Commendable Comments (Part 10)

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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