Data Quality and #FollowFriday the 13th

As Alice Hardy arrived at her desk at Crystal Lake Insurance, it seemed like a normal Friday morning.  Her thoughts about her weekend camping trip were interrupted by an eerie sound emanating from one of the adjacent cubicles:

Da da da, ta ta ta.  Da da da, ta ta ta.

“What’s that sound?” Alice wondered out loud.

“Sorry, am I typing too loud again?” responded Tommy Jarvis from another adjacent cubicle.  “Can you come take a look at something for me?”

“Sure, I’ll be right over,” Alice replied as she quickly circumnavigated their cluster of cubicles, puzzled and unsettled to find the other desks unoccupied with their computers turned off, wondering, to herself this time, where did that eerie sound come from?  Where are the other data counselors today?

“What’s up?” she casually asked upon entering Tommy’s cubicle, trying, as always, to conceal her discomfort about being alone in the office with the one colleague that always gave her the creeps.  Visiting his cubicle required a constant vigilance in order to avoid making prolonged eye contact, not only with Tommy Jarvis, but also with the horrifying hockey mask hanging above his computer screen like some possessed demon spawn from a horror movie.

“I’m analyzing the Date of Death in the life insurance database,” Tommy explained.  “And I’m receiving really strange results.  First of all, there are no NULLs, which indicates all of our policyholders are dead, right?  And if that wasn’t weird enough, there are only 12 unique values: January 13, 1978, February 13, 1981, March 13, 1987, April 13, 1990, May 13, 2011, June 13, 1997, July 13, 2001, August 13, 1971, September 13, 2002, October 13, 2006, November 13, 2009, and December 13, 1985.”

“That is strange,” said Alice.  “All of our policyholders can’t be dead.  And why is Date of Death always the 13th of the month?”

“It’s not just always the 13th of the month,” Tommy responded, almost cheerily.  “It’s always a Friday the 13th.”

“Well,” Alice slowly, and nervously, replied.  “I have a life insurance policy with Crystal Lake Insurance.  Pull up my policy.”

After a few, quick, loud pounding keystrokes, Tommy ominously read aloud the results now displaying on his computer screen, just below the hockey mask that Alice could swear was staring at her.  “Date of Death: May 13, 2011 . . . Wait, isn’t that today?”

Da da da, ta ta ta.  Da da da, ta ta ta.

“Did you hear that?” asked Alice.  “Hear what?” responded Tommy with a devilish grin.

“Never mind,” replied Alice quickly while trying to focus her attention on only the computer screen.  “Are you sure you pulled up the right policy?  I don’t recognize the name of the Primary Beneficiary . . . Who the hell is Jason Voorhees?”

“How the hell could you not know who Jason Voorhees is?” asked Tommy, with anger sharply crackling throughout his words.  “Jason Voorhees is now rightfully the sole beneficiary of every life insurance policy ever issued by Crystal Lake Insurance.”

Da da da, ta ta ta.  Da da da, ta ta ta.

“What?  That’s impossible!” Alice screamed.  “This has to be some kind of sick data quality joke.”

“It’s a data quality masterpiece!” Tommy retorted with rage.  “I just finished implementing my data machete, er I mean, my data matching solution.  From now on, Crystal Lake Insurance will never experience another data quality issue.”

“There’s just one last thing that I need to take care of.”

Da da da, ta ta ta.  Da da da, ta ta ta.

“And what’s that?” Alice asked, smiling nervously while quickly backing away into the hallway—and preparing to run for her life.

Da da da, ta ta ta.  Da da da, ta ta ta.

“Real-world alignment,” replied Tommy.  Rising to his feet, he put on the hockey mask, and pulled an actual machete out of the bottom drawer of his desk.  “Your Date of Death is entered as May 13, 2011.  Therefore, I must ensure real-world alignment.”

Da da da, ta ta ta.  Da da da, ta ta ta.  Da da da, ta ta ta.  Da da da, ta ta ta.  Data Quality.

The End.

(Note — You can also listen to the OCDQ Radio Theater production of this DQ-Tale in the Scary Calendar Effects episode.)

#FollowFriday Recommendations

#FollowFriday is when Twitter users recommend other users you should follow, so here are some great tweeps who provide tweets mostly about Data Quality, Data Governance, Master Data Management, Business Intelligence, and Big Data Analytics:

(Please Note: This is by no means a comprehensive list, is listed in no particular order whatsoever, and no offense is intended to any of my tweeps not listed below.  I hope that everyone has a great #FollowFriday and an even greater weekend.)

Henrik Liliendahl Sørensen – @hlsdk Dylan Jones – @DataQualityPro Prashanta Chandramohan – @MDMGeek John Owens – @JohnIMM Phil Simon – @PhilSimon Rich Murnane – @murnane Alan David Duncan – @Alan_D_Duncan Axel Troike – @AxelTroike Chris Hale – @HaleChris Nicola Askham – @Nicola_Askham Karen Way – @KarenAWay1 Melinda Thielbar – @mthielbar Carla Gentry – @data_nerd Jill Wanless – @sheezaredhead Julie Hunt – @juliebhunt Jacqueline Roberts – @JackieMRoberts Lyndsay Wise – @wiseanalytics Loraine Lawson – @LoraineLawson Corinna Martinez – @Futureratti Jen Stirrup – @JenStirrup Loretta Mahon Smith – @silverdata April Reeve – @Datagrrl Tamara Dull – @tamaradull Tracy Austin – @tracykowal Ina Felsheim – @InaSAP Marinka Voorhout – @marinkavoorhout Meta Brown – @metabrown312 Kelle O’Neal – @1stSanFrancisco Robin Stehlik – @RobinPStehlik Natalie Eschen – @NatalieEschen Cindy Balon Harder – @CindyBHarder Kristin McMahon – @Kristin_McMahon Carrie Byrum – @CByrum Marie Haggberg – @HaggbergConsult Terri Rylander – @BIMarcom Nicole Carriere – @carrni Beth Breidenbach – @bbreidenbach Giedre Aleknonyte – @googlea Sarah Burnett – @SarahBurnett Teresa Cottam – @Teresacottam Kelly Lautt – @KellyLautt Margaret van Engers – @EngersWijs Karen Lopez – @datachick Rob Drysdale – @projmgr William Sharp – @dqchronicle Augusto Albeghi – @Stray__Cat Ted Louie – @TedLouie Gordon Hamilton – @DQStudent Juan Carlos Ore – @juancore Richard Eudes, PhD – @RichardEudes Julian Schwarzenbach – @jschwa1 Ken O’Connor – @KenOConnorData Mark Horseman – @MarkHorseman Graham Rhind – @GrahamRhind Rich Northwood – @TheDataGeek Jarrett Goldfedder – @JGoldfed Dalton Cervo – @dcervo Phil Wright – @faropress Garry Ure – @dq_midnightblue Jaime Fitzgerald – @jaimefitzgerald Simon Daniels – @mktginsightguy Ajay Ohri – @decisionstats Marcus Borba – @marcusborba Alexej Freund – @alexej_freund Vish Agashe – @VishAgashe Jean-Michel Franco – @jmichel_franco Jorge García – @jgptec Rohin Bhargava – @RohinBhargava Krish Krishnan – @datagenius Sunil Soares – @SunilSoares1 Mady Korada – @MostlyKnown Craig Mullins – @craigmullins Chris Sorensen – @wjdataguy Ted Cuzzillo – @datadoodle Mark Lorion – @mark_lorion Steve Dine – @steve_dine Doug Newdick – @dougnewdick Richard Jarvis – @ManBagBlog David Pratt – @DataMgmtWonk Gary Allemann – @Gary_Allemann David Ho – @David_Ho_DQ Jay Zaidi – @JayZaidi Ralph Winters – @RDub2 Rob Paller – @RobPaller Stephen Putman – @SJPutman JJ Burnam – @AmbivalentGeek Garnie Bolling – @GarnieBolling Benjamin Wolfe – @BenjaminWolfe Nick Giuliano – @Nick_Giuliano Scott Delaney – @scottyd99999 FX Nicolas – @FxNicolas Kimmo Kontra – @kimmokontra Leif Tietje – @Leif74 Jeff Cutler – @JeffCutler Amanda Woolf – @AmandWoolf Ron Miller – @ron_miller

IAIDQ – @IAIDQ Daragh O Brien – @daraghobrien Thomas Redman – @thedatadoc1 David Loshin – @DavidLoshin Neil Raden – @NeilRaden James Taylor – @jamet123 John Ladley – @jladley Art Petty – @ArtPetty Paul Gillin – @pgillin Shawn Rogers – @shawnrog Wayne Eckerson – @weckerson Peter Thomas – @PeterJThomas Timo Elliott – @TimoElliott Dan Power – @dan_power William McKnight – @williammcknight Kirk Borne – @KirkDBorne Michael Cavaretta – @mjcavaretta Peter Aiken – @paiken Gwen Thomas – @gwenthomasdgi Claudia Imhoff – @Claudia_Imhoff Tom Davenport – @tdav Gartner Research – @Gartner_inc Ted Friedman – @ted_friedman Andy Bitterer – @bitterer Merv Adrian – @merv Doug Laney – @Doug_Laney Forrester Research – @forrester Michele Goetz – @FORR_Mgoetz IBM Midsize Business – @MidmarketIBM IBM Big Data – @IBMbigdata Crysta Anderson – @CrystaAnderson David Pittman – @TheSocialPitt James Kobielus – @jameskobielus SAS DataFlux – @SASDataFlux Jill Dyché – @JillDyche Data Roundtable – @DataRoundtable SAS Software – @SASsoftware SAS Analytics – @SASanalytics Mark Troester – @mtroester All Analytics – @AllAnalytics Service Objects – @ServiceObjects Wendy Breakstone – @socialwen Actian Corporation – @ActianCorp Actian DataCloud – @DataIntegrate Paige Roberts – @RobertsPaige Bill Hewitt – @BillHewittCEO Kalido – @Kalido Mike Wheeler – @MWheeler_DDG Informatica – @InformaticaCorp Robert Karel – @rbkarel Clarke Patterson – @ClarkePatterson Jakki Geiger – @JakkiGeiger Ravi Shankar – @Ravi_Shankar_ Talend – @Talend Steve Sarsfield – @SteveSarsfield Trillium Software – @TrilliumSW RedPoint Global – @RedPointGlobal George Corugedo – @GeorgeCorugedo Carol Wolicki – @cwolicki TIBCO Spotfire – @TibcoSpotfire Brett Stupakevich – @Brett2point0 Melissa Data – @MelissaData Ira Warren Whiteside – @irawhiteside Dun & Bradstreet – @DnBUS Shelly Lucas – @pisarose helpIT Systems – @helpIT Joshua Buckler – @bucklerjosh Datamartist – @Datamartist Experian QAS – @Experian_QAS Ataccama – @Ataccama DataMentors – @DataMentors DQ Global – @DQ_Global Pitney Bowes Software – @PitneyBowes Utopia, Inc. – @UtopiaInc Enterworks – @enterworks Fergus Cloughley – @Master_OBASHI Match2Lists – @Match2Lists DMG Federal – @dmgfederal Data Science at UC Berkeley – @BerkeleyData MIKE2.0 – @OpenMethodology The Data Manager’s Public Library – @TheDMPL Dataversity – @Dataversity ECCMA – @ECCMA SmartData Collective – @SmartDataCo Information Management – @InfoMgmt Tony Carrini – @TonyCarrini Julie Langenkamp – @JulieLangenkamp Jim Ericson – @JimEricson Eric Kavanagh – @Eric_Kavanagh Justin Kern – @IMJustinKern Whitney Eden – @WhitneyAEden Enterprise CIO Forum – @ECIOForum John Dodge – @Thedodgeretort Paul Calento – @pcalento Pearl Zhu – @pearl_zhu Eric D. Brown – @ericdbrown

730 Days and 264 Blog Posts Later . . .

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

Thank You

Thank you for reading my many musings on data quality and its related disciplines, and for tolerating my various references, from Adventures in Data Profiling to Social Karma, Shakespeare to Dr. Seuss, The Pirates of Penzance to The Rolling Stones, from The Three Musketeers to The Three Tweets, Dante Alighieri to Dumb and Dumber, Jack Bauer to Captain Jack Sparrow, Finding Data Quality to Discovering What Data Quality Technology Wants, and from Schrödinger’s Cat to the Buttered Cat.

Thank you for reading Obsessive-Compulsive Data Quality for the last two years.  Your readership is deeply appreciated.

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Twitter, Data Governance, and a #ButteredCat #FollowFriday

I have previously blogged in defense of Twitter, the pithy platform for social networking that I use perhaps a bit too frequently, and about which many people argue is incompatible with meaningful communication (Twitter that is, not me—hopefully).

Whether it is a regularly scheduled meeting of the minds, like the Data Knights Tweet Jam, or simply a spontaneous supply of trenchant thoughts, Twitter quite often facilitates discussions that deliver practical knowledge or thought-provoking theories.

However, occasionally the discussions center around more curious concepts, such as a paradox involving a buttered cat, which thankfully Steve Sarsfield, Mark Horseman, and Daragh O Brien can help me attempt to explain (remember I said attempt):

So, basically . . . successful data governance is all about Buttered Cats, Breaded CxOs, and Beer-Battered Data Quality Managers working together to deliver Bettered Data to the organization . . . yeah, that all sounded perfectly understandable to me.

But just in case you don’t have your secret decoder ring, let’s decipher the message (remember: “Be sure to drink your Ovaltine”):

• Buttered Cats – metaphor for combining the top-down and bottom-up approaches to data governance
• Breaded CxOs – metaphor for executive sponsors, especially ones providing bread (i.e., funding, not lunch—maybe both)
• Beer-Battered Data Quality Managers – metaphor (and possibly also a recipe) for data stewardship
• Bettered Data – metaphor for the corporate asset thingy that data governance helps you manage

(For more slightly less cryptic information, check out my previous post/poll: Data Governance and the Buttered Cat Paradox)

#FollowFriday Recommendations

Today is #FollowFriday, the day when Twitter users recommend other users you should follow, so here are some great tweeps for mostly non-buttered-cat tweets about Data Quality, Data Governance, Master Data Management, and Business Intelligence:

(Please Note: This is by no means a comprehensive list, is listed in no particular order whatsoever, and no offense is intended to any of my tweeps not listed below.  I hope that everyone has a great #FollowFriday and an even greater weekend.)

Henrik Liliendahl Sørensen – @hlsdk Dylan Jones – @DataQualityPro Phil Simon – @PhilSimon Julian Schwarzenbach – @jschwa1 Rich Murnane – @murnane Rob Paller – @RobPaller Stephen Putman – @SJPutman Augusto Albeghi – @Stray__Cat Garnie Bolling – @GarnieBolling Mark Horseman – @MarkHorseman William Sharp – @dqchronicle Ken O’Connor – @KenOConnorData Graham Rhind – @GrahamRhind Jill Wanless – @sheezaredhead Jacqueline Roberts – @JackieMRoberts Terri Rylander – @BIMarcom Loraine Lawson – @LoraineLawson Sarah Burnett – @SarahBurnett Corinna Martinez – @Futureratti Beth Breidenbach – @bbreidenbach Julie Hunt – @juliebhunt Nicole Carriere – @carrni Teresa Cottam – @Teresacottam Loretta Mahon Smith – @silverdata April Reeve – @Datagrrl Karen Lopez – @datachick Rob Drysdale – @projmgr Ted Louie – @TedLouie Nick Giuliano – @Nick_Giuliano John Owens – @JohnIMM Dalton Cervo – @dcervo Phil Wright – @faropress Jaime Fitzgerald – @jaimefitzgerald Simon Daniels – @mktginsightguy Marcus Borba – @marcusborba Alexej Freund – @alexej_freund Vish Agashe – @VishAgashe Chris Sorensen – @wjdataguy Mark Lorion – @mark_lorion Brett Stupakevich – @Brett2point0 Steve Dine – @steve_dine David Pratt – @DataMgmtWonk

IAIDQ – @IAIDQ Daragh O Brien – @daraghobrien Jill Dyché – @JillDyche David Loshin – @DavidLoshin Neil Raden – @NeilRaden James Taylor – @jamet123 Shawn Rogers – @shawnrog Wayne Eckerson – @weckerson Peter Thomas – @PeterJThomas Timo Elliott – @TimoElliott Dan Power – @dan_power William McKnight – @williammcknight Gartner Research – @Gartner_inc Ted Friedman – @ted_friedman Andy Bitterer – @bitterer ANALYSTerical – @ANALYSTerical Merv Adrian – @merv Forrester Research – @forrester Robert Karel – @rbkarel Initiate, an IBM Company – @IBMInitiate Jarrett Goldfedder – @JGoldfed DataFlux, a SAS Company – @DataFlux Data Roundtable – @DataRoundtable Kalido – @Kalido Winston Chen – @WinstonChen John Evans – @jmevans00 Lorita Ba Vannah – @lorita Informatica – @InformaticaCorp Clarke Patterson – @ClarkePatterson Talend – @Talend Steve Sarsfield – @SteveSarsfield Trillium Software – @TrilliumSW Pervasive Software – @DataIntegrate Paige Roberts – @RobertsPaige Experian QAS – @Experian_QAS Datamartist – @Datamartist Utopia, Inc. – @UtopiaInc Melissa Data – @MelissaData Ira Warren Whiteside – @irawhiteside DataMentors – @DataMentors Information Management – @InfoMgmt Enterprise CIO Forum – @ECIOForum

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Social Karma (Part 7) – Twitter

Commendable Comments (Part 9)

Today is February 14 — Valentine’s Day — the annual celebration of enduring romance, where true love is publicly judged according to your willingness to purchase chocolate, roses, and extremely expensive jewelry, and privately judged in ways that nobody (and please, trust me when I say nobody) wants to see you post on Twitter, Facebook, Flickr, YouTube, or your blog.

This is the ninth entry in my ongoing series for expressing my true love to my readers for their truly commendable comments on my blog posts.  Receiving comments is the most rewarding aspect of my blogging experience.  Although I love all of my readers, I love my commenting readers most of all.

Commendable Comments

On Data Quality Industry: Problem Solvers or Enablers?, Henrik Liliendahl Sørensen commented:

“I sometimes compare our profession with that of dentists.  Dentists are also believed to advocate for good habits around your teeth, but are making money when these good habits aren’t followed.

So when 4 out 5 dentists recommend a certain toothpaste, it is probably no good :-)

Seriously though, I take the amount of money spent on data quality tools as a sign that organizations believe there are issues best solved with technology.  Of course these tools aren’t magic.

Data quality tools only solve a certain part of your data and information related challenges.  On the other hand, the few problems they do solve may be solved very well and cannot be solved by any other line of products or in any practical way by humans in any quantity or quality.”

On Data Quality Industry: Problem Solvers or Enablers?, Jarrett Goldfedder commented:

“I think that the expectations of clients from their data quality vendors have grown tremendously over the past few years.  This is, of course, in line with most everything in the Web 2.0 cloud world that has become point-and-click, on-demand response.

In the olden days of 2002, I remember clients asking for vendors to adjust data only to the point where dashboard statistics could be presented on a clean Java user interface.  I have noticed that some clients today want the software to not just run customizable reports, but to extract any form of data from any type of database, to perform advanced ETL and calculations with minimal user effort, and to be easy to use.  It’s almost like telling your dentist to fix your crooked teeth with no anesthesia, no braces, no pain, during a single office visit.

Of course, the reality today does not match the expectation, but data quality vendors and architects may need to step up their game to remain cutting edge.”

On Data Quality is not an Act, it is a Habit, Rob Paller commented:

“This immediately reminded me of the practice of Kaizen in the manufacturing industry.  The idea being that continued small improvements yield large improvements in productivity when compounded.

For years now, many of the thought leaders have preached that projects from business intelligence to data quality to MDM to data governance, and so on, start small and that by starting small and focused, they will yield larger benefits when all of the small projects are compounded.

But the one thing that I have not seen it tied back to is the successes that were found in the leaders of the various industries that have adopted the Kaizen philosophy.

Data quality practitioners need to recognize that their success lies in the fundamentals of Kaizen: quality, effort, participation, willingness to change, and communication. The fundamentals put people and process before technology.  In other words, technology may help eliminate the problem, but it is the people and process that allow that elimination to occur.”

On Data Quality is not an Act, it is a Habit, Dylan Jones commented:

“Subtle but immensely important because implementing a coordinated series of small, easily trained habits can add up to a comprehensive data quality program.

In my first data quality role we identified about ten core habits that everyone on the team should adopt and the results were astounding.  No need for big programs, expensive technology, change management and endless communication, just simple, achievable habits that importantly were focused on the workers.

To make habits work they need the WIIFM (What’s In It For Me) factor.”

On Darth Data, Rob Drysdale commented:

“Interesting concept about using data for the wrong purpose.  I think that data, if it is the ‘true’ data can be used for any business decision as long as it is interpreted the right way.

One problem is that data may have a margin of error associated with it and this must be understood in order to properly use it to make decisions.  Another issue is that the underlying definitions may be different.

For example, an organization may use the term ‘customer’ when it means different things.  The marketing department may have a list of ‘customers’ that includes leads and prospects, but the operational department may only call them ‘customers’ when they are generating revenue.

Each department’s data and interpretation of it is correct for their own purpose, but you cannot mix the data or use it in the ‘other’ department to make decisions.

If all the data is correct, the definitions and the rules around capturing it are fully understood, then you should be able to use it to make any business decision.

But when it gets misinterpreted and twisted to suit some business decision that it may not be suited for, then you are crossing over to the Dark Side.”

On Data Governance and the Social Enterprise, Jacqueline Roberts commented:

“My continuous struggle is the chaos of data electronically submitted by many, many sources, different levels of quality and many different formats while maintaining the history of classification, correction, language translation, where used, and a multitude of other ‘data transactions’ to translate this data into usable information for multi-business use and reporting.  This is my definition of Master Data Management.

I chuckled at the description of the ‘rigid business processes’ and I added ‘software products’ to the concept, since the software industry must understand the fluidity of the change of data to address the challenges of Master Data Management, Data Governance, and Data Cleansing.”

On Data Governance and the Social Enterprise, Frank Harland commented: 

“I read: ‘Collaboration is the key to business success. This essential collaboration has to be based on people, and not on rigid business processes . . .’

And I think: Collaboration is the key to any success.  This must have been true since the time man hunted the Mammoth.  When collaborating, it went a lot better to catch the bugger.

And I agree that the collaboration has to be based on people, and not on rigid business processes.  That is as opposed to based on rigid people, and not on flexible business processes. All the truths are in the adjectives.

I don’t mean to bash, Jim, I think there is a lot of truth here and you point to the exact relationship between collaboration as a requirement and Data Governance as a prerequisite.  It’s just me getting a little tired of Gartner saying things of the sort that ‘in order to achieve success, people should work together. . .’

I have a word in mind that starts with ‘du’ and ends with ‘h’ :-)”

On Quality and Governance are Beyond the Data, Milan Kučera commented:

“Quality is a result of people’s work, their responsibility, improvement initiatives, etc.  I think it is more about the company culture and its possible regulation by government.  It is the most complicated to set-up a ‘new’ (information quality) culture, because of its influence on every single employee.  It is about well balanced information value chain and quality processes at every ‘gemba’ where information is created.

Confidence in the information is necessary because we make many decisions based on it.  Sometimes we do better or worse then before.  We should store/use as much accurate information as possible.

All stewardship or governance frameworks should help companies with the change of its culture, define quality measures (the most important is accuracy), cost of poor quality system (allowing them to monitor impacts of poor quality information), and other necessary things.  Only at this moment would we be able to trust corporate information and make decisions.

A small remark on technology only.  Data quality technology is a good tool for helping you to analyze ‘technical’ quality of data – patterns, business rules, frequencies, NULL or Not NULL values, etc.  Many technology companies narrow information quality into an area of massive cleansing (scrap/rework) activities.  They can correct some errors but everything in general leads to a higher validity, but not information accuracy.  If cleansing is implemented as a regular part of the ETL processes then the company institutionalizes massive correction, which is only a cost adding activity and I am sure it is not the right place to change data contents – we increase data inconsistency within information systems.

Every quality management system (for example TQM, TIQM, Six Sigma, Kaizen) focuses on improvement at the place where errors occur – gemba.  All those systems require: leaders, measures, trained people, and simply – adequate culture.

Technology can be a good assistant (helper), but a bad master.”

On Can Data Quality avoid the Dustbin of History?, Vish Agashe commented:

“In a sense, I would say that the current definitions and approaches of/towards data quality might very well not be able to avoid the Dustbin of History.

In the world of phones and PDAs, quality of information about environments, current fashions/trends, locations and current moods of the customer might be more important than a single view of customer or de-duped customers.  The pace at which consumer’s habits are changing, it might be the quality of information about the environment in which the transaction is likely to happen that will be more important than the quality of the post transaction data itself . . . Just a thought.”

On Does your organization have a Calumet Culture?, Garnie Bolling commented:

“So true, so true, so true.

I see this a lot.  Great projects or initiatives start off, collaboration is expected across organizations, and there is initial interest, big meetings / events to jump start the Calumet.  Now what, when the events no longer happen, funding to fly everyone to the same city to bond, share, explore together dries up.

Here is what we have seen work. After the initial kick off, have small events, focus groups, and let the Calumet grow organically. Sometimes after a big powwow, folks assume others are taking care of the communication / collaboration, but with a small venue, it slowly grows.

Success breeds success and folks want to be part of that, so when the focus group achieves, the growth happens.  This cycle is then repeated, hopefully.

While it is important for folks to come together at the kick off to see the big picture, it is the small rolling waves of success that will pick up momentum, and people will want to join the effort to collaborate versus waiting for others to pick up the ball and run.

Thanks for posting, good topic.  Now where is my small focus group? :-)”

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 in October, November, and December of 2010.

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.

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Commendable Comments (Part 7)

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Commendable Comments (Part 2)

Commendable Comments (Part 1)

#FollowFriday Spotlight: @PhilSimon

FollowFriday Spotlight is an OCDQ regular segment highlighting someone you should follow—and not just Fridays on Twitter.

Phil Simon Systems Phil Simon on the Data Roundtable Phil Simon on Twitter Phil Simon on LinkedIn The New Small on Facebook The New Small (Book Website)

Phil Simon is an independent technology consultant, author, writer, and dynamic public speaker for hire, who focuses on the intersection of business and technology.  Phil is the author of three books (see below for more details) and also writes for a number of technology media outlets and sites, and hosts the podcast Technology Today.

As an independent consultant, Phil helps his clients optimize their use of technology.  Phil has cultivated over forty clients in a wide variety of industries, including health care, manufacturing, retail, education, telecommunications, and the public sector.

When not fiddling with computers, hosting podcasts, putting himself in comics, and writing, Phil enjoys English Bulldogs, tennis, golf, movies that hurt the brain, fantasy football, and progressive rock.  Phil is a particularly zealous fan of Rush, Porcupine Tree, and Dream Theater.  Anyone who reads his blog posts or books will catch many references to these bands.

Books by Phil Simon

My review of The New Small: By leveraging what Phil Simon calls the Five Enablers (Cloud computing, Software-as-a-Service (SaaS), Free and open source software (FOSS), Mobility, Social technologies), small businesses no longer need to have technology as one of their core competencies, nor invest significant time and money in enabling technology, which allows them to focus on their true core competencies and truly compete against companies of all sizes. The New Small serves as a practical guide to this brave new world of small business.

My review of The Next Wave of Technologies: The constant challenge faced by organizations, large and small, which are using technology to support the ongoing management of their decision-critical information, is that the business world of information technology can never afford to remain static, but instead, must dynamically evolve and adapt, in order to protect and serve the enterprise’s continuing mission to survive and thrive in today’s highly competitive and rapidly changing marketplace. The Next Wave of Technologies is required reading if your organization wishes to avoid common mistakes and realize the full potential of new technologies—especially before your competitors do.

My review of Why New Systems Fail: Why New Systems Fail is far from a doom and gloom review of disastrous projects and failed system implementations.  Instead, this book contains numerous examples and compelling case studies, which serve as a very practical guide for how to recognize, and more importantly, overcome the common mistakes that can prevent new systems from being successful. Phil Simon writes about these complex challenges in a clear and comprehensive style that is easily approachable and applicable to diverse audiences, both academic and professional, as well as readers with either a business or a technical orientation.

Blog Posts by Phil Simon

In addition to his great books, Phil is a great blogger.  For example, check out these brilliant blog posts written by Phil Simon:

• The Technology Adoption Life Cycle
• The Battle for 60
• Can Consultants Lead? Three Viewpoints
• Heat and Trust
• Social Networking in the Workplace
• Data Management and the Importance of Social Search
• Defending the Data
• Johnny Cash, Purging, and Data Atrocities
• Potato Chips and The Myth of the Data Warehouse
• Distant Early Warnings
• Simplicity, Enterprise Architecture, and Data Quality
• George Orwell and Why I Write
• Publishing Mysteries

Knights of the Data Roundtable

Phil Simon and I co-host and co-produce the wildly popular podcast Knights of the Data Roundtable, a bi-weekly data management podcast sponsored by the good folks at DataFlux, a SAS Company. The podcast is a frank and open discussion about data quality, data integration, data governance and all things related to managing data.

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Social Karma (Part 7) – Twitter

The Two U’s and the Three C’s

The question that I get asked most frequently about blogging is:

“Is there a simple formula for writing effective blog posts?”

And the only honest answer is:

“NO!  There is NOT a simple formula for writing effective blog posts.”

Well, okay . . . according to conventional blogging wisdom . . . maybe there is one simple formula:

The Two U’s

The first aspect of conventional blogging wisdom is to follow the Two U’s:

1. Useful – Focus on your reader and provide them assistance with a specific problem

2. Unique – Capture your reader’s attention and share your perspective in your own voice

Blogging is all about you.  No, not you meaning me, the blogger — you meaning you, the reader.

To be useful, blogging has to be all about the reader.  If you write only for yourself, then you will also be your only reader.

Useful blog posts often provide “infotainment” — a combination of information and entertainment — that, when it’s done well, can turn readers into raving fans.  Just don’t forget—your blog content has to be informative and entertaining to your readers.

One important aspect of being unique is writing effective titles.  Most potential readers scan titles to determine if they will click and read more.  There is a delicate balance between effective titles and “baiting” – which will only alienate potential readers.

If you write a compelling title that makes your readers click through to an interesting post, then “You Rock!”  However, if you write a “Shock and Awe” title followed by “Aw Shucks” content, then “You Suck!”

Your blog content also has to be unique—your topic, position, voice, or a combination of all three.

Consider the following when striving to write unique blog posts:

• The easiest way to produce unique content is to let your blogging style reflect your personality

• Don’t be afraid to express your opinion—even on subjects where it seems like “everything has already be said”

• Your opinion is unique—because it is your opinion

• An opinion—as long as it is respectfully given—is never wrong

• Consistency in both style and message is important, however it’s okay to vary your style and/or change your opinion

The Three C’s

The second aspect of conventional blogging wisdom is to follow the Three C’s:

1. Clear – Get to the point and stay on point

2. Concise – No longer than absolutely necessary

3. Consumable – Formatted to be easily read on a computer screen

Clear blog posts typically have a single theme or one primary topic to communicate.  Don’t run off on tangents, especially ones not related to the point you are trying to make.  If you have several legitimate sub-topics to cover, then consider creating a series.

Concise doesn’t necessarily mean “write really short blog posts.”  There is no specific word count to target.  Being concise simply means taking out anything that doesn’t need to be included.  Editing is the hardest part of writing, but also the most important.

Consumable content is extremely essential when people are reading off of a computer screen.

Densely packed text attacks the eyes, which doesn’t encourage anyone to keep reading.

Consumable blog posts effectively use techniques such as the following:

• Providing an introduction and/or a conclusion

• Using section headings (in a larger size or different font or both)

• Varying the lengths of both sentences and paragraphs

• Highlighting key words or phrases using bold or italics

• Making or summarizing keys points in a short sentence or a short paragraph

• Making or summarizing key points using numbered or bulleted lists

As a general rule, the longer (although still both clear and concise) the blog post, the more consumable you need to make it.

Conclusion

If writing is not your thing, and you’re podcasting or video blogging or using some combination of all three (and that’s another way to be unique), I still think the conventional blogging wisdom applies, which, of course, you are obviously free to ignore since blogging is definitely more art than science.

However, I recommend that you first learn and practice the conventional blogging wisdom.

After all, it’s always more fun to break the rules when you actually know what the rules are.

#FollowFriday Spotlight: @hlsdk

FollowFriday Spotlight is an OCDQ regular segment highlighting someone you should follow—and not just Fridays on Twitter.

Liliendahl on Data Quality (Blog) Henrik Liliendahl Sørensen on Twitter Henrik Liliendahl Sørensen on LinkedIn

Henrik Liliendahl Sørensen is a data quality and master data management (MDM) professional with over 30 years of experience in the information technology (IT) business working within a large range of business areas, such as government, insurance, manufacturing, membership, healthcare, and public transportation.

For more details about what Henrik has been, and is, working on, check out his My Been Done List and 2011 To Do List.

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.

So check out Liliendahl on Data Quality for great blog posts written by Henrik Liliendahl Sørensen, such as these popular posts:

• What is Data Quality anyway?
• 55 reasons to improve data quality
• Top 5 Reasons for Downstream Cleansing
• Bad word?: Data Owner
• The Myth about a Myth
• Returns from Investing in a Data Quality Tool
• The Next Level
• Data Quality from the Cloud
• Big Time ROI in Identity Resolution
• What is a best-in-class match engine?
• Create Table Homo_Sapiens
• What’s In a Given Name?
• What’s in an eMail Address? 
• Perfect Wrong Answer
• Sharing data is key to a single version of the truth
• The Statue of Liberty versus The Little Mermaid

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Social Karma (Part 7) – Twitter

Quality is more important than Quantity

Quantity/Quality lists shown above are from my social media presentation, which you can download by clicking on this link: Social Karma Presentation

Effectively using social media in a business context requires a significant commitment—mostly measured in time.

Since the “opportunity cost” of social media can be quite high, many understandably argue about how to effectively measure its return on investment (ROI), which can often feel like you are searching for the Sasquatch or the Loch Ness Monster.

No, social media ROI is not an urban myth.

However, the Albert Einstein quote “not everything that can be counted counts, and not everything that counts can be counted” is relevant to social media ROI because quality is more important than quantity—and quality is also more difficult to measure.

Social media ROI is not measured in followers, fans, recommendations, subscribers, comments or other feedback.  Although this quantitative analysis is useful and its metrics can be meaningful, it is important to realize that this only measures connection.

Qualitative analysis is more challenging because it attempts to measure your engagement with the online community.

Engagement is about going beyond establishing a presence and achieving active participation.  Engagement is measured by the quality of the relationships you are able to form and maintain—not the quantity of connections you are able to collect and count.

Although both quantitative and qualitative analysis are essential to forming a complete measurement of your social media ROI, quality is more important than quantity, because engagement is more important than connection.

Engagement requires a long-term investment in the community, but if you’re not willing to make a long-term investment, then don’t expect any meaningful returns from your social media efforts.

Don’t Ignore “The Man Behind the Curtain”

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: OCDQ Video

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Listening and Broadcasting

Photo via Flickr by: Colleen AF Venable

Photo via Flickr by: Anders Pollas

As we continue to witness the decline of traditional media and the corresponding rise of social media, the business world is attempting to keep up with the changing times.  Many organizations are “looking to do whatever it is that’s intended to replace advertising,” explained Douglas Rushkoff in a recent blog post about how marketing threatens the true promise of social media by “devolving to the limited applications of social marketing” and trying to turn the “social landscape back into a marketplace.”

We can all relate to Rushkoff’s central concern—the all-too-slippery slope separating social networking from social marketing.

The primary reason I started blogging was to demonstrate my expertise and establish my authority with regards to data quality and its related disciplines.  As an independent consultant, I am trying to help sell my writing, speaking, and consulting services.

You and/or your company are probably using social media to help sell your products and services as well.

Effective social networking is about community participation, which requires actively listening, inviting others to get involved, sharing meaningful ideas, contributing to conversations—and not just broadcasting your sales and marketing messages.

An often cited reason for the meteoric rise of social media is its exchange of a broadcast medium for a conversation medium.  However, some people, including Mitch Joel and Jay Baer, have pondered whether social media conversations are a myth.

“One of the main tenets of social media,” Joel blogged, “was the reality that brands could join a conversation, but by the looks of things there aren’t really any conversations happening at all.” 

Joel wasn’t being negative, just observational.  He pointed out that most blog comments provide feedback, not a back and forth conversation between blogger and reader, Twitter “feels more like everyone screaming a thought at once than a conversation that can be followed and engaged with” and “Facebook has some great banter with the wall posts and status updates, but it’s more chatty than conversational and it’s not an open/public environment.”

“To expect social media to truly emulate conversation as we know it is a fools errand,” Baer blogged.  “The information exchange is asynchronous.  However, there’s a difference between striving for conversation and settling for broadcasting.  The success path must lie somewhere in the middle of those two boundaries.”

Regardless of how we are striving for conversation, whether it be blogging, tweeting, Facebooking, or a face-to-face discussion, we must remember the importance of empathically listening to each other—and not just waiting for our turn to broadcast.

An effective social media strategy is essential for organizations as well as individual professionals, but it is a constant struggle to find the right balance between the headphones and the bullhorn—between listening and broadcasting.

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#FollowFriday Spotlight: @DataQualityPro

FollowFriday Spotlight is an OCDQ regular segment highlighting someone you should follow—and not just Fridays on Twitter.

Links for Data Quality Pro and Dylan Jones: DataQualityPro.com @DataQualityPro on Twitter Dylan Jones on the Data Roundtable Dylan Jones on LinkedIn

Data Quality Pro, founded and maintained by Dylan Jones, is a free and independent community resource dedicated to helping data quality professionals take their career or business to the next level.  Data Quality Pro is your free expert resource providing data quality articles, webinars, forums and tutorials from the world’s leading experts, every day.

With the mission to create the most beneficial data quality resource that is freely available to members around the world, the goal of Data Quality Pro is “winning-by-sharing” and they believe that by contributing a small amount of their experience, skill or time to support other members then truly great things can be achieved.

Membership is 100% free and provides a broad range of additional content for professionals of all backgrounds and skill levels.

Check out the Best of Data Quality Pro, which includes the following great blog posts written by Dylan Jones in 2010:

• Data Quality staff retention – what’s your approach?
• Cash-Strapped Procurement? Try Sharing Data Quality Software
• Are You Afraid to Say Goodbye to Your Data?
• Data Quality Director Required - Must Possess Powers of Invincibility
• Rise of the Data Quality Practitioner 2.0
• Data Quality Project Selection - A Missing Skill?
• How Are You Creating a Pull for Data Quality in Your Organisation?
• Does Your Project Suffer From Data Quality Product Myopia?
• Are You "Cookie-Cutting" Your Data Quality Strategy?
• WANTED: Data Quality Change Agents

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Social Karma (Part 7) – Twitter

Please don’t become a zombie in 2011

If one of your New Year’s Resolutions is to start a blog, please be forewarned that the blogosphere has a real zombie problem.

No, not that kind of zombie.

“Zombie” is a slang term used to describe a blog that has stopped publishing new posts.  In other words, the blog has joined the Blogosphere of the Living Dead, which is comprised of blogs that still have a valid URL, but desperately crave new “Posts!”

It’s Not Personal—Zombies are Professional

If you’re considering starting a personal blog (especially one about “real zombies”), then please stop reading—and start blogging.

However, if you’re considering starting a professional blog, then please continue reading.  By a “professional blog” I do not mean a blog that makes money.  I simply mean a blog that’s part of the social media strategy for your organization or a blog that helps advance your professional career—which, yes, may also directly or (far more likely, if at all) indirectly make you money.

If you are seriously considering starting a professional blog, before you do anything else, complete the 20-10-5 plan.

The 20-10-5 Plan

• Brainstorm 20 high level ideas for blog posts
• Write 10 rough drafts based on those ideas
• Finish 5 ready-to-publish posts from those drafts

If you are unable to complete this simple plan, then seriously reconsider starting a professional blog.

Please Note: I will add the caveat that if writing is not your thing, and you’re planning on podcasting or video blogging instead, I still adamantly believe you must complete the 20-10-5 plan.  In essence, the plan is simply a challenge to see if you can create five pieces of ready-to-publish content—BEFORE you launch your professional blog, since IMHO—if you can’t, then don’t.

Recommended Next Steps

If you completed the 20-10-5 plan, then after you launch your blog, consider the following recommendations:

• Do not post more than once a week
• Maintain an editorial calendar and schedule your future posts
• Finish more ready-to-publish posts (you’re good until Week 6 because of the 20-10-5 plan)

Yes, you’ll be tempted to start posting more than once a week.  Yes, you’ll be eager to share your brilliance with the blogosphere.

However, just like many new things, blogging is really fun—when it’s new.

So let’s run the numbers:

• Posting once a week = 52 blog posts a year
• Posting twice a week = 104 blog posts a year
• Posting five times a week (basically once every weekday) = 260 blog posts a year

I am not trying to harsh your mellow.  I am simply saying that you need to pace yourself—especially at the beginning.

I am not a Zombie—or a Social Media Expert

I am not a “social media expert.”  In fact, until late 2008, I wasn’t even interested enough to ask people what they meant when I heard them talking about “social media.”  I started blogging, tweeting, and using other social media in early 2009.

Do I practice what I preach?  Check my archives.

My blog was started in March 2009.  I published 5-8 posts per month (1-2 posts per week) for each of the first five months, and then I gradually increased my posting frequency.  Now, almost two years later, I have published 236 posts on this blog, which is an overall average of 10 posts per month (2-3 posts per week), without ever posting fewer than 5 times in one month.

So if you do decide to become a blogger, please don’t become a zombie in 2011—wait until the Zombie Apocalypse of 2012 :-)

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#FollowFriday and Re-Tweet-Worthiness

There is perhaps no better example of the peer pressure aspects of social networking than FollowFriday—the day when Twitter users recommend other users that you should follow (i.e., “I recommended you, why didn’t you recommend me?”).

However, every day of the week re-tweeting (the forwarding of another user’s Twitter status update, aka tweet) is performed.  Many bloggers (such as myself) use Twitter to promote their content by tweeting links to their new blog posts, and therefore, most re-tweets are attempts—made by the other members of the blogger’s collablogaunity—to help share meaningful content.

But I would be willing to wager that a considerable amount of re-tweeting is based on the act of reciprocity—and not based on evaluating the Re-Tweet-Worthiness of the shared content.  In other words, I believe that many people (myself included) sometimes don’t read what they re-tweet, but simply share content from a previously determined re-tweet-worthy source, or a source that they hope will reciprocate in the future (i.e., “I re-tweeted your blog post, why didn’t you re-tweet my blog post?”).

How do YOU determine Re-Tweet-Worthiness?

#FollowFriday Recommendations

By no means a comprehensive list, and listed in no particular order whatsoever, here are some great tweeps, and especially for truly re-tweet-worthy tweets about Data Quality, Data Governance, Master Data Management, and Business Intelligence:

Henrik Liliendahl Sørensen – @hlsdk Dylan Jones – @DataQualityPro Rich Murnane – @murnane Gordon Hamilton – @DQStudent Julian Schwarzenbach – @jschwa1 Ken O’Connor – @KenOConnorData Mark Horseman – @MarkHorseman Graham Rhind – @GrahamRhind Prashanta Chandramohan – @MDMGeek Phil Simon – @PhilSimon William Sharp – @dqchronicle Augusto Albeghi – @Stray__Cat Jill Wanless – @sheezaredhead Julie Hunt – @juliebhunt Jacqueline Roberts – @JackieMRoberts Terri Rylander – @BIMarcom Loraine Lawson – @LoraineLawson Sarah Burnett – @SarahBurnett Corinna Martinez – @Futureratti Beth Breidenbach – @bbreidenbach Nicola Askham – @Nicola_Askham Jenna Scherczer – @JScherczer Jen Stirrup – @JenStirrup Teresa Cottam – @Teresacottam Loretta Mahon Smith – @silverdata April Reeve – @Datagrrl Tamara Dull – @tamaradull Tracy Austin – @tracykowal Ina Mutschelknaus – @InaSAP Marinka Voorhout – @marinkavoorhout Meta Brown – @metabrown312 Lyndsay Wise – @wiseanalytics Kelle O’Neal – @FirstSanFranMDM Nicole Carriere – @carrni Robin Stehlik – @RobinPStehlik Giedre Aleknonyte – @googlea Natalie Eschen – @NatalieEschen Cindy Balon Harder – @CindyBHarder Kristin McMahon – @Kristin_McMahon Carrie Byrum – @CByrum Carla Gentry – @data_nerd Marie Haggberg – @HaggbergConsult Melinda Thielbar – @mthielbar Karen Lopez – @datachick Rob Drysdale – @projmgr Ted Louie – @TedLouie John Owens – @JohnIMM Axel Troike – @AxelTroike Rich Northwood – @TheDataGeek Jarrett Goldfedder – @JGoldfed Dalton Cervo – @dcervo Phil Wright – @faropress Garry Ure – @dq_midnightblue Jaime Fitzgerald – @jaimefitzgerald Simon Daniels – @mktginsightguy Ajay Ohri – @decisionstats Marcus Borba – @marcusborba Alexej Freund – @alexej_freund Vish Agashe – @VishAgashe Jean-Michel Franco – @jmichel_franco Jorge García – @jgptec Rohin Bhargava – @RohinBhargava Krish Krishnan – @datagenius Chris Sorensen – @wjdataguy Mark Lorion – @mark_lorion Steve Dine – @steve_dine Doug Newdick – @dougnewdick Richard Jarvis – @ManBagBlog David Pratt – @DataMgmtWonk David Ho – @David_Ho_DQ Jay Zaidi – @JayZaidi Rob Paller – @RobPaller Stephen Putman – @SJPutman JJ Burnam – @AmbivalentGeek Garnie Bolling – @GarnieBolling Chris Hale – @HaleChris Alan David Duncan – @Alan_D_Duncan Benjamin Wolfe – @BenjaminWolfe Nick Giuliano – @Nick_Giuliano Scott Delaney – @scottyd99999 FX Nicolas – @FxNicolas

IAIDQ – @IAIDQ Daragh O Brien – @daraghobrien David Loshin – @DavidLoshin Neil Raden – @NeilRaden James Taylor – @jamet123 Art Petty – @ArtPetty Paul Gillin – @pgillin Shawn Rogers – @shawnrog Wayne Eckerson – @weckerson Peter Thomas – @PeterJThomas Timo Elliott – @TimoElliott Dan Power – @dan_power William McKnight – @williammcknight Gwen Thomas – @gwenthomasdgi Claudia Imhoff – @Claudia_Imhoff Gartner Research – @Gartner_inc Ted Friedman – @ted_friedman Andy Bitterer – @bitterer Merv Adrian – @merv Forrester Research – @forrester Michele Goetz – @FORR_Mgoetz IBM Midsize Business – @MidmarketIBM IBM Initiate – @IBMInitiate IBM InfoSphere – @IBM_InfoSphere IBM Big Data – @IBMbigdata Crysta Anderson – @CrystaAnderson David Pittman – @TheSocialPitt James Kobielus – @jameskobielus DataFlux – @DataFlux Jill Dyché – @JillDyche Data Roundtable – @DataRoundtable SAS Software – @SASsoftware SAS Analytics – @SASanalytics Mark Troester – @mtroester Pervasive Software – @DataIntegrate Pervasive Big Data – @BigDataPVSW Paige Roberts – @RobertsPaige Kalido – @Kalido Bill Hewitt – @Kalido_CEO John Evans – @jmevans00 Informatica – @InformaticaCorp Robert Karel – @rbkarel Clarke Patterson – @ClarkePatterson Jakki Geiger – @JakkiGeiger Ravi Shankar – @Ravi_Shankar_ Talend – @Talend Steve Sarsfield – @SteveSarsfield Trillium Software – @TrilliumSW TIBCO Spotfire – @TibcoSpotfire Brett Stupakevich – @Brett2point0 Melissa Data – @MelissaData Ira Warren Whiteside – @irawhiteside Dun & Bradstreet – @DnBUS Shelly Lucas – @pisarose helpIT Systems – @helpIT Joshua Buckler – @bucklerjosh Datamartist – @Datamartist Experian QAS – @Experian_QAS Ataccama – @Ataccama DataMentors – @DataMentors DQ Global – @DQ_Global Pitney Bowes Software – @PitneyBowes Utopia, Inc. – @UtopiaInc Enterworks – @enterworks Fergus Cloughley – @Master_OBASHI Match2Lists – @Match2Lists MIKE2.0 – @OpenMethodology The Data Manager’s Public Library – @TheDMPL Dataversity – @Dataversity Information Management – @InfoMgmt Tony Carrini – @TonyCarrini Julie Langenkamp – @JulieLangenkamp Jim Ericson – @JimEricson Eric Kavanagh – @Eric_Kavanagh Justin Kern – @IMJustinKern Whitney Eden – @WhitneyAEden Enterprise CIO Forum – @ECIOForum John Dodge – @Thedodgeretort Paul Calento – @pcalento Pearl Zhu – @pearl_zhu Eric D. Brown – @ericdbrown

PLEASE NOTE: No offense is intended to any of my tweeps not listed above.  However, if you feel that I have made a glaring omission of an obviously Twitterific Tweep, then please feel free to post a comment below and add them to the list.  Thanks!

I hope that everyone has a great FollowFriday and an even greater weekend.  See you all around the Twittersphere.

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Tweet

Can Data Quality avoid the Dustbin of History?

After reading two blog posts about the 2011 predictions for data management by Steve Sarsfield and Henrik Liliendahl Sørensen, I was pondering writing a 2011 prediction post of my own—and then I read this recent Dilbert comic strip.

What if Dogbert is right and the only things that matter are social networks, games, and phones?  What implications does this have for the data management industry, and more specifically, the data quality profession?  How can data quality practitioners avoid being cast into the Dustbin of History in 2011 and beyond?

Perhaps we need to create a social network for data?  Let’s call it DataTweetBook.  Although we would be allowed to follow any data with a public profile, data would have to approve our friend requests—you know, in order to respect data’s privacy.

(Quick Side Bar Question: Do you think that your organization’s data would accept your friend request—or block you?)

Next, we would partner with Zynga and create DataVille and Data Quality Wars, which would be online games exclusive to the DataTweetBook platform.  These games would include fun challenges, like “consolidate duplicates in your contact database” and “design a user interface that prevents data quality issues from happening.”  You and your data can even ask other people and data in your social network for help with completing tasks, such as “ask postal reference data to validate your mailing addresses.”

Of course, we would then need to create iPhone and Android apps for DataTweetBook, DataVille, and Data Quality Wars, so that everyone can access the new social network and games on their mobile phones.  And eventually, we would start a bidding war between Apple and Google over the exclusive rights to make an integrated mobile device, either iDataPad or DataGoogler.

So that’s my 2011 prognostication for the data quality industry—it’s going be all about social networks, games, and phones.

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Data Governance and the Social Enterprise

In his blog post Socializing Software, Michael Fauscette explained that in order “to create a next generation enterprise, businesses need to take two concepts from the social web and apply them across all business functions: community and content.”

“Traditional enterprise software,” according to Fauscette, “was built on the concept of managing through rigid business processes and controlled workflow.  With process at the center of the design, people-based collaboration was not possible.”

Peter Sondergaard, the global head of research at Gartner, explained at a recent conference that “the rigid business processes which dominate enterprise organizational architectures today are well suited for routine, predictable business activities.  But they are poorly suited to support people who’s jobs require discovery, interpretation, negotiation and complex decision-making.”

“Social computing,” according to Sondergaard, “not Facebook, or Twitter, or LinkedIn, but the technologies and principals behind them will be implemented across and between all organizations, and it will unleash yet to be realized productivity growth.”

Since the importance of collaboration is one of my favorite topics, I like Fauscette’s emphasis on people-based collaboration and Sondergaard’s emphasis on the limitations of process-based collaboration.  The key to success for most, if not all, organizational initiatives is the willingness of people all across the enterprise to embrace collaboration.

Successful organizations view collaboration not just as a guiding principle, but as a call to action in their daily business practices.

As Sondergaard points out, the technologies and principals behind social computing are the key to enabling what many analysts have begun referring to as the social enterprise.  Collaboration is the key to business success.  This essential collaboration has to be based on people, and not on rigid business processes since business activities and business priorities are constantly changing.

Data Governance and the Social Enterprise

Often the root cause of poor data quality can be traced to a lack of a shared understanding of the roles and responsibilities involved in how the organization is using its data to support its business activities.  The primary focus of data governance is the strategic alignment of people throughout the organization through the definition, implementation, and enforcement of the policies that govern the interactions between people, business processes, data, and technology.

A data quality program within a data governance framework is a cross-functional, enterprise-wide initiative requiring people to be accountable for its data, business process, and technology aspects.  However, policy enforcement and accountability are often confused with traditional notions of command and control, which is the antithesis of the social enterprise that instead requires an emphasis on communication, cooperation, and people-based collaboration.

Data governance policies for data quality illustrate the intersection of business, data, and technical knowledge, which is spread throughout the enterprise, transcending any artificial boundaries imposed by an organizational chart or rigid business processes, where different departments or different business functions appear as if they were independent of the rest of the organization.

Data governance reveals how interconnected and interdependent the organization is, and why people-driven social enterprises are more likely to survive and thrive in today’s highly competitive and rapidly evolving marketplace.

Social enterprises rely on the strength of their people asset to successfully manage their data, which is a strategic corporate asset because high quality data serves as a solid foundation for an organization’s success, empowering people, enabled by technology, to optimize business processes for superior business performance.

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Commendable Comments (Part 8)

This Thursday is Thanksgiving Day, which is a United States holiday with a long and varied history.  The most consistent themes remain family and friends gathering together to share a large meal and express their gratitude.

This is the eighth entry in my ongoing series for expressing my gratitude to my readers for their truly commendable comments on my blog posts.  Receiving comments is the most rewarding aspect of my blogging experience.  Although I am truly grateful to all of my readers, I am most grateful to my commenting readers.

Commendable Comments

On The Data-Decision Symphony, James Standen commented:

“Being a lover of both music and data, it struck all the right notes!

I think the analogy is a very good one—when I think about data as music, I think about a companies business intelligence architecture as being a bit like a very good concert hall, stage, and instruments. All very lovely to listen to music—but without the score itself (the data), there is nothing to play.

And while certainly a real live concert hall is fantastic for enjoying Bach, I’m enjoying some Bach right now on my laptop—and the MUSIC is really the key.

Companies very often focus on building fantastic concert halls (made with all the best and biggest data warehouse appliances, ETL servers, web servers, visualization tools, portals, etc.) but forget that the point was to make that decision—and base it on data from the real world. Focusing on the quality of your data, and on the decision at hand, can often let you make wonderful music—and if your budget or schedule doesn't allow for a concert hall, you might be able to get there regardless.”

On “Some is not a number and soon is not a time”, Dylan Jones commented:

“I used to get incredibly frustrated with the data denial aspect of our profession.  Having delivered countless data quality assessments, I’ve never found an organization that did not have pockets of extremely poor data quality, but as you say, at the outset, no-one wants to believe this.

Like you, I’ve seen the natural defense mechanisms.  Some managers do fear the fallout and I’ve even had quite senior directors bury our research and quickly cut any further activity when issues have been discovered, fortunately that was an isolated case.

In the majority of cases though I think that many senior figures are genuinely shocked when they see their data quality assessments for the first time.  I think the big problem is that because they institutionalize so many scrap and rework processes and people that are common to every organization, the majority of issues are actually hidden.

This is one of the issues I have with the big shock announcements we often see in conference presentations (I’m as guilty as hell for these so call me a hypocrite) where one single error wipes millions off a share price or sends a space craft hurtling into Mars. 

Most managers don’t experience this cataclysm, so it’s hard for them to relate to because it implies their data needs to be perfect, they believe that’s unattainable and lose interest.

Far better to use anecdotes like the one cited in this blog to demonstrate how simple improvements can change lives and the bottom line in a limited time span.”

On The Real Data Value is Business Insight, Winston Chen commented:

“Yes, quality is in the eye of the beholder.  Data quality metrics must be calculated within the context of a data consumer.  This context is missing in most software tools on the market.

Another important metric is what I call the Materiality Metric.

In your example, 50% of customer data is inaccurate.  It’d be helpful if we know which 50%.  Are they the customers that generate the most revenue and profits, or are they dormant customers?  Are they test records that were never purged from the system?  We can calculate the materiality metric by aggregating a relevant business metric for those bad records.

For example, 85% of the year-to-date revenue is associated with those 50% bad customer records.

Now we know this is serious!”

On The Real Data Value is Business Insight, James Taylor commented:

“I am constantly amazed at the number of folks I meet who are paralyzed about advanced analytics, saying that ‘we have to fix/clean/integrate all our data before we can do that.’

They don’t know if the data would even be relevant, haven’t considered getting the data from an external source and haven't checked to see if the analytic techniques being considered could handle the bad or incomplete data automatically!  Lots of techniques used in data mining were invented when data was hard to come by and very ‘dirty’ so they are actually pretty good at coping.  Unless someone thinks about the decision you want to improve, and the analytics they will need to do so, I don’t see how they can say their data is too dirty, too inconsistent to be used.”

On The Business versus IT—Tear down this wall!, Scott Andrews commented:

“Early in my career, I answered a typical job interview question ‘What are your strengths?’ with:

‘I can bring Business and IT together to deliver results.’

My interviewer wryly poo-poo’d my answer with ‘Business and IT work together well already,’ insinuating that such barriers may have existed in the past, but were now long gone.  I didn’t get that particular job, but in the years since I have seen this barrier in action (I can attest that my interviewer was wrong).

What is required for Business Intelligence success is to have smart business people and smart IT people working together collaboratively.  Too many times one side or the other says ‘that’s not my job’ and enormous potential is left unrealized.”

On The Business versus IT—Tear down this wall!, Jill Wanless commented:

“It amazes me (ok, not really...it makes me cynical and want to rant...) how often Business and IT SAY they are collaborating, but it’s obvious they have varying views and perspectives on what collaboration is and what the expected outcomes should be.  Business may think collaboration means working together for a solution, IT may think it means IT does the dirty work so Business doesn’t have to.

Either way, why don’t they just start the whole process by having a (honest and open) chat about expectations and that INCLUDES what collaboration means and how they will work together.

And hopefully, (here’s where I start to rant because OMG it’s Collaboration 101) that includes agreement not to use language such as BUSINESS and IT, but rather start to use language like WE.”

On Delivering Data Happiness, Teresa Cottam commented:

“Just a couple of days ago I had this conversation about the curse of IT in general:

When it works no-one notices or gives credit; it’s only when it’s broken we hear about it.

A typical example is government IT over here in the UK.  Some projects have worked well; others have been spectacular failures.  Guess which we hear about?  We review failure mercilessly but sometimes forget to do the same with success so we can document and repeat the good stuff too!

I find the best case studies are the balanced ones that say: this is what we wanted to do, this is how we did it, these are the benefits.  Plus this is what I’d do differently next time (lessons learned).

Maybe in those lessons learned we should also make a big effort to document the positive learnings and not just take these for granted.  Yes these do come out in ‘best practices’ but again, best practices never get the profile of disaster stories...

I wonder if much of the gloom is self-fulfilling almost, and therefore quite unhealthy.  So we say it’s difficult, the failure rate is high, etc. – commonly known as covering your butt.  Then when something goes wrong you can point back to the low expectations you created in the first place.

But maybe, the fact we have low expectations means we don’t go in with the right attitude?

The self-defeating outcome is that many large organizations are fearful of getting to grips with their data problems.  So lots of projects we should be doing to improve things are put on hold because of the perceived risk, disruption, cost – things then just get worse making the problem harder to resolve.

Data quality professionals surely don’t want to be seen as effectively undertakers to the doomed project, necessary yes, but not surrounded by the unmistakable smell of death that makes others uncomfortable.

Sure the nature of your work is often to focus on the broken, but quite apart from anything else, isn’t it always better to be cheerful?”

On Why isn’t our data quality worse?, Gordon Hamilton commented:

“They say that sport coaches never teach the negative, or to double the double negative, they never say ‘don’t do that.’  I read somewhere, maybe Daniel Siegel’s stuff, that when the human brain processes the statement ‘don’t do that’ it drops the ‘don’t,’ which leaves it thinking ‘do that.’

Data quality is a complex and multi-splendiforous area with many variables intermingled, but our task as Data Quality Evangelists would be more pleasant if we were helping people rise to the level of the positive expectations, rather than our being codependent in their sinking to the level of the negative expectation.”

DQ-Tip: “There is no such thing as data accuracy...” sparked an excellent debate between Graham Rhind and Peter Benson, who is the Project Leader of ISO 8000, which is the international standards for data quality.  Their debate included the differences and interdependencies that exist between data and information, as well as between data quality and information quality.

Thanks for giving your comments

Thank you very much for giving your comments and sharing your perspectives with our collablogaunity.

This entry in the series highlighted commendable comments on OCDQ Blog posts published in August and September of 2010.

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.

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Commendable Comments (Part 4)

Commendable Comments (Part 3)

Commendable Comments (Part 2)

Commendable Comments (Part 1)