Do you believe in Magic (Quadrants)?


If you follow Data Quality on Twitter like I do, then you are probably already well aware that the 2010 Gartner Magic Quadrant for Data Quality Tools was released this week (surprisingly, it did not qualify as a Twitter trending topic).

The five vendors that were selected as the “data quality market leaders” were SAS DataFlux, IBM, Informatica, SAP Business Objects, and Trillium.

Disclosure: I am a former IBM employee, former IBM Information Champion, and I blog for the Data Roundtable, which is sponsored by SAS.

Please let me stress that I have the highest respect for both Ted Friedman and Andy Bitterer, as well as their in depth knowledge of the data quality industry and their insightful analysis of the market for data quality tools.

In this blog post, I simply want to encourage a good-natured debate, and not about the Gartner Magic Quadrant specifically, but rather about market research in general.  Gartner is used as the example because they are perhaps the most well-known and the source most commonly cited by data quality vendors during the sales cycle—and obviously, especially by the “leading vendors.”

I would like to debate how much of an impact market research really has on a prospect’s decision to purchase a data quality tool.

Let’s agree to keep this to a very informal debate about how research can affect both the perception and the reality of the market.

Therefore—for the love of all high quality data everywhere—please, oh please, data quality vendors, do NOT send me your quarterly sales figures, or have your PR firm mercilessly spam either my comments section or my e-mail inbox with all the marketing collateral “proving” how Supercalifragilisticexpialidocious your data quality tool is—I said please, so play nice.


The OCDQ View on OOBE-DQ

In a previous post, I used the term OOBE-DQ to refer to the out-of-box-experience (OOBE) provided by data quality (DQ) tools, which usually becomes a debate between “ease of use” and “powerful functionality” after you ignore the Magic Beans sales pitch that guarantees you the data quality tool is both remarkably easy to use and incredibly powerful.

However, the data quality market continues to evolve away from esoteric technical tools and toward business-empowering suites providing robust functionality with easier to use and role-based interfaces that are tailored to the specific needs of different users, such as business analysts, data stewards, application developers, and system administrators.

The major players are still the large vendors who have innovated (mostly via acquisition and consolidation) enterprise application development platforms with integrated (to varying degrees) components, which provide not only data quality functionality, but also data integration and master data management (MDM) as well.

Many of these vendors also offer service-oriented deployments delivering the same functionality within more loosely coupled technical architectures, which includes leveraging real-time services to prevent (or at least greatly minimize) poor data quality at the multiple points of origin within the data ecosystem.

Many vendors are also beginning to provide better built-in reporting and data visualization capabilities, which is helping to make the correlation between poor data quality and suboptimal business processes more tangible, especially for executive management.

It must be noted that many vendors (including the “market leaders”) continue to struggle with their International OOBE-DQ. 

Many (if not most) data quality tools are strongest in their native country or their native language, but their OOBE-DQ declines significantly when they travel abroad.  Especially outside of the United States, smaller vendors with local linguistic and cultural expertise built into their data quality tools have continued to remain fiercely competitive with the larger vendors.

Market research certainly has a role to play in making a purchasing decision, and perhaps most notably as an aid in comparing and contrasting features and benefits, which of course, always have to be evaluated against your specific requirements, including both your current and future needs. 

Now let’s shift our focus to examining some of the inherent challenges of evaluating market research, perception, and reality.


Confirmation Bias

First of all, I realize that this debate will suffer from a considerable—and completely understandable—confirmation bias.

If you are a customer, employee, or consultant for one of the “High Five” (not an “official” Gartner Magic Quadrant term for the Leaders), then obviously you have a vested interest in getting inebriated on your own Kool-Aid (as noted in my disclosure above, I used to get drunk on the yummy Big Blue Kool-Aid).  Now, this doesn’t mean that you are a “yes man” (or a “yes woman”).  It simply means it is logical for you to claim that market research, market perception, and market reality are in perfect alignment.

Likewise, if you are a customer, employee, or consultant for one of the “It Isn’t Easy Being Niche-y” (rather surprisingly, not an “official” Gartner Magic Quadrant term for the Niche Players), then obviously you have a somewhat vested interest in claiming that market research is from Mars, market perception is from Venus, and market reality is really no better than reality television.

And, if you are a customer, employee, or consultant for one of the “We are on the outside looking in, flipping both Gartner and their Magic Quadrant the bird for excluding us” (I think that you can figure out on your own whether or not that is an “official” Gartner Magic Quadrant term), then obviously you have a vested interest in saying that market research can “Kiss My ASCII!”

My only point is that your opinion of market research will obviously be influenced by what it says about your data quality tool. 

Therefore, should it really surprise anyone when, during the sales cycle, one of the High Five uses the Truly Awesome Syllogism:

“Well, of course, we say our data quality tool is awesome.
However, the Gartner Magic Quadrant also says our data quality tool is awesome.
Therefore, our data quality tool is Truly Awesome.”

Okay, so technically, that’s not even a syllogism—but who said any form of logical argument is ever used during a sales cycle?

On a more serious note, and to stop having too much fun at Gartner’s expense, they do advise against simply selecting vendors in their “Leaders quadrant” and instead always advise to select the vendor that is the better match for your specific requirements.


Features and Benefits: The Game Nobody Wins

As noted earlier, a features and benefits comparison is not only the most common technique used by prospects, but it is also the most common—if not the only—way that the vendors themselves position their so-called “competitive differentiation.”

The problem with this approach—and not just for data quality tools—is that there are far more similarities than differences to be found when comparing features and benefits. 

Practically every single data quality tool on the market today will include functionality for data profiling, data quality assessment, data standardization, data matching, data consolidation, data integration, data enrichment, and data quality monitoring.

Therefore, running down a checklist of features is like playing a game of Buzzword Bingo, or constantly playing Musical Chairs, but without removing any of the chairs in between rounds—in others words, the Features Game almost always ends in a tie.

So then next we play the Benefits Game, which is usually equally pointless because it comes down to silly arguments such as “our data matching engine is better than yours.”  This is the data quality tool vendor equivalent of:

Vendor D: “My Dad can beat up your Dad!”

Vendor Q: “Nah-huh!”

Vendor D: “Yah-huh!”

Vendor Q: “NAH-HUH!”

Vendor D: “YAH-HUH!”

Vendor Q: “NAH-HUH!”

Vendor D: “Yah-huh!  Stamp it!  No Erasies!  Quitsies!”

Vendor Q: “No fair!  You can’t do that!”

After both vendors have returned from their “timeout,” a slightly more mature approach is to run a vendor “bake-off” where the dueling data quality tools participate in a head-to-head competition processing a copy of the same data provided by the prospect. 

However, a bake-off often produces misleading results because the vendors—and not the prospect—perform the competition, making it mostly about vendor expertise, not OOBE-DQ.  Also, the data used rarely exemplifies the prospect’s data challenges.

If competitive differentiation based on features and benefits is a game that nobody wins, then what is the alternative?


The Golden Circle

The Golden Circle

I recently read the book Start with Why by Simon Sinek, which explains that “people don’t buy WHAT you do, they buy WHY you do it.” 

The illustration shows what Simon Sinek calls The Golden Circle.

WHY is your purpose—your driving motivation for action. 

HOW is your principles—specific actions that are taken to realize your Why. 

WHAT is your results—tangible ways in which you bring your Why to life. 

It’s a circle when viewed from above, but in reality it forms a megaphone for broadcasting your message to the marketplace. 

When you rely only on the approach of attempting to differentiate your data quality tool by discussing its features and benefits, you are focusing on only your WHAT, and absent your WHY and HOW, you sound just like everyone else to the marketplace.

When, as is often the case, nobody wins the Features and Benefits Game, a data quality tool sounds more like a commodity, which will focus the marketplace’s attention on aspects such as your price—and not on aspects such as your value.

Due to the considerable length of this blog post, I have been forced to greatly oversimplify the message of this book, which a future blog post will discuss in more detail.  I highly recommend the book (and no, I am not an affiliate).

At the very least, consider this question:

If there truly was one data quality tool on the market today that, without question, had the very best features and benefits, then why wouldn’t everyone simply buy that one? 

Of course your data quality tool has solid features and benefits—just like every other data quality tool does.

I believe that the hardest thing for our industry to accept is—the best technology hardly ever wins the sale. 

As most of the best salespeople will tell you, what wins the sale is when a relationship is formed between vendor and customer, a strategic partnership built upon a solid foundation of rapport, respect, and trust.

And that has more to do with WHY you would make a great partner—and less to do with WHAT your data quality tool does.


Do you believe in Magic (Quadrants)?

I Want To Believe

How much of an impact do you think market research has on the purchasing decision of a data quality tool?  How much do you think research affects both the perception and the reality of the data quality tool market?  How much do you think the features and benefits of a data quality tool affect the purchasing decision?

All perspectives on this debate are welcome without bias.  Therefore, please post a comment below.


Comments advertising your products and services (or bashing competitors) will not be approved.



Enterprise Data World 2010

Enterprise Data World 2010

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.


Applying Agile Software Engineering Principles to Data Governance

Conference session by Marty Moseley, CTO of Initiate Systems, an IBM company.

Quotes from the session:

  • “Data governance is 80% people and only 20% technology”
  • “Data governance is an ongoing, evolutionary practice”
  • “There are some organizational problems that are directly caused by poor data quality”
  • “Build iterative 'good enough' solutions – not 'solve world hunger' efforts”
  • “Traditional approaches to data governance try to 'boil the ocean' and solve every data problem”
  • “Agile approaches to data governance laser focus on iteratively solving one problem at a time”
  • “Quality is everything, don't sacrifice accuracy for performance, you can definitely have both”

Seven iterative steps of Agile Data Governance:

  1. “Form the Data Governance Board – Small guidance team of executives who can think cross-organizationally”
  2. “Define the Problem and the Team – Root cause analysis, build the business case, appoint necessary resources”
  3. “Nail Down Size and Scope – Prioritize the scope in order to implement the current iteration in less than 9 months”
  4. “Validate Your Assumptions – Challenge all estimates, perform data profiling, list data quality issues to resolve”
  5. “Establishing Data Policies – Measurable statements of 'what must be achieved' for which kinds of data”
  6. “Implement the data quality solution for the current iteration”
  7. “Evaluate the overall progress and plan for the next iteration”


Monitor the Quality of your Master Data

Conference session by Thomas Ravn, MDM Practice Director at Platon.

Quotes from the session:

  • “Ensure master data is taken into account each and every time a business process or IT system is changed”
  • “Web forms requiring master data attributes can NOT be based on a single country's specific standards”
  • “There is no point in monitoring data quality if no one within the business feels responsible for it”
  • “The greater the business impact of a data quality dimension, the more difficult it is to measure”
  • “Data quality key performance indicators (KPI) should be tied directly to business processes”
  • “Implement a data input validation rule rather than allow bad data to be entered”
  • “Sometimes the business logic is too ambiguous to be enforced by a single data input validation rule”
  • “Data is not always clean or dirty in itself – it depends on the viewpoint or defined standard”
  • “Data quality is in the eye of the beholder”


Measuring the Business Impact of Data Governance

Conference session by Tony Fisher, CEO of DataFlux, and Dr. Walid el Abed, CEO of Global Data Excellence.

Quotes from the session:

  • “The goal of data governance is to position the business to improve”
  • “Revenue optimization, cost control, and risk mitigation are the business drivers of data management”
  • “You don't manage data to manage data, you manage data to improve your business”
  • “Business rules are rules that data should comply with in order to have the process execute properly”
  • “For every business rule, define the main impact (cost of failure) and the business value (result of success)”
  • “Power Shift – Before: Having information is power – Now: Sharing information is power”
  • “You must translate technical details into business language, such as cost, revenue, risk”
  • “Combine near-term fast to value with long-term alignment with business strategy”
  • “Data excellence must be a business value added driven program”
  • “Communication is key to data excellence, make it visible and understood by all levels of the organization”


The Effect of the Financial Meltdown on Data Management

Conference session by April Reeve, Consultant at EMC Consulting.

Quotes from the session:

  • “The recent financial crisis has greatly increased the interest in both data governance and data transparency”
  • “Data Governance is a symbiotic relationship of Business Governance and Technology Governance”
  • “Risk management is a data problem in the forefront of corporate concern – now viewing data as a corporate asset”
  • “Data transparency increases the criticality of data quality – especially regarding the accuracy of financial reporting”


What the Business Wants

Closing Keynote Address by Graeme Simsion, Principal at Simsion & Associates.

Quotes from the keynote:

  • “You can get a lot done if you don't care who gets the credit”
  • “People will work incredibly hard to implement their own ideas”
  • “What if we trust the business to know what's best for the business?”
  • “Let's tell the business what we (as data professionals) do – and then ask the business what they want”


Social Karma

My Badge for Enterprise Data World 2010

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


Live-Tweeting at Enterprise Data World 2010

Twitter at Enterprise Data World 2010

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 


Additional Resources

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.


Related Posts

Enterprise Data World 2009

TDWI World Conference Chicago 2009

DataFlux IDEAS 2009

TDWI World Conference Chicago 2009

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: 


BI from Both Sides: Aligning Business and IT

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:

  • Five Critical Success Factors for Business Intelligence (BI):
    1. Organization - Build organizational structures and skills to foster a sustainable program
    2. Processes - Align both business and IT development processes that facilitate delivery of ongoing business value
    3. Technology - Select and build technologies that deploy information cost-effectively
    4. Strategy - Align information solutions to the company's strategic goals and objectives
    5. Information - Treat data as an asset by separating data management from technology implementation
  • Three Different Requirement Categories:
    1. What is the business need, pain, or problem?  What business questions do we need to answer?
    2. What data is necessary to answer those business questions?
    3. How do we need to use the resulting information to answer those business questions?
  • “Data warehouses are used to make business decisions based on data – so data quality is critical”
  • “Even companies with mature enterprise data warehouses still have data silos - each business area has its own data mart”
  • “Instead of pushing a business intelligence tool, just try to get people to start using data”
  • “Deliver a usable system that is valuable to the business and not just a big box full of data”


TDWI Data Governance Summit

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:

  • “Data Governance usually boils down to some form of control for data and its usage”
  • “Four Ps of Data Governance: People, Policies, Procedures, Process”
  • “Three Pillars of Data Governance: Compliance, Business Transformation, Business Integration”
  • “Two Foundations of Data Governance: Business Initiatives and Data Management Practices”
  • “Cross-functional collaboration is a requirement for successful Data Governance”


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:

  • “Data Governance is the act of managing the organization's data assets in a way that promotes business value, integrity, usability, security and consistency across the company”
  • Five Steps of Data Governance:
    1. Determine what data is required
    2. Evaluate potential data sources (internal and external)
    3. Perform data profiling and analysis on data sources
    4. Data Services - Definition, modeling, mapping, quality, integration, monitoring
    5. Data Stewardship - Classification, access requirements, archiving guidelines
  • “You must realize and accept that Data Governance is a program and not just a project”


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:

  • Four Common Barriers to Data Governance:
    1. Information - Existence of information silos and inconsistent data meanings
    2. Organization - Lack of end-to-end data ownership and organization cultural challenges
    3. Skill - Difficulty shifting resources from operational to transformational initiatives
    4. Technology - Business data locked in large applications and slow deployment of new technology
  • Four Key Decision Making Bodies for Data Governance:
    1. Enterprise Integration Team - Oversees the execution of CIO funded cross enterprise initiatives
    2. Integrated Enterprise Assessment - Responsible for the success of transformational initiatives
    3. Integrated Portfolio Management Team - Responsible for making ongoing business investment decisions
    4. Unit Architecture Review - Responsible for the IT architecture compliance of business unit solutions


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:

  • Five Data Governance Best Practices:
    1. Create a sense of urgency that the organization can rally around
    2. Start small, grow fast...pick a few visible areas to set an example
    3. Sunset legacy systems (application, data, tools) as new ones are deployed
    4. Recognize the importance of organization culture…this will make or break you
    5. Always, always, always – Listen to your customers


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:

  • “Without Data Governance, multiple sources of customer information can produce multiple versions of the truth”
  • “Data Governance helps break down organizational silos and shares customer data as an enterprise asset”
  • “Data Governance provides a roadmap that translates into best practices throughout the entire enterprise”


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:

  • “Data Governance identifies business process priorities and then translates them into enabling technology”
  • “Data Governance provides direction and Data Stewardship puts direction into action”
  • “Data Stewardship identifies and prioritizes applications and data for consolidation and improvement”


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:

  • “The hard part of Data Governance is the data
  • “No data will be formally sanctioned unless it meets a business need”
  • “Data Governance focuses on policies and strategic alignment”
  • “Data Management focuses on translating defined polices into executable actions”
  • “Entrench Data Governance in the development environment”
  • “Everything is customer data – even product and financial data”


Data Quality Assessment - Practical Skills

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:

  • “Nothing is worse for data quality than desperately trying to fix it during the last few weeks of an ETL project”
  • “Quality of data after conversion is in direct correlation with the amount of knowledge about actual data”
  • “Data profiling tools do not do data profiling - it is done by data analysts using data profiling tools”
  • “Data Profiling does not answer any questions - it helps us ask meaningful questions”
  • “Data quality is measured by its fitness to the purpose of use – it's essential to understand how data is used”
  • “When data has multiple uses, there must be data quality rules for each specific use”
  • “Effective root cause analysis requires not stopping after the answer to your first question - Keep asking: Why?”
  • “The central product of a Data Quality Assessment is the Data Quality Scorecard”
  • “Data quality scores must be both meaningful to a specific data use and be actionable”
  • “Data quality scores must estimate both the cost of bad data and the ROI of data quality initiatives”


Modern Data Quality Techniques in Action - A Demonstration Using Human Resources Data

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:

  • “Data Quality is rich with theory and concepts – however it is not an academic exercise, it has real business impact”
  • “To do data quality well, you must walk away from the computer and go talk with the people using the data”
  • “Undertaking a data quality initiative demands developing a deeper knowledge of the data and the business”
  • “Some essential data quality rules are ‘hidden’ and can only be discovered by ‘clicking around’ in the data”
  • “Data quality projects are not about systems working together - they are about people working together”
  • “Sometimes, data quality can be ‘good enough’ for source systems but not when integrated with other systems”
  • “Unfortunately, no one seems to care about bad data until they have it”
  • “Data quality projects are only successful when you understand the problem before trying to solve it”


Mark Your Calendar

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.

Enterprise Data World 2009

Formerly known as the DAMA International Symposium and Wilshire MetaData Conference, Enterprise Data World 2009 was held April 5-9 in Tampa, Florida at the Tampa Convention Center.


Enterprise Data World is the business world’s most comprehensive vendor-neutral educational event about data and information management.  This year’s program was bigger than ever before, with more sessions, more case studies, and more can’t-miss content.  With 200 hours of in-depth tutorials, hands-on workshops, practical sessions and insightful keynotes, the conference was a tremendous success.  Congratulations and thanks to Tony Shaw, Maya Stosskopf and the entire Wilshire staff.


I attended Enterprise Data World 2009 as a member of the Iowa Chapter of DAMA and as a Data Quality Journalist for the International Association for Information and Data Quality (IAIDQ).

I used Twitter to provide live reporting from the sessions that I was attending.

I wish that I could have attended every session, but here are some highlights from ten of my favorites:


8 Ways Data is Changing Everything

Keynote by Stephen Baker from BusinessWeek

His article Math Will Rock Your World inspired his excellent book The Numerati.  Additionally, check out his blog: Blogspotting.

Quotes from the keynote:

  • "Data is changing how we understand ourselves and how we understand our world"
  • "Predictive data mining is about the mathematical modeling of humanity"
  • "Anthropologists are looking at social networking (e.g. Twitter, Facebook) to understand the science of friendship"


Master Data Management: Proven Architectures, Products and Best Practices

Tutorial by David Loshin from Knowledge Integrity.

Included material from his excellent book Master Data Management.  Additionally, check out his blog: David Loshin.

Quotes from the tutorial:

  • "Master Data are the core business objects used in the different applications across the organization, along with their associated metadata, attributes, definitions, roles, connections and taxonomies"
  • "Master Data Management (MDM) provides a unified view of core data subject areas (e.g. Customers, Products)"
  • "With MDM, it is important not to over-invest and under-implement - invest in and implement only what you need"


Master Data Management: Ignore the Hype and Keep the Focus on Data

Case Study by Tony Fisher from DataFlux and Jeff Grayson from Equinox Fitness.

Quotes from the case study:

  • "The most important thing about Master Data Management (MDM) is improving business processes"
  • "80% of any enterprise implementation should be the testing phase"
  • "MDM Data Quality (DQ) Challenge: Any % wrong means you’re 100% certain you’re not always right"
  • "MDM DQ Solution: Re-design applications to ensure the ‘front-door’ protects data quality"
  • "Technology is critical, however thinking through the operational processes is more important"


A Case of Usage: Working with Use Cases on Data-Centric Projects

Case Study by Susan Burk from IBM.

Quotes from the case study:

  • "Use Case is a sequence of actions performed to yield a result of observable business value"
  • "The primary focus of data-centric projects is data structure, data delivery and data quality"
  • "Don’t like use cases? – ok, call them business acceptance criteria – because that’s what a use case is"


Crowdsourcing: People are Smart, When Computers are Not

Session by Sharon Chiarella from Amazon Web Services.

Quotes from the session:

  • "Crowdsourcing is outsourcing a task typically performed by employees to a general community of people"
  • "Crowdsourcing eliminates over-staffing, lowers costs and reduces work turnaround time"
  • "An excellent example of crowdsourcing is open source software development (e.g. Linux)"


Improving Information Quality using Lean Six Sigma Methodology

Session by Atul Borkar and Guillermo Rueda from Intel.

Quotes from the session:

  • "Information Quality requires a structured methodology in order to be successful"
  • Lean Six Sigma Framework: DMAIC – Define, Measure, Analyze, Improve, Control:
    • Define = Describe the challenge, goal, process and customer requirements
    • Measure = Gather data about the challenge and the process
    • Analyze = Use hypothesis and data to find root causes
    • Improve = Develop, implement and refine solutions
    • Control = Plan for stability and measurement


Universal Data Quality: The Key to Deriving Business Value from Corporate Data

Session by Stefanos Damianakis from Netrics.

Quotes from the session:

  • "The information stored in databases is NEVER perfect, consistent and complete – and it never can be!"
  • "Gartner reports that 25% of critical data within large businesses is somehow inaccurate or incomplete"
  • "Gartner reports that 50% of implementations fail due to lack of attention to data quality issues"
  • "A powerful approach to data matching is the mathematical modeling of human decision making"
  • "The greatest advantage of mathematical modeling is that there are no data matching rules to build and maintain"


Defining a Balanced Scorecard for Data Management

Seminar by C. Lwanga Yonke, a founding member of the International Association for Information and Data Quality (IAIDQ).

Quotes from the seminar:

  • "Entering the same data multiple times is like paying the same invoice multiple times"
  • "Good metrics help start conversations and turn strategy into action"
  • Good metrics have the following characteristics:
    • Business Relevance
    • Clarity of Definition
    • Trending Capability (i.e. metric can be tracked over time)
    • Easy to aggregate and roll-up to a summary
    • Easy to drill-down to the details that comprised the measurement


Closing Panel: Data Management’s Next Big Thing!

Quotes from Panelist Peter Aiken from Data Blueprint:

  • Capability Maturity Levels:
    1. Initial
    2. Repeatable
    3. Defined
    4. Managed
    5. Optimized
  • "Most companies are at a capability maturity level of (1) Initial or (2) Repeatable"
  • "Data should be treated as a durable asset"

Quotes from Panelist Noreen Kendle from Burton Group:

  • "A new age for data and data management is on horizon – a perfect storm is coming"
  • "The perfect storm is being caused by massive data growth and software as a service (i.e. cloud computing)"
  • "Always remember that you can make lemonade from lemons – the bad in life can be turned into something good"

Quotes from Panelist Karen Lopez from InfoAdvisors:

  • "If you keep using the same recipe, then you keep getting the same results"
  • "Our biggest problem is not technical in nature - we simply need to share our knowledge"
  • "Don’t be a dinosaur! Adopt a ‘go with what is’ philosophy and embrace the future!"

Quotes from Panelist Eric Miller from Zepheira:

  • "Applications should not be ON The Web, but OF The Web"
  • "New Acronym: LED – Linked Enterprise Data"
  • "Semantic Web is the HTML of DATA"

Quotes from Panelist Daniel Moody from University of Twente:

  • "Unified Modeling Language (UML) was the last big thing in software engineering"
  • "The next big thing will be ArchiMate, which is a unified language for enterprise architecture modeling"


Mark Your Calendar

Enterprise Data World 2010 will take place in San Francisco, California at the Hilton San Francisco on March 14-18, 2010.