Commendable Comments (Part 10)

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Welcome to the 300th Obsessive-Compulsive Data Quality (OCDQ) blog post!

You might have been expecting a blog post inspired by the movie 300, but since I already did that with Spartan Data Quality, instead I decided to commemorate this milestone with the 10th entry in my ongoing series for expressing my gratitude to my readers for their truly commendable comments on my blog posts.

 

Commendable Comments

On DQ-BE: Single Version of the Time, Vish Agashe commented:

“This has been one of my pet peeves for a long time. Shared version of truth or the reference version of truth is so much better, friendly and non-dictative (if such a word exists) than single version of truth.

I truly believe that starting a discussion with Single Version of the Truth with business stakeholders is a nonstarter. There will always be a need for multifaceted view and possibly multiple aspects of the truth.

A very common term/example I have come across is the usage of the term revenue. Unfortunately, there is no single version of revenue across the organizations (and for valid reasons). From Sales Management prospective, they like to look at sales revenue (sales bookings) which is the business on which they are compensated on, financial folks want to look at financial revenue, which is the revenue they capture in the books and marketing possibly wants to look at marketing revenue (sales revenue before the discount) which is the revenue marketing uses to justify their budgets. So if you ever asked questions to a group of people about what revenue of the organization is, you will get three different perspectives. And these three answers will be accurate in the context of three different groups.”

On Data Confabulation in Business Intelligence, Henrik Liliendahl Sørensen commented:

“I think this is going to dominate the data management realm in the coming years. We are not only met with drastically increasing volumes of data, but also increasing velocity and variety of data.

The dilemma is between making good decisions and making fast decisions, whether the decisions based on business intelligence findings should wait for assuring the quality of the data upon which the decisions are made, thus risking the decision being too late. If data quality always could be optimal by being solved at the root we wouldn’t have that dilemma.

The challenge is if we are able to have optimal data all the time when dealing with extreme data, which is data of great variety moving in high velocity and coming in huge volumes.”

On The People Platform, Mark Allen commented:

“I definitely agree and think you are burrowing into the real core of what makes or breaks EDM and MDM type initiatives -- it's the people.

Business models, processes, data, and technology all provide fixed forms of enablement or constraint. And where in the past these dynamics have been very compartmentalized throughout a company's business model and systems architecture, with EDM and MDM involving more integrated functions and shared data, people become more of the x-factor in the equation. This demands the presence of data governance to be the facilitating process that drives the collaborative, cross-functional, and decision making dynamics needed for successful EDM and MDM. Of course, the dilemma is that in a governance model people can still make bad decisions that inhibit people from working effectively.

So in terms of the people platform and data governance, there needs to be the correct focus on what are the right roles and good decisions made that can enable people to interact effectively.”

On Beware the Data Governance Ides of March, Jill Wanless commented:

“Our organization has taken the Hybrid Approach (starting Bottom-Up) and it works well for two reasons: (1) the worker bee rock stars are all aligned and ready to hit the ground running, and (2) the ‘Top’ can sit back and let the ‘aligned’ worker bees get on with it.

Of course, this approach is sometimes (painfully) slow, but with the ground-level rock stars already aligned, there is less resistance implementing the policies, and the Top’s heavy hand is needed much less frequently, but I voted for Hybrid Approach (starting Top-Down) because I have less than stellar patience for the long and scenic route.”

On Data Governance and the Buttered Cat Paradox, Rob Drysdale commented:

“Too many companies get paralyzed thinking about how to do this and implement it. (Along with the overwhelmed feeling that it is too much time/effort/money to fix it.) But I think your poll needs another option to vote on, specifically: ‘Whatever works for the company/culture/organization’ since not all solutions will work for every organization.

In some where it is highly structured, rigid and controlled, there wouldn’t be the freedom at the grass-roots level to start something like this and it might be frowned upon by upper-level management. In other organizations that foster grass-roots things then it could work.

However, no matter which way you can get it started and working, you need to have buy-in and commitment at all levels to keep it going and make it effective.”

On The Data Quality Wager, Gordon Hamilton commented:

“Deming puts a lot of energy into his arguments in 'Out of the Crisis' that the short-term mindset of the executives, and by extension the directors, is a large part of the problem.

Jackanapes, a lovely under-used term, might be a bit strong when the executives are really just doing what they are paid for. In North America we get what the directors measure! In fact, one quandary is that a proactive executive, who invests in data quality is building the long-term value of their company but is also setting it up to be acquired by somebody who recognizes that the 'under the radar' improvements are making the prize valuable.

Deming says on p.100: 'Fear of unfriendly takeover may be the single most important obstacle to constancy of purpose. There is also, besides the unfriendly takeover, the equally devastating leveraged buyout. Either way, the conqueror demands dividends, with vicious consequences on the vanquished.'”

On Got Data Quality?, Graham Rhind commented:

“It always makes me smile when people attempt to put a percentage value on their data quality as though it were something as tangible and measurable as the fat content of your milk.

In order to make such a measurement one would need to know where 100% of the defects lie. If they knew that they would be able to resolve the defects and achieve 100% quality. In reality you cannot and do not know where each defect is and how many there are.

Even though tools such as profilers will tell you, for example, that 95% of your US address records have a valid state added, there is still no way to measure how many of these valid states are applicable to the real world entity on the ground. Mr Smith may be registered in the database to an existing and valid address in the database, but if he moved last week there's a data quality issue that won't be discovered until one attempts to contact him.

The same applies when people say they have removed 95% of duplicates from their data. If they can measure it then they know where the other 5% of duplicates are and they can remove them.

But back to the point: you may not achieve 100% quality. In fact, we know you never will. But aiming for that target means that you're aiming in the right direction. As long as your goal is to get close to perfection and not to achieve it, I don't see the problem.”

On Data Governance Star Wars: Balancing Bureaucracy and Agility, Rob “Darth” Karel commented:

“A curious question to my Rebellious friend OCDQ-Wan, while data governance agility is a wonderful goal, and maybe a great place to start your efforts, is it sustainable?

Your agile Rebellion is like any start-up: decisions must be made quickly, you must do a lot with limited resources, everyone plays multiple roles willingly, and your objective is very targeted and specific. For example, to fire a photon torpedo into a small thermal exhaust port - only 2 meters wide - connected directly to the main reactor of the Death Star. Let's say you 'win' that market objective. What next?

The Rebellion defeats the Galactic Empire, leaving a market leadership vacuum. The Rebellion begins to set up a new form of government to serve all (aka grow existing market and expand into new markets) and must grow larger, with more layers of management, in order to scale. (aka enterprise data governance supporting all LOBs, geographies, and business functions).

At some point this Rebellion becomes a new Bureaucracy - maybe with a different name and legacy, but with similar results. Don't forget, the Galactic Empire started as a mini-rebellion itself spearheaded by the agile Palpatine!” 

You Are Awesome

Thank you very much for sharing your perspectives with our collablogaunity.  This entry in the series highlighted the commendable comments received on OCDQ Blog posts published between January and June of 2011.

Since there have been so many commendable comments, please don’t be offended if one of your comments wasn’t featured.

Please keep on commenting and stay tuned for future entries in the series.

By the way, even if you have never posted a comment on my blog, you are still awesome — feel free to tell everyone I said so.

Thank you for reading the Obsessive-Compulsive Data Quality (OCDQ) blog.  Your readership is deeply appreciated.

 

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730 Days and 264 Blog Posts Later – The Second Blogiversary of OCDQ Blog

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Commendable Comments (Part 9)

Commendable Comments (Part 8)

Commendable Comments (Part 7)

Commendable Comments (Part 6)

Commendable Comments (Part 5) – The 100th OCDQ Blog Post

Commendable Comments (Part 4)

Commendable Comments (Part 3)

Commendable Comments (Part 2)

Commendable Comments (Part 1)

Social Media Strategy

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

Effectively using social media within a business context is more art than science, which is why properly planning and executing a social media strategy is essential for organizations as well as individual professionals.

On this episode, I discuss social media strategy and content marketing with Crysta Anderson, a Social Media Strategist for IBM, who manages IBM InfoSphere’s social media presence, including the Mastering Data Management blog, the @IBMInitiate and @IBM_InfoSphere Twitter accounts, LinkedIn and other platforms.

Crysta Anderson also serves as a social media subject matter expert for IBM’s Information Management division.

Under Crysta’s execution, IBM Initiate has received numerous social media awards, including “Best Corporate Blog” from the Chicago Business Marketing Association, Marketing Sherpa’s 2010 Viral and Social Marketing Hall of Fame, and BtoB Magazine’s list of “Most Successful Online Social Networking Initiatives.”

Crysta graduated from the University of Chicago with a BA in Political Science and is currently pursuing a Master’s in Integrated Marketing Communications at Northwestern University’s Medill School.  Learn more about Crysta Anderson on LinkedIn.

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.
  • 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 Stakeholder’s Dilemma

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Game theory models a strategic situation as a game in which an individual player’s success depends on the choices made by the other players involved in the game.  One excellent example is the game known as The Prisoner’s Dilemma, which is deliberately designed to demonstrate why two people might not cooperate—even if it is in both of their best interests to do so.

Here is the classic scenario.  Two criminal suspects are arrested, but the police have insufficient evidence for a conviction.  So they separate the prisoners and offer each the same deal.  If one testifies for the prosecution against the other (i.e., defects) and the other remains silent (i.e., cooperates), the defector goes free and the silent accomplice receives the full one-year sentence.  If both remain silent, both prisoners are sentenced to only one month in jail for a minor charge.  If each betrays the other, each receives a three-month sentence.  Each prisoner must choose to betray the other or to remain silent.

If you have ever regularly watched a police procedural television series, such as Law & Order, then you have seen many dramatizations of the prisoner’s dilemma, including several sample outcomes of when the prisoners make different choices.

The Iterated Prisoner’s Dilemma

In iterated versions of the prisoner’s dilemma, players remember the previous actions of their opponent and change their strategy accordingly.  In many fields of study, these variations are considered fundamental to understanding cooperation and trust.

Here is an economics scenario with two players and a banker.  Each player holds a set of two cards, one printed with the word Cooperate (as in, with each other), the other printed with the word Defect.  Each player puts one card face-down in front of the banker.  By laying them face down, the possibility of a player knowing the other player’s selection in advance is eliminated.  At the end of each turn, the banker turns over both cards and gives out the payments, which can vary, but one example is as follows.

If both players cooperate, they are each awarded $5.  If both players defect, they are each penalized $1.  But if one player defects while the other player cooperates, the defector is awarded $10, while the cooperator neither wins nor loses any money.

Therefore, the safest play is to always cooperate, since you would never lose any money—and if your opponent always cooperates, then you can both win on every turn.  However, although defecting creates the possibility of losing a small amount of money, it also creates the possibility of winning twice as much money.

It is the iterated nature of this version of the prisoner’s dilemma that makes it so interesting for those studying human behavior.

For example, if you were playing against me, and I defected on the first two turns while you cooperated, I would have won $20 while you would have won nothing.  So what would you do on the third turn?  Let’s say that you choose to defect.

But if I defected yet again, although we would both lose $1, overall I would still be +$19 while you would be -$1.  And what if I continued defecting?  This would actually be an understandable strategy for me—if I was only playing for money, since you would have to defect 19 more times in a row before I broke even, but by which time you would have also lost $20.  And if instead, you start cooperating again in order to stop your losses, I could win a lot of money—at the expense of losing your trust.

Although the iterated prisoner’s dilemma is designed so that, over the long-term, cooperating players generally do better than non-cooperating players, in the short-term, the best result for an individual player is to defect while their opponent cooperates.

The Stakeholder’s Dilemma

Organizations embarking on an enterprise-wide initiative, such as data quality, master data management, and data governance, play a version of the iterated prisoner’s dilemma, which I refer to as The Stakeholder’s Dilemma.

These initiatives often bring together key stakeholders from all around the organization, representing each business unit or business function, and perhaps stakeholders representing data and technology as well.  These stakeholders usually form a committee or council, which is responsible for certain top-down aspects of the initiative, such as funding and strategic planning.

Of course, it is unrealistic to expect every stakeholder to cooperate equally at all times.  The realities of the fiscal calendar effect, conflicting interests, and changing business priorities, will mean that during any particular turn in the game (i.e., the current phase of the initiative), the amount of resources (money, time, people) allocated to the effort by a particular stakeholder will vary.

There will be times when sacrifices for the long-term greater good of the initiative will require that cooperating stakeholders either contribute more resources during the current phase, or receive fewer benefits from its deliverables, than defecting stakeholders.

As with the iterated prisoner’s dilemma, the challenge is what happens during the next turn (i.e., the next phase of the initiative).

If the same stakeholders repeatedly defect, then will the other stakeholders continue to cooperate?  Or will the spirit of trust, cooperation, and collaboration necessary for the continuing success of the ongoing initiative be irreparably damaged?

There are many, and often complex, reasons for why enterprise-wide initiatives fail, but failing to play the stakeholder’s dilemma well is one very common reason—and it is also a reason why many future enterprise-wide initiatives will fail to garner support.

How well does your organization play The Stakeholder’s Dilemma?

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Data Profiling Early and Often

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

On this episode of OCDQ Radio, I discuss data profiling with James Standen, the founder and CEO of nModal Solutions Inc., the makers of Datamartist, which is a fast, easy to use, visual data profiling and transformation tool.

Before founding nModal, James had over 15 years experience in a broad range of roles involving data, ranging from building business intelligence solutions, creating data warehouses and a data warehouse competency center, through to working on data migration and ERP projects in large organizations.  You can learn more about and connect with James Standen on LinkedIn.

James thinks that while there is obviously good data and bad data, that often bad data is just misunderstood and can be coaxed away from the dark side if you know how to approach it.  He does recommend wearing the proper safety equipment however, and having the right tools.  For more of his wit and wisdom, follow Datamartist on Twitter, and read the Datamartist Blog.

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.
  • 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 and Information Quality 2011

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Last week, I attended the Data Governance and Information Quality 2011 Conference, which was held June 27-30 in San Diego, California at the Catamaran Resort Hotel and Spa.

In this blog post, I summarize a few of the key points from some of the sessions I attended.  I used Twitter to help me collect my notes, and you can access the complete archive of my conference tweets on Twapper Keeper.

 

Assessing Data Quality Maturity

In his pre-conference tutorial, David Loshin, author of the book The Practitioner’s Guide to Data Quality Improvement, described five stages comprising a continuous cycle of data quality improvement:

  1. Identify and measure how poor data quality impedes business objectives
  2. Define business-related data quality rules and performance targets
  3. Design data quality improvement processes that remediate business process flaws
  4. Implement data quality improvement methods
  5. Monitor data quality against targets

 

Getting Started with Data Governance

Oliver Claude from Informatica provided some tips for making data governance a reality:

  • Data Governance requires acknowledging People, Process, and Technology are interlinked
  • You need to embed your data governance policies into your operational business processes
  • Data Governance must be Business-Centric, Technology-Enabled, and Business/IT Aligned

 

Data Profiling: An Information Quality Fundamental

Danette McGilvray, author of the book Executing Data Quality Projects, shared some of her data quality insights:

  • Although the right technology is essential, data quality is more than just technology
  • Believing tools cause good data quality is like believing X-Ray machines cause good health
  • Data Profiling is like CSI — Investigating the Poor Data Quality Crime Scene

 

Building Data Governance and Instilling Data Quality

In the opening keynote address, Dan Hartley of ConAgra Foods shared his data governance and data quality experiences:

  • It is important to realize that data governance is a journey, not a destination
  • One of the commonly overlooked costs of data governance is the cost of inaction
  • Data governance must follow a business-aligned and business-value-driven approach
  • Data governance is as much about change management as it is anything else
  • Data governance controls must be carefully balanced so they don’t disrupt business processes
  • Common Data Governance Challenge: Balancing Data Quality and Speed (i.e., Business Agility)
  • Common Data Governance Challenge: Picking up Fumbles — Balls dropped between vertical organizational silos
  • Bad business processes cause poor data quality
  • Better Data Quality = A Better Bottom Line
  • One of the most important aspects of Data Governance and Data Quality — Wave the Flag of Success

 

Practical Data Governance

Winston Chen from Kalido discussed some aspects of delivering tangible value with data governance:

  • Data governance is the business process of defining, implementing, and enforcing data policies
  • Every business process can be improved by feeding it better data
  • Data Governance is the Horse, not the Cart, i.e., Data Governance drives MDM and Data Quality
  • Data Governance needs to balance Data Silos (Local Authority) and Data Cathedrals (Central Control)

 

The Future of Data Governance and Data Quality

The closing keynote panel, moderated by Danette McGilvray, included the following insights:

  • David Plotkin: “It is not about Data, Process, or Technology — It is about People”
  • John Talburt: “For every byte of Data, we need 1,000 bytes of Metadata to go along with it”
  • C. Lwanga Yonke: “One of the most essential skills is the ability to lead change”
  • John Talburt: “We need to be focused on business-value-based data governance and data quality”
  • C. Lwanga Yonke: “We must be multilingual: Speak Data/Information, Business, and Technology”

 

Organizing for Data Quality

In his post-conference tutorial, Tom Redman, author of the book Data Driven, described ten habits of those with the best data:

  1. Focus on the most important needs of the most important customers
  2. Apply relentless attention to process
  3. Manage all critical sources of data, including external suppliers
  4. Measure data quality at the source and in business terms
  5. Employ controls at all levels to halt simple errors and establish a basis for moving forward
  6. Develop a knack for continuous improvement
  7. Set and achieve aggressive targets for improvement
  8. Formalize management accountabilities for data
  9. Lead the effort using a broad, senior group
  10. Recognize that the hard data quality issues are soft and actively manage the needed cultural changes

 

Tweeps Out at the Ball Game

As I mentioned earlier, I used Twitter to help me collect my notes, and you can access the complete archive of my conference tweets on Twapper Keeper.

But I wasn’t the only data governance and data quality tweep at the conference.  Steve Sarsfield, April Reeve, and Joe Dos Santos were also attending and tweeting.

However, on Tuesday night, we decided to take a timeout from tweeting, and instead became Tweeps out at the Ball Game by attending the San Diego Padres and Kansas Royals baseball game at PETCO Park.

We sang Take Me Out to the Ball Game, bought some peanuts and Cracker Jack, and root, root, rooted for the home team, which apparently worked since Padres closer Heath Bell got one, two, three strikes, you’re out on Royals third baseman Wilson Betemit, and the San Diego Padres won the game by a final score of 4-2.

So just like at the Data Governance and Information Quality 2011 Conference, a good time was had by all.  See you next year!

 

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Data Governance Star Wars

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

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Shown above is the poll results from the recent Star Wars themed blog debate about one of data governance’s biggest challenges, how to balance bureaucracy and business agility.  Rob Karel took the position for Bureaucracy as Darth Karel of the Empire, and I took the position for Agility as OCDQ-Wan Harris of the Rebellion.

However, this was a true debate format where Rob and I intentionally argued polar opposite positions with full knowledge that the reality is data governance success requires effectively balancing bureaucracy and business agility.

Just in case you missed the blog debate, here are the post links:

On this special, extended, and Star Wars themed episode of OCDQ Radio, I am joined by Rob Karel and Gwen Thomas to discuss this common challenge of effectively balancing bureaucracy and business agility on data governance programs.

Rob Karel is a Principal Analyst at Forrester Research, where he serves Business Process and Applications Professionals.  Rob is a leading expert in how companies manage data and integrate information across the enterprise.  His current research focus includes process data management, master data management, data quality management, metadata management, data governance, and data integration technologies.  Rob has more than 19 years of data management experience, working in both business and IT roles to develop solutions that provide better quality, confidence in, and usability of critical enterprise data.

Gwen Thomas is the Founder and President of The Data Governance Institute, a vendor-neutral, mission-based organization with three arms: publishing free frameworks and guidance, supporting communities of practitioners, and offering training and consulting.  Gwen also writes the popular blog Data Governance Matters, frequently contributes to IT and business publications, and is the author of the book Alpha Males and Data Disasters: The Case for Data Governance.

This extended episode of OCDQ Radio is 49 minutes long, and is divided into two parts, which are separated by a brief Star Wars themed intermission.  In Part 1, Rob and I discuss our blog debate.  In Part 2, Gwen joins us to provide her excellent insights.

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).
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

Stuck in the Middle with Data Governance

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Perhaps the most common debate about data governance is whether it should be started from the top down or the bottom up.

Data governance requires the coordination of a complex combination of a myriad of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data quality remediation, data stewardship, business process optimization, technology, policy enforcement—and obviously many other factors as well.

This common debate is understandable since some of these data governance success factors are mostly top-down (e.g., funding), and some of these data governance success factors are mostly bottom-up (e.g., data quality remediation and data stewardship).

However, the complexity that stymies many organizations is most data governance success factors are somewhere in the middle.

 

Stuck in the Middle with Data Governance

At certain times during the evolution of a data governance program, top-down aspects will be emphasized, and at other times, bottom-up aspects will be emphasized.  So whether you start from the top down or the bottom up, eventually you are going to need to blend together top-down and bottom-up aspects in order to sustain an ongoing and pervasive data governance program.

To paraphrase The Beatles, when you get to the bottom, you go back to the top, where you stop and turn, and you go for a ride until you get to the bottom—and then you do it again.  (But hopefully your program doesn’t get code-named: “Helter Skelter”)

But after some initial progress has been made, to paraphrase Stealers Wheel, people within the organization may start to feel like we have top-down to the left of us, bottom-up to the right to us, and here we are—stuck in the middle with data governance.

In other words, although data governance is never a direct current only flowing in one top-down or bottom-up direction, but instead continually flows in an alternating current between top-down and bottom-up, when this dynamic is not communicated to everyone throughout the organization, progress is disrupted by people waiting around for someone else to complete the circuit.

But when, paraphrasing Pearl Jam, data governance is taken up by the middle—then there ain’t gonna be any middle any more.

In other words, when data governance pervades every level of the organization, everyone stops thinking in terms of top-down and bottom-up, and acts like an enterprise in the midst of sustaining the momentum of a successful data governance program.

 

Data Governance Conference

DGIQ Event Button

Next week, I will be attending the Data Governance and Information Quality Conference, which will be held June 27-30 in San Diego, California at the Catamaran Resort Hotel and Spa.

If you will also be attending, and you want to schedule a meeting with me: Contact me via email

If you will not be attending, you can follow the conference tweets using the hashtag: #DGIQ2011

 

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Master Data Management in Practice

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

Master Data Management in Practice: Achieving True Customer MDM is a great new book by Dalton Cervo and Mark Allen, which demystifies the theories and industry buzz surrounding Master Data Management (MDM), and provides a practical guide for successfully implementing a Customer MDM program.

The book discusses the three major types of MDM (Analytical, Operational, and Enterprise), explaining exactly how MDM is related to, and supported by, data governance, data stewardship, and data quality.  Dalton and Mark explain how MDM does much more than just bring data together—it provides a set of processes, services, and policies that bring people together in a cross-functional and collaborative approach to enterprise data management.

Dalton Cervo has over 20 years experience in software development, project management, and data management, including architectural design and implementation of analytical MDM, and management of a data quality program for an enterprise MDM implementation.  Dalton is a senior solutions consultant at DataFlux, helping organizations in the areas of data governance, data quality, data integration, and MDM.  Read Dalton’s blog, follow Dalton on Twitter, and connect with Dalton on LinkedIn.

Mark Allen has over 20 years of data management and project management experience including extensive planning and deployment experience with customer master data initiatives, data governance programs, and leading data quality management practices.  Mark is a senior consultant and enterprise data governance lead at WellPoint, Inc.  Prior to WellPoint, Mark was a senior program manager in customer operations groups at Sun Microsystems and Oracle, where Mark served as the lead data steward for the customer data domain throughout the planning and implementation of an enterprise customer data hub.

On this episode of OCDQ Radio, I am joined by the authors to discuss how to properly prepare for a new MDM program.

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.
  • 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-View: Baseball and Data Quality

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Data Quality (DQ) View is an OCDQ regular segment.  Each DQ-View is a brief video discussion of a data quality key concept.

 

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: DQ-View on Vimeo

 

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Data Quality is not a Magic Trick

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Video: Oh, the Data You’ll Show!

The Art of Data Matching

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

On this episode of OCDQ Radio, I am joined by Henrik Liliendahl Sørensen for a discussion about the Art of Data Matching.

Henrik is a data quality and master data management (MDM) professional also doing data architecture.  Henrik has worked 30 years in the IT business within a large range of business areas, such as government, insurance, manufacturing, membership, healthcare, public transportation, and more.

Henrik’s current engagements include working as practice manager at Omikron Data Quality, a data quality tool maker with headquarters in Germany, and as data quality specialist at Stibo Systems, a master data management vendor with headquarters in Denmark.  Henrik is also a charter member of the IAIDQ, and the creator of the LinkedIn Group for Data Matching for people interested in data quality and thrilled by automated data matching, deduplication, and identity resolution.

Henrik is one of the most prolific and popular data quality bloggers, regularly sharing his excellent insights about data quality, data matching, MDM, data architecture, data governance, diversity in data quality, and many other data management topics.

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.
  • 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 Star Wars: Balancing Bureaucracy and Agility

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I was recently discussing data governance best practices with Rob Karel, the well respected analyst at Forrester Research, and our conversation migrated to one of data governance’s biggest challenges — how to balance bureaucracy and business agility.

So Rob and I thought it would be fun to tackle this dilemma in a Star Wars themed debate across our individual blog platforms with Rob taking the position for Bureaucracy as the Empire and me taking the opposing position for Agility as the Rebellion.

(Yes, the cliché is true, conversations between self-proclaimed data geeks tend to result in Star Wars or Star Trek parallels.)

Disclaimer: Remember that this is a true debate format where Rob and I are intentionally arguing polar opposite positions with full knowledge that the reality is data governance success requires effectively balancing bureaucracy and agility.

Please take the time to read both of our blog posts, then we encourage your comments — and your votes (see the poll below).

Data Governance Star Wars

If you are having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Data Governance Star Wars

The Force is Too Strong with This One

“Don’t give in to Bureaucracy—that is the path to the Dark Side of Data Governance.”

Data governance requires the coordination of a complex combination of a myriad of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data quality remediation, data stewardship, business process optimization, technology enablement, and, perhaps most notably, policy enforcement.

When confronted by this phantom menace of complexity, many organizations believe that the only path to success must be command and control—institute a rigid bureaucracy to dictate policies, demand compliance, and dole out punishments.  This approach to data governance often makes policy compliance feel like imperial rule, and policy enforcement feel like martial law.

But beware.  Bureaucracy, command, control—the Dark Side of Data Governance are they.  Once you start down the dark path, forever will it dominate your destiny, consume your organization it will.

No Time to Discuss this as a Committee

“There is a great disturbance in the Data, as if millions of voices suddenly cried out for Governance but were suddenly silenced.  I fear something terrible has happened.  I fear another organization has started by creating a Data Governance Committee.”

Yes, it’s true—at some point, an official Data Governance Committee (or Council, or Board, or Galactic Senate) will be necessary.

However, one of the surest ways to guarantee the failure of a new data governance program is to start by creating a committee.  This is often done with the best of intentions, bringing together key stakeholders from all around the organization, representatives of each business unit and business function, as well as data and technology stakeholders.  But when you start by discussing data governance as a committee, you often never get data governance out of the committee (i.e., all talk, mostly arguing, no action).

Successful data governance programs often start with a small band of rebels (aka change agents) struggling to restore quality to some business-critical data, or struggling to resolve inefficiencies in a key business process.  Once news of their successful pilot project spreads, more change agents will rally to the cause—because that’s what data governance truly requires, not a committee, but a cause to believe in and fight for—especially after the Empire of Bureaucracy strikes back and tries to put down the rebellion.

Collaboration is the Data Governance Force

“Collaboration is what gives a data governance program its power.  Its energy binds us together.  Cooperative beings are we.  You must feel the Collaboration all around you, among the people, the data, the business process, the technology, everywhere.”

Many rightfully lament the misleading term “data governance” because it appears to put the emphasis on “governing data.”

Data governance actually governs the interactions among business processes, data, technology and, most important—people.  It is the organization’s people, empowered by high quality data and enabled by technology, who optimize business processes for superior corporate performance.  Data governance reveals how truly interconnected and interdependent the organization is, showing how everything that happens within the enterprise happens as a result of the interactions occurring among its people.

Data governance provides the framework for the communication and collaboration of business, data, and technical stakeholders, and establishes an enterprise-wide understanding of the roles and responsibilities involved, and the accountability required to support the organization’s business activities, and materialize the value of the enterprise’s data as positive business impacts.

Enforcing data governance policies with command and control is the quick and easy path—to failure.  Principles, not policies, are what truly give a data governance program its power.  Communication and collaboration are the two most powerful principles.

“May the Collaboration be with your Data Governance program.  Always.”

Always in Motion is the Future

“Be mindful of the future, but not at the expense of the moment.  Keep your concentration here and now, where it belongs.”

Perhaps the strongest case against bureaucracy in data governance is the business agility that is necessary for an organization to survive and thrive in today’s highly competitive and rapidly evolving marketplace.  The organization must follow what works for as long as it works, but without being afraid to adjust as necessary when circumstances inevitably change.

Change is the only galactic constant, which is why data governance policies can never be cast in stone (or frozen in carbonite).

Will a well-implemented data governance strategy continue to be successful?  Difficult to see.  Always in motion is the future.  And this is why, when it comes to deliberately designing a data governance program for agility: “Do or do not.  There is no try.”

Click here to read Rob “Darth” Karel’s blog post entry in this data governance debate

Please feel free to also post a comment below and explain your vote or simply share your opinions and experiences.

Listen to Data Governance Star Wars on OCDQ Radio — In Part 1, Rob Karel and I discuss our blog mock debate, which is followed by a brief Star Wars themed intermission, and then in Part 2, Gwen Thomas joins us to provide her excellent insights.

Data Quality Pro

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

On this episode, I am joined by special guest Dylan Jones, the community leader of Data Quality Pro, the largest membership resource dedicated entirely to the data quality profession.

Dylan is currently overseeing the re-build and re-launch of Data Quality Pro into a next generation membership platform, and during our podcast discussion, Dylan describes some of the great new features that will be coming soon to Data Quality Pro.

Links for Data Quality Pro and Dylan Jones:

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.
  • 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 IT Prime Directive of Business First Contact

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This blog post is sponsored by the Enterprise CIO Forum and HP.

Every enterprise requires, as Ralph Loura explains, “end to end business insight to generate competitive advantage, and it’s hard to gain insight if the business is arms length away from the data and the systems and the processes that support business insight.”

Loura explains that one of the historical challenges with technology has been that most IT systems have traditionally taken years to deploy and are supported on timelines and lifecycles that are inconsistent with the dynamic business needs of the organization, which has, in some cases, caused technology to become a business disabler instead of a business enabler.

The change-averse nature of most legacy applications is the antithesis of the agile nature of most modern applications.

“It wasn’t too long ago,” explains John Dodge, “when speed didn’t matter, or was considered an enemy of a carefully laid out IT strategy based largely on lowest cost.”  However, speed and agility are now “a competitive imperative.  You have to be fast in today’s marketplace and no department feels the heat more than IT, according to the Enterprise CIO Forum Council members.”

“If you think in terms of speed and the dynamic nature of business,” explains Joseph Spagnoletti, “clearly the organization couldn’t operate at that pace or make the necessary changes without IT woven very deeply into the work that the business does.”

Spagnoletti believes that cloud computing, mobility, and analytics are the three technology enablers for the timely delivery of the information that the organization requires to support its constantly evolving business needs.

“Embedding IT into an organization optimizes a business’s competitive edge,” explains Bill Laberis, “because it empowers the people right at the front lines of the enterprise to make better, faster and more informed decisions — right at the point of contact with customers, partners and clients.”

Historically, IT had a technology-first mindset.  However, the new IT prime directive must become business first contact, embedding advanced technology right at the point of contact with the organization’s business needs, enabling the enterprise to continue its mission to explore new business opportunities with the agility to boldly go where no competitor has gone before.

This blog post is sponsored by the Enterprise CIO Forum and HP.

 

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A Brave New Data World

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

Welcome to the highly anticipated debut episode of the Obsessive-Compulsive Data Quality (OCDQ) podcast—OCDQ Radio!

In this episode, I discuss how data, data quality, data-driven decision making, and metadata quality no longer reside exclusively within the esoteric realm of data management.  Data has now so thoroughly pervaded mainstream culture that we hardly seem to notice that we are quite literally swimming in data on a daily basis.

The growing challenge is can we extract meaningful insights from these vast and veritable oceans of unrelenting data volumes, and use those insights to make better decisions in near real-time in order to positively impact the various aspects of our lives.

We are now living in a brave new data world where everyone is a data geek—and data quality affects us all.

Or to paraphrase William Shakespeare:

“O wonder!

How many goodly data are there here!  How beauteous data geeks are! 

O brave new world! 

That is so dependent on the quality of the data in it!”

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.
  • 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.

A Sadie Hawkins Dance of Business Transformation

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This blog post is sponsored by the Enterprise CIO Forum and HP.

In the United States, a Sadie Hawkins Dance is a school-sponsored semi-formal dance, in which, contrary to the usual custom, female students invite male students.  In the world of information technology (IT), a Sadie Hawkins Dance is an enterprise-wide initiative, in which, contrary to the usual custom, a strategic business transformation is driven by IT.

Although IT-driven business transformation might seem like an oxymoron, the reality is a centralized IT department is one of the few organizational functions that regularly interacts with the entire enterprise.  Therefore, IT is strategically positioned to influence enterprise-wide business transformation—and CIOs might be able to take a business leadership role in those activities.

Wayne Shurts, the CIO of Supervalu, recently discussed how CIOs can make the transition to business leader by “approaching things from a business point of view, as opposed to a technology point of view.  IT must become intensely business driven.”

One thing Shurts emphasized is necessary for this shift in the perception of the CIO is that other C-level executives must realize “technology can be transformative for the organization, especially since it is transforming the consumer behavior of customers.”

 

Business Transformation through IT

David Steiner and Puneet Bhasin, the CEO and CIO of Waste Management, recently recorded a great two-part video interview called Business Transformation through IT, which you can check out using the following links: Part 1, Part 2, Transcript

“From day one,” explained Steiner, “I knew that the one way we could transform our company was through technology.”  Steiner then set out to find a CIO that could help him realize this vision of technology being transformative for the organization.

“If you’re going to be a true business partner,” explained Steiner, “which is what every CEO is looking for from their CIO, you have to go understand the business.”  Steiner explained that one of the first things that Bhasin did after he was hired as CIO was go out into the field and live the life of a customer service rep, a driver, a dispatcher, and a route manager—so that before Bhasin tried to do anything with technology, he first sought to understand the business so that he could become a true business partner.

“So the best advice I could give to any CIO would be,” concluded Steiner, “be a business partner, not a technologist.  Know the technology.  You’ve got to know how to apply the technology.  But be a business partner.”

“My advice to CEOs,” explained Bhasin, “would be look for a business person first and a technologist second.  And make sure that your CIO is a part of the decision-making strategic body within the organization.  If you are looking at IT purely as an area to reduce cost, that’s probably the wrong thing.  To me the value of IT is certainly in the area of efficiencies and cost reduction.  I think it has a huge role to play in that.  But I think it has an even greater role to play in product design, and growing customers, and expanding segments, and driving profitability.”

John Dodge recently blogged that business transformation is the CIO’s responsibility and opportunity.  Even though CIOs will eventually need their business partners to take the lead once they get out on the dance floor, CIOs may need to initiate things by inviting their business partners to A Sadie Hawkins Dance of Business Transformation.

This blog post is sponsored by the Enterprise CIO Forum and HP.

 

Related Posts

Are Applications the La Brea Tar Pits for Data?

Why does the sun never set on legacy applications?

The Partly Cloudy CIO

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