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.

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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.

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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!”

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The Costs and Profits of Poor Data Quality

Continuing the theme of my two previous posts, which discussed when it’s okay to call data quality as good as it needs to get and when perfect data quality is necessary, in this post I want to briefly discuss the costs — and profits — of poor data quality.

Loraine Lawson interviewed Ted Friedman of Gartner Research about How to Measure the Cost of Data Quality Problems, such as the costs associated with reduced productivity, redundancies, business processes breaking down because of data quality issues, regulatory compliance risks, and lost business opportunities.  David Loshin blogged about the challenge of estimating the cost of poor data quality, noting that many estimates, upon close examination, seem to rely exclusively on anecdotal evidence.

A recent Mental Floss article recounted 10 Very Costly Typos, including the 1962 $80 million dollar missing hyphen in the programming code that led to the destruction of the Mariner 1 spacecraft, the 2007 Roswell, New Mexico car dealership promotion where instead of 1 out of 50,000 scratch lottery tickets revealing a $1,000 cash grand prize, all of the tickets were printed as grand-prize winners, which would have been a $50 million payout, but $250,000 in Walmart gift certificates were given out instead, and, more recently, the March 2013 typographical error in the price of pay-per-ride cards on 160,000 maps and posters that cost New York City’s Transportation Authority approximately $500,000.

Although we often only think about the costs of poor data quality, the article also shared some 2010 research performed by Harvard University claiming that Google profits an estimated $497 million dollars a year from people mistyping the names of popular websites and landing on typosquatter sites, which just happen to be conveniently littered with Google ads.

Poor data quality has also long played an important role in improving Google Search, where misspellings of search terms entered by users (and not just a spellchecker program) is leveraged by the algorithm providing the Did you mean, Including results for, and Search instead for help text displayed at the top of the first page of Google Search results.

What examples (or calculation methods) can you provide about either the costs or profits associated with poor data quality?

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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?

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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?

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The Big Datastillery

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: The Big Datastillery on Vimeo

To view or download the infographic featured in the video, click on this direct link to its PDF: The Big Datastillery.pdf

This video was sponsored by 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 video are my own and don’t necessarily represent IBM’s positions, strategies, or opinions.

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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).

• Measuring Data Quality for Ongoing Improvement — Guest Laura Sebastian-Coleman discusses bringing together a better understanding of what is represented in data with the expectations for use in order to improve the overall quality of data.

• 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.”

• 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.

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.

• 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.

• The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.

• 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.

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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

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Smart Big Data Adoption for Midsize Businesses

In a previous post, I explained that big data is not just for big businesses.  During a recent interview Ed Abrams discussed how mobile, social, and cloud are driving big data adoption among midsize businesses.

As Sharon Hurley Hall recently blogged, midsize businesses are adopting social for the simple reason “a significant proportion of your potential customers are online, and while there they could be buying your product or service.”  She also makes a great point about social adoption not being only externally focused.  “Social business technologies will improve internal communication and knowledge-sharing.  The result is a better-informed and more engaged workforce, and the technology gives the ability to harness creativity and implement innovation to increase your competitive advantage.”

“Becoming more social,” Hall concluded, “doesn’t mean that employees waste time online; in fact, it means that everyone is better informed about both data and strategy, leading to business benefits.  The combination of integrating social technologies to improve your operational efficiency and harnessing social data to boost your knowledge base means that your business can be more competitive and more profitable.”

Hall’s insights also exemplify the proper perspective for midsize businesses to use when adopting big data.  No business of any size should adopt big data just because everyone is talking about it, nor simply because they think it might help their business.

As with everything in the business world, you should seek first to understand what big data adoption can offer, and what kind of investment it requires, before making any type of commitment.  The best thing about big data for midsize businesses is that it provides a big list of possibilities.  But trying to embrace all of the possibilities of big data would be a big mistake.  Start small with big data.  Smart big data adoption for midsize businesses means starting with well-defined, business-enhancing opportunities.

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.

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Demystifying Data Science

OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, special guest, and actual data scientist, Dr. Melinda Thielbar, a Ph.D. Statistician, and I attempt to demystify data science by explaining what a data scientist does, including the requisite skills involved, bridging the communication gap between data scientists and business leaders, delivering data products business users can use on their own, and providing a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, experimentation, and correlation.

Melinda Thielbar is the Senior Mathematician for IAVO Research and Scientific.  Her work there focuses on power system optimization using real-time prediction models.  She has worked as a software developer, an analytic lead for big data implementations, and a statistics and programming teacher.

Melinda Thielbar is a co-founder of Research Triangle Analysts, a professional group for analysts and data scientists located in the Research Triangle of North Carolina.

While Melinda Thielbar doesn’t specialize in a single field, she is particularly interested in power systems because, as she puts it, “A power systems optimizer has to work every time.”

Popular OCDQ Radio Episodes

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

• 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.

• Gaining a Competitive Advantage with Data — Guest William McKnight discusses some of the practical, hands-on guidance provided by his book Information Management: Strategies for Gaining a Competitive Advantage with Data.

• Doing Data Governance — Guest John Ladley discusses his book How to Design, Deploy and Sustain Data Governance and how to understand the difference and relationship between data governance and enterprise information management.

• 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).

• Measuring Data Quality for Ongoing Improvement — Guest Laura Sebastian-Coleman discusses bringing together a better understanding of what is represented in data with the expectations for use in order to improve the overall quality of data.

• 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.”

• 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.

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.

• 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.

• The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.

• 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 Hawthorne Effect, Helter Skelter, and Data Governance

In his book The Half-life of Facts: Why Everything We Know Has an Expiration Date, Samuel Arbesman introduced me to the Hawthorne Effect, which is “when subjects behave differently if they know they are being studied.  The effect was named after what happened in a factory called Hawthorne Works outside Chicago in the 1920s and 1930s.”

“Scientists wished to measure,” Arbesman explained, “the effects of environmental changes on the productivity of workers.  They discovered whatever they did to change the workers’ behaviors — whether they increased the lighting or altered any other aspect of the environment — resulted in increased productivity.  However, as soon as the study was completed, productivity dropped.  The researchers concluded that the observations themselves were affecting productivity and not the experimental changes.”

I couldn’t help but wonder how the Hawthorne Effect could affect a data governance program.  When data governance policies are first defined, and their associated procedures and processes are initially implemented, after a little while, and usually after a little resistance, productivity often increases and the organization begins to advance its data governance maturity level.

Perhaps during these early stages employees are well-aware that they’re being observed to make sure they’re complying with the new data governance policies, and this observation itself accounts for advancing to the next maturity level.  Especially since after progress stops being studied so closely, it’s not uncommon for an organization to backslide to an earlier maturity level.

You might be tempted to conclude that continuous monitoring, especially of the Orwellian Big Brother variety, might be able to prevent this from happening, but I doubt it.  Data governance maturity is often misperceived in the same way that expertise is misperceived — as a static state that once achieved signifies a comforting conclusion to all the grueling effort that was required, either to become an expert, or reach a particular data governance maturity level.

However, just like the five stages of data quality, oscillating between different levels of data governance maturity, and perhaps even occasionally coming full circle, may be an inevitable part of the ongoing evolution of a data governance program, which can often feel like a top-down/bottom-up amusement park ride of the Beatles “Helter Skelter” variety:

When you get to the bottom, you go back to the top, where you stop and you turn, and you go for a ride until you get to the bottom — and then you do it again.

Come On Tell Me Your Answers

Do you, don’t you . . . think the Hawthorne Effect affects data governance?

Do you, don’t you . . . think data governance is Helter Skelter?

Tell me, tell me, come on tell me your answers — by posting a comment below.