Too Big to Ignore

/

OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Phil Simon shares his sage advice for getting started with big data, including the importance of having a data-oriented mindset, that ambitious long-term goals should give way to more reasonable and attainable short-term objectives, and always remembering that big data is just another means toward solving business problems.

Phil Simon is a sought-after speaker and the author of five management books, most recently Too Big to Ignore: The Business Case for Big Data.  A recognized technology expert, he consults companies on how to optimize their use of technology.  His contributions have been featured on NBC, CNBC, ABC News, Inc. magazine, BusinessWeek, Huffington Post, Globe and Mail, Fast Company, Forbes, the New York Times, ReadWriteWeb, and many other sites.

Popular OCDQ Radio Episodes

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

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • 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.
  • Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.
  • 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.

DQ-Tip: “An information centric organization...”

/

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“An information centric organization is an organization driven from high-quality, complete, and timely information that is relevant to its goals.”

This DQ-Tip is from the new book Patterns of Information Management by Mandy Chessell and Harald Smith.

“An organization exists for a purpose,” Chessell and Smith explained.  “It has targets to achieve and long-term aspirations.  An organization needs to make good use of its information to achieve its goals.”  In order to do this, they recommend that you define an information strategy that lays out why, what, and how your organization will manage its information:

  • Why — The business imperatives that drive the need to be information centric, which helps focus information management efforts on the activities that deliver value to the organization.
  • What — The type of information that you must manage to deliver on those business imperatives, which includes the subject areas to cover, which attributes within each subject area that need to be managed, the valid values for those attributes, and the information management policies (such as retention and protection) that the organization wants to implement.
  • How — The information management principles that provide the general rules for how information is to be managed by the information systems and the people using them along with how information flows between them.

Developing an information strategy, according to Chessell and Smith, “creates a set of objectives for the organization, which guides the investment in information management technology and related solutions that support the business.  Starting with the business imperatives ensures the information management strategy is aligned with the needs of the organization, making it easier to demonstrate its relevance and value.”

Chessell and Smith also noted that “technology alone is not sufficient to ensure the quality, consistency, and flexibility of an organization’s information.  Classify the people connected to the organization according to their information needs and skills, provide common channels of communication and knowledge sharing about information, and user interfaces and reports through which they can access the information as appropriate.”

Chessell and Smith explained that the attitudes and skills of the organization’s people will be what enables the right behaviors in everyday operations, which is a major determination of the success of an information management program.

 

Related Posts

DQ-Tip: “The quality of information is directly related to...”

DQ-Tip: “Undisputable fact about the value and use of data...”

DQ-Tip: “Data quality tools do not solve data quality problems...”

DQ-Tip: “There is no such thing as data accuracy...”

DQ-Tip: “Data quality is primarily about context not accuracy...”

DQ-Tip: “There is no point in monitoring data quality...”

DQ-Tip: “Don't pass bad data on to the next person...”

DQ-Tip: “...Go talk with the people using the data”

DQ-Tip: “Data quality is about more than just improving your data...”

DQ-Tip: “Start where you are...”

i blog of Data glad and big

/

I recently blogged about the need to balance the hype of big data with some anti-hype.  My hope was, like a collision of matter and anti-matter, the hype and anti-hype would cancel each other out, transitioning our energy into a more productive discussion about big data.  But, of course, few things in human discourse ever reach such an equilibrium, or can maintain it for very long.

For example, Quentin Hardy recently blogged about six big data myths based on a conference presentation by Kate Crawford, who herself also recently blogged about the hidden biases in big data.  “I call B.S. on all of it,” Derrick Harris blogged in his response to the backlash against big data.  “It might be provocative to call into question one of the hottest tech movements in generations, but it’s not really fair.  That’s because how companies and people benefit from big data, data science or whatever else they choose to call the movement toward a data-centric world is directly related to what they expect going in.  Arguing that big data isn’t all it’s cracked up to be is a strawman, pure and simple — because no one should think it’s magic to begin with.”

In their new book Big Data: A Revolution That Will Transform How We Live, Work, and Think, Viktor Mayer-Schonberger and Kenneth Cukier explained that “like so many new technologies, big data will surely become a victim of Silicon Valley’s notorious hype cycle: after being feted on the cover of magazines and at industry conferences, the trend will be dismissed and many of the data-smitten startups will flounder.  But both the infatuation and the damnation profoundly misunderstand the importance of what is taking place.  Just as the telescope enabled us to comprehend the universe and the microscope allowed us to understand germs, the new techniques for collecting and analyzing huge bodies of data will help us make sense of our world in ways we are just starting to appreciate.  The real revolution is not in the machines that calculate data, but in data itself and how we use it.”

Although there have been numerous critical technology factors making the era of big data possible, such as increases in the amount of computing power, decreases in the cost of data storage, increased network bandwidth, parallel processing frameworks (e.g., Hadoop), scalable and distributed models (e.g., cloud computing), and other techniques (e.g., in-memory computing), Mayer-Schonberger and Cukier argued that “something more important changed too, something subtle.  There was a shift in mindset about how data could be used.  Data was no longer regarded as static and stale, whose usefulness was finished once the purpose for which it was collected was achieved.  Rather, data became a raw material of business, a vital economic input, used to create a new form of economic value.”

“In fact, with the right mindset, data can be cleverly used to become a fountain of innovation and new services.  The data can reveal secrets to those with the humility, the willingness, and the tools to listen.”

Pondering this big data war of words reminded me of the E. E. Cummings poem i sing of Olaf glad and big, which sings of Olaf, a conscientious objector forced into military service, who passively endures brutal torture inflicted upon him by training officers, while calmly responding (pardon the profanity): “I will not kiss your fucking flag” and “there is some shit I will not eat.”

Without question, big data has both positive and negative aspects, but the seeming unwillingness of either side in the big data war of words to “kiss each other’s flag,” so to speak, is not as concerning to me as is the conscientious objection to big data and data science expanding into realms where people and businesses were not used to enduring its influence.  For example, some will feel that data-driven audits of their decision-making is like brutal torture inflicted upon their less-than data-driven intuition.

E.E. Cummings sang the praises of Olaf “because unless statistics lie, he was more brave than me.”  i blog of Data glad and big, but I fear that, regardless of how big it is, “there is some data I will not believe” will be a common refrain by people who will lack the humility and willingness to listen to data, and who will not be brave enough to admit that statistics don’t always lie.

 

Related Posts

The Need for Data Philosophers

On Philosophy, Science, and Data

OCDQ Radio - Demystifying Data Science

OCDQ Radio - Data Quality and Big Data

Big Data and the Infinite Inbox

The Laugh-In Effect of Big Data

HoardaBytes and the Big Data Lebowski

Magic Elephants, Data Psychics, and Invisible Gorillas

Will Big Data be Blinded by Data Science?

The Graystone Effects of Big Data

Information Overload Revisited

Exercise Better Data Management

A Tale of Two Datas

Our Increasingly Data-Constructed World

The Wisdom of Crowds, Friends, and Experts

Data Separates Science from Superstition

Headaches, Data Analysis, and Negativity Bias

Why Data Science Storytelling Needs a Good Editor

Predictive Analytics, the Data Effect, and Jed Clampett

Rage against the Machines Learning

The Flying Monkeys of Big Data

Cargo Cult Data Science

Speed Up Your Data to Slow Down Your Decisions

It’s Not about being Data-Driven

Big Data, Sporks, and Decision Frames

Big Data: Structure and Quality

Darth Vader, Big Data, and Predictive Analytics

Big Data, Predictive Analytics, and the Ideal Chronicler

What Magic Tricks teach us about Data Science

What Mozart for Babies teaches us about Data Science

Business Analytics for Midsize Businesses

/

As this growing list of definitions for big data attests, big data evangelist and IBM thought leader James Kobielus rightfully warns that big data is in danger of definitional overkill.  But most midsize business owners are less concerned about defining big data as they are about, as Laurie McCabe recently blogged, determining whether big data is relevant for their business.

“The fact of the matter is, big is a relative term,” McCabe explained, “relative to the amount of information that your organization needs to sift through to find the insights you need to operate the business more proactively and profitably.”

McCabe also noted that this is not just a problem for big businesses, since getting better insights from the data you already have is a challenge for businesses of all sizes.  Midsize businesses “may not be dealing with terabytes of data,” McCabe explained, “but many are finding that tools that used to suffice—such as Excel spreadsheets—fall short even when it comes to analyzing internal transactional databases.”  McCabe also provided recommendations for how midsize businesses can put big data to work.

The recent IBM study The Case for Business Analytics in Midsize Firms lists big data as one of the trends making a compelling case for the growing importance of business analytics for midsize businesses.  The study also noted that important functional data continues to live in departmental spreadsheets, and state-of-the-art business analytics solutions are needed to make it easy to pull all that data, along with data from other sources, together in a meaningful way.  Despite the common misconception that such solutions are too expensive for midsize businesses, solutions are now available that can deliver analytics capabilities to help overcome big data challenges without requiring a big upfront investment in hardware or software.

Phil Simon, author of Too Big to Ignore: The Business Case for Big Data, recently blogged about reporting versus analytics, explaining the essence of analytics is it goes beyond the what and where provided by reporting, and tries to explain the why.

Big data isn’t the only reason why analytics is becoming more of a necessity.  But with the barriers to what it costs and where it can be deployed becoming easier to overcome, business analytics is becoming more commonplace in midsize businesses.

IBM Logo.jpg

The Need for Data Philosophers

/

In my post On Philosophy, Science, and Data, I explained that although some argue philosophy only reigns in the absence of data while science reigns in the analysis of data, a conceptual bridge still remains between analysis and insight, the crossing of which is itself a philosophical exercise.  Therefore, I argued that an endless oscillation persists between science and philosophy, which is why, despite the fact that all we hear about is the need for data scientists, there’s also a need for data philosophers.

Of course, the debate between science and philosophy is a very old one, as is the argument we need both.  In my previous post, I slightly paraphrased Immanuel Kant (“perception without conception is blind and conception without perception is empty”) by saying that science without philosophy is blind and philosophy without science is empty.

In his book Cosmic Apprentice: Dispatches from the Edges of Science, Dorion Sagan explained that science and philosophy hang “in a kind of odd balance, watching each other, holding hands.  Science’s eye for detail, buttressed by philosophy’s broad view, makes for a kind of alembic, an antidote to both.  Although philosophy isn’t fiction, it can be more personal, creative and open, a kind of counterbalance for science even as it argues that science, with its emphasis on a kind of impersonal materialism, provides a crucial reality check for philosophy and a tendency to over-theorize that’s inimical to the scientific spirit.  Ideally, in the search for truth, science and philosophy, the impersonal and autobiographical, can keep each other honest in a kind of open circuit.”

“Science’s spirit is philosophical,” Sagan concluded.  “It is the spirit of questioning, of curiosity, of critical inquiry combined with fact-checking.  It is the spirit of being able to admit you’re wrong, of appealing to data, not authority.”

“Science,” as his father Carl Sagan said, “is a way of thinking much more than it is a body of knowledge.”  By extension, we could say that data science is about a way of thinking much more than it is about big data or about being data-driven.

I have previously blogged that science has always been about bigger questions, not bigger data.  As Claude Lévi-Strauss said, “the scientist is not a person who gives the right answers, but one who asks the right questions.”  As far as data science goes, what are the right questions?  Data scientist Melinda Thielbar proposes three key questions (Actionable? Verifiable? Repeatable?).

Here again we see the interdependence of science and philosophy.  “Philosophy,” Marilyn McCord Adams said, “is thinking really hard about the most important questions and trying to bring analytic clarity both to the questions and the answers.”

“Philosophy is critical thinking,” Don Cupitt said. “Trying to become aware of how one’s own thinking works, of all the things one takes for granted, of the way in which one’s own thinking shapes the things one’s thinking about.”  Yes, even a data scientist’s own thinking could shape the things they are thinking scientifically about.  James Kobielus has blogged about five biases that may crop up in a data scientist’s work (Cognitive, Selection, Sampling, Modeling, Funding).

“Data science has a bright future ahead,” explained Hilary Mason in a recent interview.  “There will only be more data, and more of a need for people who can find meaning and value in that data.  We’re also starting to see a greater need for data engineers, people to build infrastructure around data and algorithms, and data artists, people who can visualize the data.”

I agree with Mason, and I would add that we are also starting to see a greater need for data philosophers, people who can, borrowing the words that Anthony Kenny used to define philosophy, “think as clearly as possible about the most fundamental concepts that reach through all the disciplines.”

Keep Looking Up Insights in Data

/

In a previous post, I used the history of the Hubble Space Telescope to explain how data cleansing saves lives, based on a true story I read in the book Space Chronicles: Facing the Ultimate Frontier by Neil deGrasse Tyson.  In this post, Hubble and Tyson once again provide the inspiration for an insightful metaphor about data quality.

Hubble is one of dozens of space telescopes of assorted sizes and shapes orbiting the Earth.  “Each one,” Tyson explained, “provides a view of the cosmos that is unobstructed, unblemished, and undiminished by Earth’s turbulent and murky atmosphere.  They are designed to detect bands of light invisible to the human eye, some of which never penetrate Earth’s atmosphere.  Hubble is the first and only space telescope to observe the universe using primarily visible light.  Its stunningly crisp, colorful, and detailed images of the cosmos make Hubble a kind of supreme version of the human eye in space.”

This is how we’d like the quality of data to be when we’re looking for business insights.  High-quality data provides stunningly crisp, colorful, and detailed images of the business cosmos, acting as a kind of supreme version of the human eye in data.

However, despite their less-than-perfect vision, the limitations of Earth-based telescopes still facilitated significant scientific breakthroughs long before Hubble became the first space telescope in 1990.

In 1609, when the Italian physicist and astronomer Galileo Galilei turned a telescope of his own design to the sky, as Tyson explained, he “heralded a new era of technology-aided discovery, whereby the capacities of the human senses could be extended, revealing the natural world in unprecedented, even heretical ways.  The fact that Galileo revealed the Sun to have spots, the planet Jupiter to have satellites [its four moons: Callisto, Ganymede, Europa, Io], and Earth not to be the center of all celestial motion was enough to unsettle centuries of Aristotelian teachings by the Catholic Church and to put Galileo under house arrest.”

And in 1964, another Earth-based telescope, this one operated by the American astronomers Arno Penzias and Robert Wilson at AT&T Bell Labs, was responsible for what is widely considered the most important single discovery in astrophysics, what’s now known as cosmic microwave background radiation, and for which Penzias and Wilson won the 1978 Nobel Prize in Physics.

Recently, I’ve blogged about how there are times when perfect data quality is necessary, when we need the equivalent of a space telescope, and times when okay data quality is good enough, when the equivalent of an Earth-based telescope will do.

What I would like you to take away from this post is that perfect data quality is not a prerequisite for the discovery of new business insights.  Even when data doesn’t provide a perfect view of the business cosmos, even when it’s partially obstructed, blemished, or diminished by the turbulent and murky atmosphere of poor quality, data can still provide business insights.

This doesn’t mean that you should settle for poor data quality, just that you shouldn’t demand perfection before using data.

Tyson ends each episode of his StarTalk Radio program by saying “keep looking up,” so I will end this blog post by saying, even when its quality isn’t perfect, keep looking up insights in data.

 

Related Posts

Data Quality and the OK Plateau

When Poor Data Quality Kills

How Data Cleansing Saves Lives

The Dichotomy Paradox, Data Quality and Zero Defects

The Asymptote of Data Quality

To Our Data Perfectionists

Data Quality and Miracle Exceptions

Data Quality: Quo Vadimus?

The Seventh Law of Data Quality

A Tale of Two Q’s

Data Quality and The Middle Way

Stop Poor Data Quality STOP

Freudian Data Quality

Predictably Poor Data Quality

This isn’t Jeopardy

Satisficing Data Quality

Business Intelligence for Midsize Businesses

/

Business intelligence is one of those phrases that everyone agrees is something all organizations, regardless of their size, should be doing.  After all, no organization would admit to doing business stupidity.  Nor, I presume, would any vendor admit to selling it.

But not everyone seems to agree on what the phrase means.  Personally, I have always defined business intelligence as the data analytics performed in support of making informed business decisions (i.e., for me, business intelligence = decision support).

Oftentimes, this analytics is performed on data integrated, cleansed, and consolidated into a repository (e.g., a data warehouse).  Other times, it’s performed on a single data set (e.g., a customer information file).  Either way, business decision makers interact with the analytical results via static reports, data visualizations, dynamic dashboards, and ad hoc querying and reporting tools.

But robust business intelligence and analytics solutions used to be perceived as something only implemented by big businesses, as evinced in the big price tags usually associated with them.  However, free and open source software, cloud computing, mobile, social, and a variety of as-a-service technologies drove the consumerization of IT, driving down the costs of solutions, enabling small and midsize businesses to afford them.  Additionally, the open data movement lead to a wealth of free public data sets that can be incorporated into business intelligence and analytics solutions (examples can be found at kdnuggets.com/datasets).

Lyndsay Wise, author of the insightful book Using Open Source Platforms for Business Intelligence (to listen to a podcast about the book, click here: OSBI on OCDQ Radio), recently blogged about business intelligence for small and midsize businesses.

Wise advised that “recent market changes have shifted the market in favor of small and midsize businesses.  Before this, most were limited by requirements for large infrastructures, high-cost licensing, and limited solution availability.  With this newly added flexibility and access to lower price points, business intelligence and analytics solutions are no longer out of reach.”

 

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. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies, or opinions.

 

Related Posts

The Big Datastillery

Smart Big Data Adoption for Midsize Businesses

Big Data is not just for Big Businesses

Big Data Lessons from Orbitz

The Graystone Effects of Big Data

Will Big Data be Blinded by Data Science?

Social Business is more than Social Marketing

Social Media Marketing: From Monologues to Dialogues

Social Media for Midsize Businesses

Barriers to Cloud Adoption

Leveraging the Cloud for Application Development

Cloud Computing for Midsize Businesses

Cloud Computing is the New Nimbyism

Devising a Mobile Device Strategy

The Age of the Mobile Device

Word of Mouth has become Word of Data

Information Asymmetry versus Empowered Customers

Talking Business about the Weather

The Laugh-In Effect of Big Data

/

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!”

 

Related Posts

On Philosophy, Science, and Data

OCDQ Radio - Demystifying Data Science

OCDQ Radio - Data Quality and Big Data

Big Data and the Infinite Inbox

HoardaBytes and the Big Data Lebowski

Will Big Data be Blinded by Data Science?

Data Silence

Magic Elephants, Data Psychics, and Invisible Gorillas

The Graystone Effects of Big Data

Big Data el Memorioso

Information Overload Revisited

Exercise Better Data Management

A Tale of Two Datas

Dot Collectors and Dot Connectors

The Wisdom of Crowds, Friends, and Experts

A Contrarian’s View of Unstructured Data

The Flying Monkeys of Big Data

Cargo Cult Data Science

A Statistically Significant Resolution for 2013

Speed Up Your Data to Slow Down Your Decisions

Rage against the Machines Learning

It’s Not about being Data-Driven

Big Data, Sporks, and Decision Frames

Big Data: Structure and Quality

Darth Vader, Big Data, and Predictive Analytics

Big Data, Predictive Analytics, and the Ideal Chronicler

The Big Data Theory

Swimming in Big Data

What Magic Tricks teach us about Data Science

What Mozart for Babies teaches us about Data Science

When Poor Data Quality Kills

/

In my previous post, I made the argument that many times it’s okay to call data quality as good as it needs to get, as opposed to demanding data perfection.  However, a balanced perspective demands acknowledging there are times when nothing less than perfect data quality is necessary.  In fact, there are times when poor data quality can have deadly consequences.

In his book The Information: A History, a Theory, a Flood, James Gleick explained “pharmaceutical names are a special case: a subindustry has emerged to coin them, research them, and vet them.  In the United States, the Food and Drug Administration reviews proposed drug names for possible collisions, and this process is complex and uncertain.  Mistakes cause death.”

“Methadone, for opiate dependence, has been administrated in place of Metadate, for attention-deficit disorder, and Taxcol, a cancer drug, for Taxotere, a different cancer drug, with fatal results.  Doctors fear both look-alike errors and sound-alike errors: Zantac/Xanax; Verelan/Virilon.  Linguists devise scientific measures of the distance between names.  But Lamictal and Lamisil and Ludiomil and Lomotil are all approved drug names.”

All data matching techniques, such as edit distance functions, phonetic comparisons, and more complex algorithms, provide a way to represent (e.g., numeric probabilities, weighted percentages, odds ratios, etc.) the likelihood that two non-exact matching data items are the same.  No matter what data quality software vendors tell you, all data matching techniques are susceptible to false negatives (data that did not match, but should have) and false positives (data that matched, but should not have).

This pharmaceutical example is one case where a false positive could be deadly, a time when poor data quality kills.  Admittedly, this is an extreme example.  What other examples can you offer where perfect data quality is actually a necessity?

 

Related Posts

Data Quality and the OK Plateau

How Data Cleansing Saves Lives

What going to the dentist taught me about data quality

The Dichotomy Paradox, Data Quality and Zero Defects

The Asymptote of Data Quality

To Our Data Perfectionists

Data Quality and Miracle Exceptions

Data Quality: Quo Vadimus?

Data and its Relationships with Quality

DQ-Tip: “There is no such thing as data accuracy...”

The Seventh Law of Data Quality

A Tale of Two Q’s

Paleolithic Rhythm and Data Quality

Groundhog Data Quality Day

Data Quality and The Middle Way

Stop Poor Data Quality STOP

When Poor Data Quality Calls

Freudian Data Quality

Predictably Poor Data Quality

Satisficing Data Quality

Data Quality and the OK Plateau

/

In his book Moonwalking with Einstein: The Art and Science of Remembering, Joshua Foer explained that “when people first learn to use a keyboard, they improve very quickly from sloppy single-finger pecking to careful two-handed typing, until eventually the fingers move so effortlessly across the keys that the whole process becomes unconscious and the fingers seem to take on a mind of their own.”

“At this point,” Foer continued, “most people’s typing skills stop progressing.  They reach a plateau.  If you think about it, it’s a strange phenomenon.  After all, we’ve always been told that practice makes perfect, and many people sit behind a keyboard for at least several hours a day in essence practicing their typing.  Why don’t they just keep getting better and better?”

Foer then recounted research performed in the 1960s by the psychologists Paul Fitts and Michael Posner, which described the three stages that everyone goes through when acquiring a new skill:

  1. Cognitive — During this stage, you intellectualize the task and discover new strategies to accomplish it more proficiently.
  2. Associative — During this stage, you concentrate less, make fewer major errors, and generally become more efficient.
  3. Autonomous — During this stage, you have gotten as good as you need to get, and are basically running on autopilot.

“During that autonomous stage,” Foer explained, “you lose conscious control over what you are doing.  Most of the time that’s a good thing.  Your mind has one less thing to worry about.  In fact, the autonomous stage seems to be one of those handy features that evolution worked out for our benefit.  The less you have to focus on the repetitive tasks of everyday life, the more you can concentrate on the stuff that really matters, the stuff you haven’t seen before.  And so, once we’re just good enough at typing, we move it to the back of our mind’s filing cabinet and stop paying it any attention.”

“You can see this shift take place in fMRI scans of people learning new skills.  As a task becomes automated, parts of the brain involved in conscious reasoning become less active and other parts of the brain take over.  You could call it the OK plateau, the point at which you decide you’re OK with how good you are at something, turn on autopilot, and stop improving.”

“We all reach OK plateaus in most things we do,” Foer concluded.  “We learn how to drive when we’re in our teens and once we’re good enough to avoid tickets and major accidents, we get only incrementally better.  My father has been playing golf for forty years, and he’s still a duffer.  In four decades his handicap hasn’t fallen even a point.  Why?  He reached an OK plateau.”

I believe that data quality improvement initiatives also eventually reach an OK Plateau, a point just short of data perfection, where the diminishing returns of chasing after zero defects gives way to calling data quality as good as it needs to get.

As long as the autopilot is on, accepting data quality is a journey not a destination, preventing data quality from getting worse, and making sure best practices don’t stop being practiced, then I’m OK with data quality and the OK plateau.  Are you OK?

 

Related Posts

The Dichotomy Paradox, Data Quality and Zero Defects

The Asymptote of Data Quality

To Our Data Perfectionists

Data Quality and Miracle Exceptions

Data Quality: Quo Vadimus?

The Seventh Law of Data Quality

Data Quality and The Middle Way

Freudian Data Quality

Predictably Poor Data Quality

Satisficing Data Quality

Expectation and Data Quality

/

One of my favorite recently read books is You Are Not So Smart by David McRaney.  Earlier this week, the book’s chapter about expectation was excerpted as an online article on Why We Can’t Tell Good Wine From Bad, which also provided additional examples about how we can be fooled by altering our expectations.

“In one Dutch study,” McRaney explained, “participants were put in a room with posters proclaiming the awesomeness of high-definition, and were told they would be watching a new high-definition program.  Afterward, the subjects said they found the sharper, more colorful television to be a superior experience to standard programming.”

No surprise there, right?  After all, a high-definition television is expected to produce a high-quality image.

“What they didn’t know,” McRaney continued, “was they were actually watching a standard-definition image.  The expectation of seeing a better quality image led them to believe they had.  Recent research shows about 18 percent of people who own high-definition televisions are still watching standard-definition programming on the set, but think they are getting a better picture.”

I couldn’t help but wonder if establishing an expectation of delivering high-quality data could lead business users to believe that, for example, the data quality of the data warehouse met or exceeded their expectations.  Could business users actually be fooled by altering their expectations about data quality?  Wouldn’t their experience of using the data eventually reveal the truth?

Retailers expertly manipulate us with presentation, price, good marketing, and great service in order to create an expectation of quality in the things we buy.  “The actual experience is less important,” McRaney explained.  “As long as it isn’t total crap, your experience will match up with your expectations.  The build up to an experience can completely change how you interpret the information reaching your brain from your otherwise objective senses.  In psychology, true objectivity is pretty much considered to be impossible.  Memories, emotions, conditioning, and all sorts of other mental flotsam taint every new experience you gain.  In addition to all this, your expectations powerfully influence the final vote in your head over what you believe to be reality.”

“Your expectations are the horse,” McRaney concluded, “and your experience is the cart.”  You might think it should be the other way around, but when your expectations determine your direction, you shouldn’t be surprised by the journey you experience.

If you find it difficult to imagine a positive expectation causing people to overlook poor quality in their experience with data, how about the opposite?  I have seen the first impression of a data warehouse initially affected by poor data quality create a negative expectation causing people to overlook the improved data quality in their subsequent experiences with the data warehouse.  Once people expect to experience poor data quality when using it, people stop trusting, and stop using, the data warehouse.

Data warehousing is only one example of how expectation can affect the data quality experience.  How are your organization’s expectations affecting its experiences with data quality?

On Philosophy, Science, and Data

/

Ever since Melinda Thielbar helped me demystify data science on OCDQ Radio, I have been pondering my paraphrasing of an old idea: Science without philosophy is blind; Philosophy without science is empty; Data needs both science and philosophy.

“A philosopher’s job is to find out things about the world by thinking rather than observing,” the philosopher Bertrand Russell once said.  One could say a scientist’s job is to find out things about the world by observing and experimenting.  In fact, Russell observed that “the most essential characteristic of scientific technique is that it proceeds from experiment, not from tradition.”

Russell also said that “science is what we know, and philosophy is what we don’t know.”  However, Stuart Firestein, in his book Ignorance: How It Drives Science, explained “there is no surer way to screw up an experiment than to be certain of its outcome.”

Although it seems it would make more sense for science to be driven by what we know, by facts, “working scientists,” according to Firestein, “don’t get bogged down in the factual swamp because they don’t care that much for facts.  It’s not that they discount or ignore them, but rather that they don’t see them as an end in themselves.  They don’t stop at the facts; they begin there, right beyond the facts, where the facts run out.  Facts are selected for the questions they create, for the ignorance they point to.”

In this sense, philosophy and science work together to help us think about and experiment with what we do and don’t know.

Some might argue that while anyone can be a philosopher, being a scientist requires more rigorous training.  A commonly stated requirement in the era of big data is to hire data scientists, but this begs the question: Is data science only for data scientists?

“Clearly what we need,” Firestein explained, “is a crash course in citizen science—a way to humanize science so that it can be both appreciated and judged by an informed citizenry.  Aggregating facts is useless if you don’t have a context to interpret them.”

I would argue that clearly what organizations need is a crash course in data science—a way to humanize data science so that it can be both appreciated and judged by an informed business community.  Big data is useless if you don’t have a business context to interpret it.  Firestein also made great points about science not being exclusionary (i.e., not just for scientists).  Just as you can enjoy watching sports without being a professional athlete and you can appreciate music without being a professional musician, you can—and should—learn the basics of data science (especially statistics) without being a professional data scientist.

In order to truly deliver business value to organizations, data science can not be exclusionary.  This doesn’t mean you shouldn’t hire data scientists.  In many cases, you will need the expertise of professional data scientists.  However, you will not be able to direct them or interpret their findings without understanding the basics, what could be called the philosophy of data science.

Some might argue that philosophy only reigns in the absence of data, while science reigns in the analysis of data.  Although in the era of big data there seems to be fewer areas truly absent of data, a conceptual bridge still remains between analysis and insight, the crossing of which is itself a philosophical exercise.  So, an endless oscillation persists between science and philosophy, which is why science without philosophy is blind, and philosophy without science is empty.  Data needs both science and philosophy.

Doing Data Governance

/

OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, I discuss the practical aspects of doing data governance with John Ladley, the author of the excellent book Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program.  Our discussion includes understanding the difference and relationship between data governance and information management, the importance of establishing principles before creating policies, data stewardship, and three critical success factors for data governance.

John Ladley is a business technology thought leader with 30 years of experience in improving organizations through the successful implementation of information systems.  He is a recognized authority in the use and implementation of business intelligence and enterprise information management (EIM).

John Ladley is the author of Making EIM Work for Business, and frequently writes and speaks on a variety of technology and enterprise information management topics.  His information management experience is balanced between strategic technology planning, project management, and, most important, the practical application of technology to business problems.

Popular OCDQ Radio Episodes

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

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • 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.
  • Data Profiling Early and Often — Guest James Standen discusses data profiling concepts and practices, and how bad data is often misunderstood and can be coaxed away from the dark side if you know how to approach it.
  • 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.

Data Governance needs Searchers, not Planners

/

In his book Everything Is Obvious: How Common Sense Fails Us, Duncan Watts explained that “plans fail, not because planners ignore common sense, but rather because they rely on their own common sense to reason about the behavior of people who are different from them.”

As development economist William Easterly explained, “A Planner thinks he already knows the answer; A Searcher admits he doesn’t know the answers in advance.  A Planner believes outsiders know enough to impose solutions; A Searcher believes only insiders have enough knowledge to find solutions, and that most solutions must be homegrown.”

I made a similar point in my post Data Governance and the Adjacent Possible.  Change management efforts are resisted when they impose new methods by emphasizing bad business and technical processes, as well as bad data-related employee behaviors, while ignoring unheralded processes and employees whose existing methods are preventing other problems from happening.

Demonstrating that some data governance policies reflect existing best practices reduces resistance to change by showing that the search for improvement was not limited to only searching for what is currently going wrong.

This is why data governance needs Searchers, not Planners.  A Planner thinks a framework provides all the answers; A Searcher knows a data governance framework is like a jigsaw puzzle.  A Planner believes outsiders (authorized by executive management) know enough to impose data governance solutions; A Searcher believes only insiders (united by collaboration) have enough knowledge to find the ingredients for data governance solutions, and a true commitment to change always comes from within.

 

Related Posts

The Hawthorne Effect, Helter Skelter, and Data Governance

Cooks, Chefs, and Data Governance

Data Governance Frameworks are like Jigsaw Puzzles

Data Governance and the Buttered Cat Paradox

Data Governance Star Wars: Bureaucracy versus Agility

Beware the Data Governance Ides of March

Aristotle, Data Governance, and Lead Rulers

Data Governance and the Adjacent Possible

The Three Most Important Letters in Data Governance

The Data Governance Oratorio

An Unsettling Truth about Data Governance

The Godfather of Data Governance

Over the Data Governance Rainbow

Getting Your Data Governance Stuff Together

Datenvergnügen

Council Data Governance

A Tale of Two G’s

Declaration of Data Governance

The Role Of Data Quality Monitoring In Data Governance

The Collaborative Culture of Data Governance

Open MIKE Podcast — Episode 12

/

Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management, which provides a comprehensive methodology that can be applied across a number of different projects within the Information Management space.  For more information, click on this link: openmethodology.org/wiki/What_is_MIKE2.0

The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

 

Episode 12: Information Development Book

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

 

MIKE2.0 Content Featured in or Related to this Podcast

Information Development Book: openmethodology.org/wiki/Information_Development_Book

Information Development: openmethodology.org/wiki/Information_Development

 

Previous Episodes of the Open MIKE Podcast

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

Episode 01: Information Management Principles

Episode 02: Information Governance and Distributing Power

Episode 03: Data Quality Improvement and Data Investigation

Episode 04: Metadata Management

Episode 05: Defining Big Data

Episode 06: Getting to Know NoSQL

Episode 07: Guiding Principles for Open Semantic Enterprise

Episode 08: Information Lifecycle Management

Episode 09: Enterprise Data Management Strategy

Episode 10: Information Maturity QuickScan

Episode 11: Information Maturity Model

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE