During this OCDQ Radio episode, conference co-chairs Joy Medved of IAIDQ and Melissa Hildebrand of ECCMA discuss the 2014 International Data Quality Summit (idqsummit.org) being held October 6-9 in historic Richmond, Virginia.
Read MoreOCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
Jill Dyché is the Vice President of Thought Leadership and Education at DataFlux. Jill’s role at DataFlux is a combination of best-practice expert, key client advisor and all-around thought leader. She is responsible for industry education, key client strategies and market analysis in the areas of data governance, business intelligence, master data management and customer relationship management. Jill is a regularly featured speaker and the author of several books.
Jill’s latest book, Customer Data Integration: Reaching a Single Version of the Truth (Wiley & Sons, 2006), was co-authored with Evan Levy and shows the business breakthroughs achieved with integrated customer data.
Dan Soceanu is the Director of Product Marketing and Sales Enablement at DataFlux. Dan manages global field sales enablement and product marketing, including product messaging and marketing analysis. Prior to joining DataFlux in 2008, Dan has held marketing, partnership and market research positions with Teradata, General Electric and FormScape, as well as data management positions in the Financial Services sector.
Dan received his Bachelor of Science in Business Administration from Kutztown University of Pennsylvania, as well as earning his Master of Business Administration from Bloomsburg University of Pennsylvania.
On this episode of OCDQ Radio, Jill Dyché, Dan Soceanu, and I discuss the recent Pacific Northwest BI Summit, where the three core conference topics were Cloud, Collaboration, and Big Data, the last of which lead to a discussion about Big Analytics.
Clicking on the link will take you to the episode’s blog post:
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
Dr. Thomas C. Redman (the “Data Doc”) is an innovator, advisor and teacher. He was first to extend quality principles to data and information in the late 80s. Since then he has crystallized a body of tools, techniques, roadmaps and organizational insights that help organizations make order-of-magnitude improvements.
More recently Tom has developed keen insights into the nature of data and formulated the first comprehensive approach to “putting data to work.” Taken together, these enable organizations to treat data as assets of virtually unlimited potential.
Tom has personally helped dozens of leaders and organizations better understand data and data quality and start their data programs. He is a sought-after lecturer and the author of dozens of papers and four books. The most recent, Data Driven: Profiting from Your Most Important Business Asset (Harvard Business Press, 2008) was a Library Journal best buy of 2008.
Prior to forming Navesink Consulting Group in 1996, Tom conceived the Data Quality Lab at AT&T Bell Laboratories in 1987 and led it until 1995. Tom holds a Ph.D. in statistics from Florida State University. He holds two patents.
On this episode of OCDQ Radio, Tom Redman and I discuss concepts from his Data Governance and Information Quality 2011 post-conference tutorial about organizing for data quality, which includes his call to action for your role in the data revolution.
Clicking on the link will take you to the episode’s blog post:
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.
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:
Oliver Claude from Informatica provided some tips for making data governance a reality:
Danette McGilvray, author of the book Executing Data Quality Projects, shared some of her data quality insights:
In the opening keynote address, Dan Hartley of ConAgra Foods shared his data governance and data quality experiences:
Winston Chen from Kalido discussed some aspects of delivering tangible value with data governance:
The closing keynote panel, moderated by Danette McGilvray, included the following insights:
In his post-conference tutorial, Tom Redman, author of the book Data Driven, described ten habits of those with the best data:
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!
Stuck in the Middle with Data Governance
DQ-BE: Invitation to Duplication
TDWI World Conference Orlando 2010
Light Bulb Moments at DataFlux IDEAS 2010
TDWI World Conference Chicago 2009
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.
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.
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
Data Governance Star Wars: Balancing Bureaucracy And Agility
DQ-View: Roman Ruts on the Road to Data Governance
Zig-Zag-Diagonal Data Governance
Data Governance and the Buttered Cat Paradox
Beware the Data Governance Ides of March
The Collaborative Culture of Data Governance
Connect Four and Data Governance
The Role Of Data Quality Monitoring In Data Governance
Quality and Governance are Beyond the Data
Podcast: Data Governance is Mission Possible
Video: Declaration of Data Governance
Don’t Do Less Bad; Do Better Good
Jack Bauer and Enforcing Data Governance Policies
MacGyver: Data Governance and Duct Tape
The Diffusion of Data Governance
Data Quality By Example (DQ-BE) is an OCDQ regular segment that provides examples of data quality key concepts.
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I recently received my invitation to 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. Well, as shown above, I actually received both of my invitations.
Although my postal address is complete, accurate, and exactly the same on both of the invitations, my name is slightly different (“James” vs. “Jim”), and my title (“Data Quality Journalist” vs. “Blogger-in-Chief”) and company (“IAIDQ” vs. “OCDQ Blog”) are both completely different. I wonder how many of the data quality software vendors sponsoring this conference would consider my invitations to be duplicates. (Maybe I’ll use the invitations to perform a vendor evaluation on the exhibit floor.)
So it would seem that even “The Premier Event in Data Governance and Data Quality” can experience data quality problems.
No worries, I doubt the invitation system will be one of the “Practical Approaches and Success Stories” presented—unless it’s used as a practical approach to a success story about demonstrating how embarrassing it might be to send duplicate invitations to a data quality journalist and blogger-in-chief. (I wonder if this blog post will affect the approval of my Press Pass for the event.)
Identifying Duplicate Customers
Adventures in Data Profiling (Part 7) – Customer Name
The Quest for the Golden Copy (Part 3) – Defining “Customer”
‘Tis the Season for Data Quality
Last week I attended the TDWI World Conference held November 7-12 in Orlando, Florida at the Loews Royal Pacific Resort.
As always, TDWI conferences offer a variety of full-day and half-day courses taught in an objective, vendor-neutral manner, designed for professionals and taught by in-the-trenches practitioners who are well known in the industry.
In this blog post, I summarize a few key points from two of the courses 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.
Wayne Eckerson, author of the book Performance Dashboards: Measuring, Monitoring, and Managing Your Business, described the four waves of business intelligence:
“Reporting is the jumping off point for analytics,” explained Eckerson, “but many executives don’t realize this. The most powerful aspect of analytics is testing our assumptions.” He went on to differentiate the two strains of analytics:
“A huge issue for predictive analytics is getting people to trust the predictions,” remarked Eckerson. “Technology is the easy part, the hard part is selling the business benefits and overcoming cultural resistance within the organization.”
“The key is not getting the right answers, but asking the right questions,” he explained, quoting Ken Rudin of Zynga.
“Deriving insight from its unique information will always be a competitive advantage for every organization.” He recommended the book Competing on Analytics: The New Science of Winning as a great resource for selling the business benefits of analytics.
Jill Dyché, a partner and co-founder of Baseline Consulting, explained that data governance transcends business intelligence and other enterprise information initiatives such as data warehousing, master data management, and data quality.
“Data governance is the organizing framework,” explained Dyché, “for establishing strategy, objectives, and policies for corporate data. Data governance is the business-driven policy making and oversight of corporate information.”
“Data governance is necessary,” remarked Dyché, “whenever multiple business units are sharing common, reusable data.”
“Data governance aligns data quality with business measures and acceptance, positions enterprise data issues as cross-functional, and ensures data is managed separately from its applications, thereby evolving data as a service (DaaS).”
In her excellent 2007 article Serving the Greater Good: Why Data Hoarding Impedes Corporate Growth, Dyché explained the need for “systemizing the notion that data – corporate asset that it is – belongs to everyone.”
“Data governance provides the decision rights around the corporate data asset.”
DQ-View: From Data to Decision
Podcast: Data Governance is Mission Possible
The Business versus IT—Tear down this wall!
MacGyver: Data Governance and Duct Tape
Live-Tweeting: Data Governance
TDWI World Conference Chicago 2009
Light Bulb Moments at DataFlux IDEAS 2010
Data Quality (DQ) Tips is an OCDQ regular segment. Each DQ-Tip is a clear and concise data quality pearl of wisdom.
“Data quality tools do not solve data quality problems—People solve data quality problems.”
This DQ-Tip came from the DataFlux IDEAS 2010 Assessing Data Quality Maturity workshop conducted by David Loshin, whose new book The Practitioner's Guide to Data Quality Improvement will be released next month.
Just like all technology, data quality tools are enablers. Data quality tools provide people with the capability for solving data quality problems, for which there are no fast and easy solutions. Although incredible advancements in technology continue, there are no Magic Beans for data quality.
And there never will be.
An organization’s data quality initiative can only be successful when people take on the challenge united by collaboration, guided by an effective methodology, and of course, enabled by powerful technology.
By far the most important variable in implementing successful and sustainable data quality improvements is acknowledging David’s sage advice: people—not tools—solve data quality problems.
DQ-Tip: “There is no such thing as data accuracy...”
DQ-Tip: “Data quality is primarily about context not accuracy...”
DQ-Tip: “There is no point in monitoring data quality...”
DQ-Tip: “Don't pass bad data on to the next person...”
DQ-Tip: “...Go talk with the people using the data”
DQ-Tip: “Data quality is about more than just improving your data...”
DQ-Tip: “Start where you are...”
This was the inaugural cartoon on Implementing Scrum by Michael Vizdos and Tony Clark, which does a great job of illustrating the fable of The Chicken and the Pig used to describe the two types of roles involved in Scrum, which, quite rare for our industry, is not an acronym, but one common approach among many iterative, incremental frameworks for agile software development.
Scrum is also sometimes used as a generic synonym for any agile framework. Although I’m not an expert, I’ve worked on more than a few agile programs. And since I am fond of metaphors, I will use the Chicken and the Pig to describe two common ways that scrums of all kinds can easily get screwed up:
However, let’s first establish a more specific context for agile development using one provided by a recent blog post on the topic.
In her excellent blog post A Contrarian’s View of Agile BI, Jill Dyché took a somewhat unpopular view of a popular view, which is something that Jill excels at—not simply for the sake of doing it—because she’s always been well-known for telling it like it is.
In preparation for the upcoming TDWI World Conference in San Diego, Jill was pondering the utilization of agile methodologies in business intelligence (aka BI—ah, there’s one of those oh so common industry acronyms straight out of The Acronymicon).
The provocative TDWI conference theme is: “Creating an Agile BI Environment—Delivering Data at the Speed of Thought.”
Now, please don’t misunderstand. Jill is an advocate for doing agile BI the right way. And it’s certainly understandable why so many organizations love the idea of agile BI. Especially when you consider the slower time to value of most other approaches when compared with, following Jill’s rule of thumb, how agile BI would have “either new BI functionality or new data deployed (at least) every 60-90 days. This approach establishes BI as a program, greater than the sum of its parts.”
“But in my experience,” Jill explained, “if the organization embracing agile BI never had established BI development processes in the first place, agile BI can be a road to nowhere. In fact, the dirty little secret of agile BI is this: It’s companies that don’t have the discipline to enforce BI development rigor in the first place that hurl themselves toward agile BI.”
“Peek under the covers of an agile BI shop,” Jill continued, “and you’ll often find dozens or even hundreds of repeatable canned BI reports, but nary an advanced analytics capability. You’ll probably discover an IT organization that failed to cultivate solid relationships with business users and is now hiding behind an agile vocabulary to justify its own organizational ADD. It’s lack of accountability, failure to manage a deliberate pipeline, and shifting work priorities packaged up as so much scrum.”
I really love the term Organizational Attention Deficit Disorder, and in spite of myself, I can’t help but render it acronymically as OADD—which should be pronounced as “odd” because the “a” is silent, as in: “Our organization is really quite OADD, isn’t it?”
Returning to the metaphor of the Scrum roles, the pigs are the people with their bacon in the game performing the actual work, and the chickens are the people to whom the results are being delivered. Most commonly, the pigs are IT or the technical team, and the chickens are the users or the business team. But these scrum lines are drawn in the sand, and therefore easily crossed.
Many organizations love the idea of agile BI because they are thinking like chickens and not like pigs. And the agile life is always easier for the chicken because they are only involved, whereas the pig is committed.
OADD organizations often “hurl themselves toward agile BI” because they’re enamored with the theory, but unrealistic about what the practice truly requires. They’re all-in when it comes to the planning, but bacon-less when it comes to the execution.
This is one common way that OADD organizations can get Scrum Screwed Up—they are All Chicken and No Pig.
Closer to the point being made in Jill’s blog post, IT can pretend to be pigs making seemingly impressive progress, but although they’re bringing home the bacon, it lacks any real sizzle because it’s not delivering any real advanced analytics to business users.
Although they appear to be scrumming, IT is really just screwing around with technology, albeit in an agile manner. However, what good is “delivering data at the speed of thought” when that data is neither what the business is thinking, nor truly needs?
This is another common way that OADD organizations can get Scrum Screwed Up—they are All Pig and No Chicken.
Scrum—and any other agile framework—is not a silver bullet. However, agile methodologies can work—and not just for BI.
But whether you want to call it Chicken-Pig Collaboration, or Business-IT Collaboration, or Shiny Happy People Holding Hands, a true enterprise-wide collaboration facilitated by a cross-disciplinary team is necessary for any success—agile or otherwise.
Agile frameworks, when implemented properly, help organizations realistically embrace complexity and avoid oversimplification, by leveraging recurring iterations of relatively short duration that always deliver data-driven solutions to business problems.
Agile frameworks are successful when people take on the challenge united by collaboration, guided by effective methodology, and supported by enabling technology. Agile frameworks allow the enterprise to follow what works, for as long as it works, and without being afraid to adjust as necessary when circumstances inevitably change.
For more information about Agile BI, follow Jill Dyché and TDWI World Conference in San Diego, August 15-20 via Twitter.
Enterprise Data World 2010 was held March 14-18 in San Francisco, California at the Hilton San Francisco Union Square.
Congratulations and thanks to Tony Shaw, Maya Stosskopf, the entire Wilshire Conferences staff, as well as Cathy Nolan and everyone with DAMA International, for their outstanding efforts on delivering yet another wonderful conference experience.
I wish I could have attended every session on the agenda, but this blog post provides some quotes from a few of my favorites.
Conference session by Marty Moseley, CTO of Initiate Systems, an IBM company.
Quotes from the session:
Seven iterative steps of Agile Data Governance:
Conference session by Thomas Ravn, MDM Practice Director at Platon.
Quotes from the session:
Conference session by Tony Fisher, CEO of DataFlux, and Dr. Walid el Abed, CEO of Global Data Excellence.
Quotes from the session:
Conference session by April Reeve, Consultant at EMC Consulting.
Quotes from the session:
Closing Keynote Address by Graeme Simsion, Principal at Simsion & Associates.
Quotes from the keynote:
I presented this session about the art of effectively using social media in business.
An effective social media strategy is essential for organizations as well as individual professionals. Using social media effectively can definitely help promote you, your expertise, your company, and its products and services. However, too many businesses and professionals have a selfish social media strategy. You should not use social media to exclusively promote only yourself or your business.
You need to view social media as Social Karma.
For free related content with no registration required, click on this link: Social Karma
The term “live-tweeting” describes using Twitter to provide near real-time reporting from an event. When a conference schedule has multiple simultaneous sessions, Twitter is great for sharing insights from the sessions you are in with other conference attendees at other sessions, as well as with the on-line community not attending the conference.
Enterprise Data World 2010 had a great group of tweeps (i.e., people using Twitter) and I want to thank all of them, and especially the following Super-Tweeps in particular:
Karen Lopez – @datachick
April Reeve – @Datagrrl
Corinna Martinez – @Futureratti
Eva Smith – @datadeva
Alec Sharp – @alecsharp
Ted Louie – @tedlouie
Rob Drysdale – @projmgr
Loretta Mahon Smith – @silverdata
Official Website for DAMA International
LinkedIn Group for DAMA International
Twitter Account for DAMA International
Facebook Group for DAMA International
Official Website for Enterprise Data World 2010
LinkedIn Group for Enterprise Data World
Twitter Account for Enterprise Data World
Facebook Group for Enterprise Data World
Enterprise Data World 2011 will take place in Chicago, Illinois at the Chicago Sheraton and Towers on April 3-7, 2011.
TDWI World Conference Chicago 2009
An effective social media strategy is essential for organizations as well as individual professionals.
Using social media effectively, including blogging and social networking sites (e.g., Twitter, Facebook, LinkedIn), can definitely help promote you, your expertise, your company, and its products and services.
However, it is sad—but true—that too many people and companies have a selfish social media strategy.
You should not use social media to exclusively promote only yourself or your business.
You need to view social media as Social Karma.
If you can focus your social media and social networking efforts on helping others, then you will get much more back than just a blog reader, a LinkedIn connection, a Facebook friend, a Twitter follower, or even a potential customer.
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.
Please let me do the complex math for you—I still have less than one year of actual experience with social media.
I don't know how you define expertise—and I do acknowledge the inherent difficulty in vetting expertise in such a new and rapidly evolving field—but less than one year of experience with anything does not an expert make, in my humble opinion.
However, I have spent over 15 years in computer science and information technology related disciplines, as a software engineer, consultant, and instructor. I have considerable experience and expertise applying technology in a business context in order to implement solutions for Global 500 companies in a wide variety of industries.
Therefore, I am not a complete moron—but I will leave it to you to determine the actual percentage.
I am currently a full-time writer making all of my income from social media—mainly from blogging and mostly from ghostwriting for corporate blogs.
I am not trying to sell you anything.
I am going to freely share what I have learned so far, including what I have learned from people with far more experience using social media. As I stated previously, I hesitate to call anyone an expert in such a rapidly evolving discipline, but I will mention several resources I have found helpful.
I have absolutely no affiliation or any paid relationship with any person, website, event, product, or book that I recommend.
The primary reason that I am organizing my thoughts about social media involves my preparation for an upcoming conference presentation about using social media effectively for business purposes (more details in the next section).
I am publishing this content as a series on my blog, not only to provide supporting material for the small group of people that actually attend my conference session, but also because I have learned firsthand how the two-way conversation that blogging provides via comments from my readers, greatly improves the quality of my material.
Throughout this series, I will combine traditional blog posts with presentation slides, podcasts, and videos, in order to build a multimedia library of supporting material—all freely available, no registration required.
Enterprise Data World is the business world’s most comprehensive vendor-neutral educational event about data and information management. This year’s program will be bigger than ever before, with more sessions, more case studies, and more can’t-miss content, providing over 200 hours of in-depth tutorials, hands-on workshops, practical sessions and insightful keynotes to take you to the forefront of your industry.
Enterprise Data World 2010 will be held March 14-18 in San Francisco, California at the Hilton San Francisco Union Square.
The full conference agenda can be viewed by clicking on this link: Enterprise Data World 2010 Conference Agenda.
The registration options can be viewed by clicking on this link: Enterprise Data World 2010 Conference Registration.
Use the discount code of EDW10SPKR for a $100 discount off your registration fees. (Discount code expires on February 26.)
On Monday, March 15 from 5:00 PM – 6:00 PM, I will be presenting (30 minutes of material and 30 minutes of Q&A):
Social Karma: The Art of Effectively Using Social Media in Business
In Part 2 of this series: We will discuss leveraging social media for “listening purposes only” as a passive (and safe) way to determine what (if any) type of active involvement with social media makes sense for you and/or your company.
Social Karma (Part 2) – Social Media Preparation
Social Karma (Part 3) – Listening Stations, Home Base, and Outposts
Social Karma (Part 4) – Blogging Best Practices
Social Karma (Part 5) – Connection, Engagement, and ROI Basics
The term “live-tweeting” describes using Twitter to provide near real-time reporting from an event. I live-tweet from the sessions I attend at industry conferences as well as interesting webinars.
Recently, I live-tweeted Successful Data Stewardship Through Data Governance, which was a data governance webinar featuring Marty Moseley of Initiate Systems and Jill Dyché of Baseline Consulting.
Instead of writing a blog post summarizing the webinar, I thought I would list my tweets with brief commentary. My goal is to provide an example of this particular use of Twitter so you can decide its value for yourself.
As the webinar begins, Marty Moseley and Jill Dyché provide some initial thoughts on data governance:
Jill Dyché provides a great list of data governance myths and facts:
Jill Dyché provides some data stewardship insights:
As the webinar ends, Marty Moseley and Jill Dyché provide some closing thoughts about data governance and data quality:
If you attended the webinar, then you know additional material was presented. Did my tweets do the webinar justice? Did you follow along on Twitter during the webinar? If you did not attend the webinar, then are these tweets helpful?
What are your thoughts in general regarding the pros and cons of live-tweeting?
The following three blog posts are conference reports based largely on my live-tweets from the events:
TDWI World Conference Chicago 2009
Data Quality (DQ) Tips is an OCDQ regular segment. Each DQ-Tip is a clear and concise data quality pearl of wisdom.
“In order for your data quality initiative to be successful, you must:
Walk away from the computer and go talk with the people using the data.”
This DQ-Tip came from the TDWI World Conference Chicago 2009 presentation Modern Data Quality Techniques in Action by Gian Di Loreto from Loreto Services and Technologies.
As I blogged about in Data Gazers (borrowing that excellent phrase from Arkady Maydanchik), within cubicles randomly dispersed throughout the sprawling office space of companies large and small, there exist countless unsung heroes of data quality initiatives. Although their job titles might be labeling them as a Business Analyst, Programmer Analyst, Account Specialist or Application Developer, their true vocation is a far more noble calling. They are Data Gazers.
A most bizarre phenomenon (that I have witnessed too many times) is that as a data quality initiative “progresses” it tends to get further and further away from the people who use the data on a daily basis.
Please follow the excellent advice of Gian and Arkady — go talk with your users.
Trust me — everyone on your data quality initiative will be very happy that you did.
DQ-Tip: “Data quality is primarily about context not accuracy...”
DQ-Tip: “Don't pass bad data on to the next person...”
In my April blog post Data Quality Whitepapers are Worthless, I called for data quality whitepapers that are worth reading.
This post will be the first in an ongoing series about data quality whitepapers that I have read and can endorse as worthy.
This is the subtitle of two brief but informative data quality whitepapers freely available (no registration required) from the Electronic Commerce Code Management Association (ECCMA): Transparency and Data Portability.
ECCMA is an international association of industry and government master data managers working together to increase the quality and lower the cost of descriptions of individuals, organizations, goods and services through developing and promoting International Standards for Master Data Quality.
Formed in April 1999, ECCMA has brought together thousands of experts from around the world and provides them a means of working together in the fair, open and extremely fast environment of the Internet to build and maintain the global, open standard dictionaries that are used to unambiguously label information. The existence of these dictionaries of labels allows information to be passed from one computer system to another without losing meaning.
The author of the whitepapers is Peter Benson, the Executive Director and Chief Technical Officer of the ECCMA. Peter is an expert in distributed information systems, content encoding and master data management. He designed one of the very first commercial electronic mail software applications, WordStar Messenger and was granted a landmark British patent in 1992 covering the use of electronic mail systems to maintain distributed databases.
Peter designed and oversaw the development of a number of strategic distributed database management systems used extensively in the UK and US by the Public Relations and Media Industries. From 1994 to 1998, Peter served as the elected chairman of the American National Standards Institute Accredited Committee ANSI ASCX 12E, the Standards Committee responsible for the development and maintenance of EDI standard for product data.
Peter is known for the design, development and global promotion of the UNSPSC as an internationally recognized commodity classification and more recently for the design of the eOTD, an internationally recognized open technical dictionary based on the NATO codification system.
Peter is an expert in the development and maintenance of Master Data Quality as well as an internationally recognized proponent of Open Standards that he believes are critical to protect data assets from the applications used to create and manipulate them.
Peter is the Project Leader for ISO 8000, which is a new international standard for data quality.
ISO 8000 is the international standards for data quality. You can get more information by clicking on this link: ISO 8000
Excerpts from Transparency:
Excerpts from Data Portability:
Founded in 1995, TDWI (The Data Warehousing Institute™) is the premier educational institute for business intelligence and data warehousing that provides education, training, certification, news, and research for executives and information technology professionals worldwide. TDWI conferences always offer a variety of full-day and half-day courses taught in an objective, vendor-neutral manner. The courses taught are designed for professionals and taught by in-the-trenches practitioners who are well known in the industry.
TDWI World Conference Chicago 2009 was held May 3-8 in Chicago, Illinois at the Hyatt Regency Hotel and was a tremendous success. I attended as a Data Quality Journalist for the International Association for Information and Data Quality (IAIDQ).
I used Twitter to provide live reporting from the conference. Here are my notes from the courses I attended:
Jill Dyché, CBIP, is a partner and co-founder of Baseline Consulting, a management and technology consulting firm that provides data integration and business analytics services. Jill is responsible for delivering industry and client advisory services, is a frequent lecturer and writer on the business value of IT, and writes the excellent Inside the Biz blog. She is the author of acclaimed books on the business value of information: e-Data: Turning Data Into Information With Data Warehousing and The CRM Handbook: A Business Guide to Customer Relationship Management. Her latest book, written with Evan Levy, is Customer Data Integration: Reaching a Single Version of the Truth.
Course Quotes from Jill Dyché:
Philip Russom is the Senior Manager of Research and Services at TDWI, where he oversees many of TDWI’s research-oriented publications, services, and events. Prior to joining TDWI in 2005, he was an industry analyst covering BI at Forrester Research, as well as a contributing editor with Intelligent Enterprise and Information Management (formerly DM Review) magazines.
Summit Quotes from Philip Russom:
Becky Briggs, CBIP, CMQ/OE, is a Senior Manager and Data Steward for Airlines Reporting Corporation (ARC) and has 25 years of experience in data processing and IT - the last 9 in data warehousing and BI. She leads the program team responsible for product, project, and quality management, business line performance management, and data governance/stewardship.
Summit Quotes from Becky Briggs:
Barbara Shelby is a Senior Software Engineer for IBM with over 25 years of experience holding positions of technical specialist, consultant, and line management. Her global management and leadership positions encompassed network authentication, authorization application development, corporate business systems data architecture, and database development.
Summit Quotes from Barbara Shelby:
Lee Doss is a Senior IT Architect for IBM with over 25 years of information technology experience. He has a patent for process of aligning strategic capability for business transformation and he has held various positions including strategy, design, development, and customer support for IBM networking software products.
Summit Quotes from Lee Doss:
Kevin Kramer is a Senior Vice President and Director of Enterprise Sales for UMB Bank and is responsible for development of sales strategy, sales tool development, and implementation of enterprise-wide sales initiatives.
Summit Quotes from Kevin Kramer:
Kanon Cozad is a Senior Vice President and Director of Application Development for UMB Bank and is responsible for overall technical architecture strategy and oversees information integration activities.
Summit Quotes from Kanon Cozad:
Jill Dyché, CBIP, is a partner and co-founder of Baseline Consulting, a management and technology consulting firm that provides data integration and business analytics services. (For Jill's complete bio, please see above).
Summit Quotes from Jill Dyché:
Arkady Maydanchik is a co-founder of Data Quality Group, a recognized practitioner, author, and educator in the field of data quality and information integration. Arkady's data quality methodology and breakthrough ARKISTRA technology were used to provide services to numerous organizations. Arkady is the author of the excellent book Data Quality Assessment, a frequent speaker at various conferences and seminars, and a contributor to many journals and online publications. Data quality curriculum by Arkady Maydanchik can be found at eLearningCurve.
Course Quotes from Arkady Maydanchik:
Gian Di Loreto formed Loreto Services and Technologies in 2004 from the client services division of Arkidata Corporation. Loreto Services provides data cleansing and integration consulting services to Fortune 500 companies. Gian is a classically trained scientist - he received his PhD in elementary particle physics from Michigan State University.
Course Quotes from Gian Di Loreto:
TDWI World Conference San Diego 2009 - August 2-7, 2009.
TDWI World Conference Orlando 2009 - November 1-6, 2009.
TDWI World Conference Las Vegas 2010 - February 21-26, 2010.
Obsessive-Compulsive Data Quality (OCDQ) is a blog offering a vendor-neutral perspective on data quality and its related disciplines.
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