invest in
Last week, when I published my blog post Lightning Strikes the Cloud, I unintentionally demonstrated three important things about data quality.
The first thing I demonstrated was even an obsessive-compulsive data quality geek is capable of data defects, since I initially published the post with the title Lightening Strikes the Cloud, which is an excellent example of the difference between validity and accuracy caused by the Cupertino Effect, since although lightening is valid (i.e., a correctly spelled word), it isn’t contextually accurate.
The second thing I demonstrated was the value of shining a social light on data quality — the value of using collaborative tools like social media to crowd-source data quality improvements. Thankfully, Julian Schwarzenbach quickly noticed my error on Twitter. “Did you mean lightning? The concept of lightening clouds could be worth exploring further,” Julian humorously tweeted. “Might be interesting to consider what happens if the cloud gets so light that it floats away.” To which I replied that if the cloud gets so light that it floats away, it could become Interstellar Computing or, as Julian suggested, the start of the Intergalactic Net, which I suppose is where we will eventually have to store all of that big data we keep hearing so much about these days.
The third thing I demonstrated was the potential dark side of data cleansing, since the only remaining trace of my data defect is a broken URL. This is an example of not providing a well-documented audit trail, which is necessary within an organization to communicate data quality issues and resolutions.
Communication and collaboration are essential to finding our way with data quality. And social media can help us by providing more immediate and expanded access to our collective knowledge, experience, and wisdom, and by shining a social light that illuminates the shadows cast upon data quality issues when a perception filter or bystander effect gets the better of our individual attention or undermines our collective best intentions — which, as I recently demonstrated, occasionally happens to all of us.
Data Quality and the Cupertino Effect
Are you turning Ugly Data into Cute Information?
The Wisdom of the Social Media Crowd
Perception Filters and Data Quality
Data Quality and the Bystander Effect
The Family Circus and Data Quality
Metadata, Data Quality, and the Stroop Test
Like many young intellectuals, the only part of the Sunday newspaper I read growing up was the color comics section, and one of my favorite comic strips was The Family Circus created by cartoonist Bil Keane. One of the recurring themes of the comic strip was a set of invisible gremlins that the children used to shift blame for any misdeeds, including Ida Know, Not Me, and Nobody.
Although I no longer read any section of the newspaper on any day of the week, this Sunday morning I have been contemplating how this same set of invisible gremlins is used by many people throughout most organizations to shift blame for any incidents when poor data quality negatively impacted business activities, especially since, when investigating the root cause, you often find that Ida Know owns the data, Not Me is accountable for data governance, and Nobody takes responsibility for data quality.
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.”)
Today is February 14 — Valentine’s Day — the annual celebration of enduring romance, where true love is publicly judged according to your willingness to purchase chocolate, roses, and extremely expensive jewelry, and privately judged in ways that nobody (and please, trust me when I say nobody) wants to see you post on Twitter, Facebook, YouTube, or your blog.
Valentine’s Day is for people in love to celebrate their love privately in whatever way works best for them.
But since your data needs love too, this blog post provides a mashup of love songs for your data.
I’ve got sunshine on a cloud computing day
When it’s cold outside, I’ve got backups from the month of May
I guess you’d say, what can make me feel this way?
My data, my data, my data
Singing about my data
My data
My data’s so beautiful
And I tell it every day
When I see your user interface
There’s not a thing that I would change
Because my data, you’re amazing
Just the way you are
You’re amazing data
Just the way you are
They say we’re young and we don’t know
We won’t find data quality issues until we grow
Well I don’t know if that is true
Because you got me, data
And data, I got you
I got you, data
Look into my eyes, and you will see
What my data means to me
Don’t tell me data quality is not worth trying for
Don’t tell me it’s not worth fighting for
You know it’s true
Everything I do, I do data quality for you
I can’t make you love data if you don’t
I can’t make your heart feel something it won’t
But there’s nothing you can do that can’t be done
Nothing you can sing that can’t be sung
Nothing you can make that can’t be made
All you need is love . . . for data
Love for data is all you need
Business people working hard all day and through the night
Their database queries searching for business insight
Some will win, some will lose
Some were born to sing the data quality blues
Oh, the need for business insight never ends
It goes on and on and on and on
Don’t stop believing
Hold on to that data loving feeling
Look at your data, I know its poor quality is showing
Look at your organization, you don’t know where it’s going
I don’t know much, but I know your data needs love too
And that may be all I need to know
Nothing compares to data quality, no worries or cares
Business regrets and decision mistakes, they’re memories made
But if you don’t continuously improve, how bittersweet that will taste
I wish nothing but the best for you
I wish nothing but the best for your data too
Don’t forget data quality, I beg, please remember I said
Sometimes quality lasts in data, but sometimes it hurts instead
Happy Valentine’s Day to you and yours
Happy Data Quality to you and your data
The recent #GartnerChat on Big Data was an excellent Twitter discussion about what I often refer to as the Seven Letter Tsunami of the data management industry, which as Gartner Research explains, although the term acknowledges the exponential growth, availability, and use of information in today’s data-rich landscape, big data is about more than just data volume. Data variety (i.e., structured, semi-structured, and unstructured data, as well as other types, such as the sensor data emanating from the Internet of Things), and data velocity (i.e., how fast data is produced and how fast data must be processed to meet demand) are also key characteristics of the big challenges associated with the big buzzword that big data has become over the last year.
Since ours is an industry infatuated with buzzwords, Timo Elliott remarked “new terms arise because of new technology, not new business problems. Big Data came from a need to name Hadoop [and other technologies now being relentlessly marketed as big data solutions], so anybody using big data to refer to business problems is quickly going to tie themselves in definitional knots.”
To which Mark Troester responded, “the hype of Hadoop is driving pressure on people to keep everything — but they ignore the difficulty in managing it.” John Haddad then quipped that “big data is a hoarders dream,” which prompted Andy Bitterer to coin the term HoardaByte for measuring big data, and then asking, “Would the real Big Data Lebowski please stand up?”
Although it’s probably no surprise that a blogger with obsessive-compulsive in the title of his blog would like Bitterer’s new term, the fact is that whether you choose to measure it in terabytes, petabytes, exabytes, HoardaBytes, or how much reality bitterly bites, our organizations have been compulsively hoarding data for a long time.
And with silos replicating data as well as new data, and new types of data, being created and stored on a daily basis, managing all of the data is not only becoming impractical, but because we are too busy with the activity of trying to manage all of it, we are hoarding countless bytes of data without evaluating data usage, gathering data requirements, or planning for data archival.
In The Big Lebowski, Jeff Lebowski (“The Dude”) is, in a classic data quality blunder caused by matching on person name only, mistakenly identified as millionaire Jeffrey Lebowski (“The Big Lebowski”) in an eccentric plot expected from a Coen brothers film, which, since its release in the late 1990s, has become a cult classic and inspired a religious following known as Dudeism.
Historically, a big part of the problem in our industry has been the fact that the word “data” is prevalent in the names we have given industry disciplines and enterprise information initiatives. For example, data architecture, data quality, data integration, data migration, data warehousing, master data management, and data governance — to name but a few.
However, all this achieved was to perpetuate the mistaken identification of data management as an esoteric technical activity that played little more than a minor, supporting, and often uncredited, role within the business activities of our organizations.
But since the late 1990s, there has been a shift in the perception of data. The real data deluge has not been the rising volume, variety, and velocity of data, but instead the rising awareness of the big impact that data has on nearly every aspect of our professional and personal lives. In this brave new data world, companies like Google and Facebook have built business empires mostly out of our own personal data, which is why, like it or not, as individuals, we must accept that we are all data geeks now.
All of the hype about Big Data is missing the point. The reality is that Data is Big — meaning that data has now so thoroughly pervaded mainstream culture that data has gone beyond being just a cult classic for the data management profession, and is now inspiring an almost religious following that we could call Dataism.
“The Dude abides. I don’t know about you, but I take comfort in that,” remarked The Stranger in The Big Lebowski.
The Data must also abide. And the Data must abide both the Business and the Individual. The Data abides the Business if data proves useful to our business activities. The Data abides the Individual if data protects the privacy of our personal activities.
The Data abides. I don’t know about you, but I would take more comfort in that than in any solutions The Stranger Salesperson wants to sell me that utilize an eccentric sales pitch involving HoardaBytes and the Big Data Lebowski.
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
During this episode, recorded on the first of three occurrences of Friday the 13th in 2012, I discuss scary calendar effects.
In other words, I discuss how schedules, deadlines, and other date-related aspects can negatively affect enterprise initiatives such as data quality, master data management, and data governance.
Please Beware: This episode concludes with the OCDQ Radio Theater production of Data Quality and Friday the 13th.
Clicking on the link will take you to the episode’s blog post:
Obsessive-Compulsive Data Quality (OCDQ) is a blog offering a vendor-neutral perspective on data quality and its related disciplines.
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