Data governance requires the coordination of a complex combination of a myriad of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data quality remediation, data stewardship, business process optimization, technology enablement, and, perhaps most notably, policy enforcement.
In their book Practical Wisdom, Barry Schwartz and Kenneth Sharpe use the Nicomachean Ethics of Aristotle as a guide to explain that although rules are important, what is more important is “knowing the proper thing to aim at in any practice, wanting to aim at it, having the skill to figure out how to achieve it in a particular context, and then doing it.”
Aristotle observed the practical wisdom of the craftsmen of his day, including carpenters, shoemakers, blacksmiths, and masons, noting how “their work was not governed by systematically applying rules or following rigid procedures. The materials they worked with were too irregular, and each task posed new problems.”
“Aristotle was particularly fascinated with how masons used rulers. A normal straight-edge ruler was of little use to the masons who were carving round columns from slabs of stone and needed to measure the circumference of the columns.”
Unless you bend the ruler.
“Which is exactly what the masons did. They fashioned a flexible ruler out of lead, a forerunner of today’s tape measure. For Aristotle, knowing how to bend the rule to fit the circumstance was exactly what practical wisdom was all about.”
Although there’s a tendency to ignore the existing practical wisdom of the organization, successful data governance is not about systematically applying rules or following rigid procedures, and precisely because the dynamic challenges faced, and overcome daily, by business analysts, data stewards, technical architects, and others, exemplify today’s constantly changing business world.
But this doesn’t mean that effective data governance policies can’t be implemented. It simply means that instead of focusing on who should lead the way (i.e., top-down or bottom-up), we should focus on what the rules of data governance are made of.
Well-constructed data governance policies are like lead rulers—flexible rules that empower us with an understanding of the principle of the policy, and trust us to figure out how best to enforce the policy in a particular context, how to bend the rule to fit the circumstance. Aristotle knew this was exactly what practical wisdom was all about—data governance needs practical wisdom.
“Tighter rules and regulations, however necessary, are pale substitutes for wisdom,” concluded Schwartz and Sharpe. “We need rules to protect us from disaster. But at the same time, rules without wisdom are blind and at best guarantee mediocrity.”
A previous post examined the data aspects of enterprise security, which requires addressing both outside-in and inside-out risks.
Most organizations tend to both overemphasize and oversimplify outside-in data security using a perimeter fence model, which, as Doug Newdick commented, “implicitly treats all of your information system assets as equivalent from a security and risk perspective, when that is clearly not true.” Different security levels are necessary for different assets, and therefore a security zone model makes more sense, where you focus more on securing specific data or applications, and less on securing the perimeter.
“I think that these sorts of models will become more prevalent,” Newdick concluded, “as we face the proliferation of different devices and platforms in the enterprise, and the sort of Bring Your Own Device approaches that many organizations are examining. If you don’t own or manage your perimeter, securing the data or application itself becomes more important.”
Although there’s also a growing recognition that inside-out data security needs to be improved, “it’s critical that organizations recognize the internal threat can’t be solved solely via policy and process,” commented Richard Jarvis, who recommended an increase in the internal use of two-factor authentication, as well as the physical separation of storage so highly confidential data is more tightly restricted within a dedicated hardware infrastructure.
As Rafal Los recently blogged, the costs of cyber crime continue to rise. Although the fear of a cloud security breach is the most commonly expressed concern, Judy Redman recently blogged about how cyber crime doesn’t only happen in the cloud. With the growing prevalence of smart phones, tablet PCs, and other mobile devices, data security in our hyper-connected world requires, as John Dodge recently blogged, that organizations also institute best practices for mobile device security.
Cloud, social, and mobile technologies “make business and our life more enriched,” commented Pearl Zhu, “but on the other hand, this open environment makes the business environment more vulnerable from the security perspective.” In other words, this open environment, which some have described as a multi-dimensional attack space, is good for business, but bad for security.
Most organizations already spend a fistful of dollars on enterprise security, but they may need to budget for a few dollars more because the digital age is about the good, the bad, and the secure. In other words, we have to take the good with the bad in the more open business environment enabled by cloud, mobile, and social technologies, which requires a modern data security model that can protect us from the bad without being overprotective to the point of inhibiting the good.
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
On this episode, I celebrate the autumnal equinox by falling back to look at the Best of OCDQ Radio, including discussions about Data, Information, Business-IT Collaboration, Change Management, Big Analytics, Data Governance, and the Data Revolution.
Thank you for listening to OCDQ Radio. Your listenership is deeply appreciated.
Special thanks to all OCDQ Radio guests. If you missed any of their great appearances, check out the full episode list below.
Popular OCDQ Radio Episodes
Clicking on the link will take you to the episode’s blog post:
Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”
Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.
The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.
Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
On this episode, Daragh O Brien and I discuss the Blue Box of Information Quality, which is much bigger on the inside, as well as using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”
Daragh O Brien is one of Ireland’s leading Information Quality and Governance practitioners. After being born at a young age, Daragh has amassed a wealth of experience in quality information driven business change, from CRM Single View of Customer to Regulatory Compliance, to Governance and the taming of information assets to benefit the bottom line, manage risk, and ensure customer satisfaction. Daragh O Brien is the Managing Director of Castlebridge Associates, one of Ireland’s leading consulting and training companies in the information quality and information governance space.
Daragh O Brien is a founding member and former Director of Publicity for the IAIDQ, which he is still actively involved with. He was a member of the team that helped develop the Information Quality Certified Professional (IQCP) certification and he recently became the first person in Ireland to achieve this prestigious certification.
In 2008, Daragh O Brien was awarded a Fellowship of the Irish Computer Society for his work in developing and promoting standards of professionalism in Information Management and Governance.
Daragh O Brien is a regular conference presenter, trainer, blogger, and author with two industry reports published by Ark Group, the most recent of which is The Data Strategy and Governance Toolkit.
Popular OCDQ Radio Episodes
Clicking on the link will take you to the episode’s blog post:
Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.
The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.
Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.
Although its cyber-security plot oversimplifies some technology aspects of data encryption, the Dan Brown novel Digital Fortress is an enjoyable read. The digital fortress of the novel was a computer program thought capable of creating an unbreakable data encryption algorithm, but it’s later discovered the program is capable of infiltrating and dismantling any data security protocol.
The data aspects of enterprise security are becoming increasingly prevalent topics of discussion within many organizations, which are pondering how secure their digital fortress actually is. In other words, whether or not their data assets are truly secure.
Most organizations focus almost exclusively on preventing external security threats, using a data security model similar to building security, where security guards make sure that only people with valid security badges are allowed to enter the building. However, once you get past the security desk, you have mostly unrestricted access to all areas inside the building.
As Bryan Casey recently blogged, the data security equivalent is referred to as “Tootsie Pop security,” the practice of having a hard, crunchy, security exterior, but with a soft security interior. In other words, once you enter a valid user name and password, or as a hacker you obtain or create one, you have mostly unrestricted access to all databases inside the organization.
Although hacking is a real concern, this external focus could cause companies to turn a blind eye to internal security threats.
“I think the real risk is not the outside threat in,” explained Joseph Spagnoletti, “it’s more the inside threat out.” As more data is available to more people within the organization, and with more ways to disseminate data more quickly, data security risks can be inadvertently created when sharing data outside of the organization, perhaps in the name of customer service or marketing.
A commonly cited additional example of an inside-out threat is cloud security, especially the use of public or community clouds for collaboration and social networking. The cloud complicates data security in the sense that not all of the organization’s data is stored within its physical fortresses of buildings and on-premises computer hardware and software.
However, it must be noted that mobility is likely an even greater inside-out data security threat than cloud computing. Laptops have long been the primary antagonist in the off-premises data security story, but with the growing prevalence of smart phones, tablet PCs, and other mobile devices, the digital fortress is now constantly in motion, a moving target in a hyper-connected world.
So how do organizations institute effective data security protocols in the digital age? Can the digital fortress truly be secured?
“The key to data security, and really all security,” Bryan Casey concluded, “is the ability to affect outcomes. It’s not enough to know what’s happening, or even what’s happening right now. You need to know what’s happening right now and what actions you can take to protect yourself and your organization.”
What actions are you taking to protect yourself and your organization? How are you securing your digital fortress?
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
On this episode, Julie Hunt and I discuss the intersection of data quality and business intelligence, especially the strategy of good-enough data for fast-enough decisions, a necessity for surviving and thriving in the constantly changing business world.
Julie Hunt is an accomplished software industry analyst and business technology strategist, providing market and competitive insights for software vendors. Julie Hunt has the unique perspective of a hybrid, which means she has extensive experience in the technology, business, and customer/people-oriented aspects of creating, marketing and selling software. Working in the B2B software industry for more than 25 years, she has hands-on experience for multiple solution spaces including data integration, business intelligence, analytics, content management, and collaboration. She is also a member of the Boulder BI Brain Trust.
Julie Hunt regularly shares her insights about the software industry on Twitter as well as via her highly recommended blog.
Popular OCDQ Radio Episodes
Clicking on the link will take you to the episode’s blog post:
Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”
Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.
Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.
The relationship between data and information, and by extension data quality and information quality, is acknowledged and explored in the book’s second chapter, which includes a brief history of information theory, as well as the origins of many of the phrases frequently used throughout the data/information quality industry, e.g., fitness for use and information product.
Talburt explains that the problem with the fitness-for-use definition for the quality of an information product (IP) is that it “assumes that the expectations of an IP user and the value produced by the IP in its application are both well understood.”
Different users often have different applications for data and information, requiring possibly different versions of the IP, each with a different relative value to the user. This is why Talburt believes that the quality of information is best defined, not as fitness for use, but instead as the degree to which the information creates value for a user in a particular application. This allows us to measure the business-driven value of information quality with technology-enabled metrics, which are truly relevant to users.
Talburt believes that casting information quality in terms of business value is essential to gaining management’s endorsement of information quality practices within an organizaiton, and Talburt recommends three keys to success with information quality:
Always relate information quality to business value
Give stakeholders a way to talk about information quality—the vocabulary and concepts
Show them a way to get started on improving information quality—and a vision for sustaining it
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
On this episode, Gordon Hamilton and I discuss data quality key concepts, including those which we have studied in some of our favorite data quality books, and more important, those which we have implemented in our careers as data quality practitioners.
Gordon Hamilton is a Data Quality and Data Warehouse professional, whose 30 years’ experience in the information business encompasses many industries, including government, legal, healthcare, insurance and financial. Gordon was most recently engaged in the healthcare industry in British Columbia, Canada, where he continues to advise several health care authorities on data quality and business intelligence platform issues.
Gordon Hamilton’s passion is to bring together:
Exposure of business rules through data profiling as recommended by Ralph Kimball.
Monitoring business rules in the EQTL (Extract-Quality-Transform-Load) pipeline leading into the data warehouse.
Managing the business rule violations through systemic and specific solutions within the statistical process control framework of Shewhart/Deming.
Researching how to sustain data quality metrics as the “fit for purpose” definitions change faster than the information product process can easily adapt.
Gordon Hamilton’s moniker of DQStudent on Twitter hints at his plan to dovetail his Lean Six Sigma skills and experience with the data quality foundations to improve the manufacture of the “information product” in today’s organizations. Gordon is a member of IAIDQ, TDWI, and ASQ, as well as an enthusiastic reader of anything pertaining to data.
By no means a comprehensive list, and listed in no particular order whatsoever, the following books were either discussed during this OCDQ Radio episode, or are otherwise recommended for anyone looking to study data quality and its related disciplines:
Clicking on the link will take you to the episode’s blog post:
Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”
Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.
The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.
One of the many things I love about Twitter is its ability to spark ideas via real-time conversations. For example, while live-tweeting during last week’s episode of DM Radio, the topic of which was how to get started with data governance, I tweeted about the data silo challenges and corporate cultural obstacles being discussed.
I tweeted that data is an asset only if it is a shared asset, across the silos, across the corporate culture, and that, in order to be successful with data governance, organizations must replace the mantra “my private knowledge is my power” with “our shared knowledge empowers us all.”
“That’s very socialist thinking,” Mark Madsen responded. “Soon we’ll be having arguments about capitalizing over socializing our data.”
To which I responded that the more socialized data is, the more capitalized data can become . . . just ask Google.
“Oh no,” Mark humorously replied, “decades of political rhetoric about socialism to be ruined by a discussion of data!” And I quipped that discussions about data have been accused of worse, and decades of data rhetoric certainly hasn’t proven very helpful in corporate politics.
Later, while ruminating on this light-hearted exchange, I wondered if we actually are in the midst of the Data Cold War.
The Data Cold War
The Cold War, which lasted approximately from 1946 to 1991, was the political, military, and economic competition between the Communist World, primarily the former Soviet Union, and the Western world, primarily the United States. One of the major tenets of the Cold War was the conflicting ideologies of socialism and capitalism.
In enterprise data management, one of the most debated ideologies is whether or not data should be viewed as a corporate asset, especially by the for-profit corporations of capitalism, which is (even before the Cold War began), and will likely forever remain, the world’s dominant economic model.
My earlier remark that data is an asset only if it is a shared asset, across the silos, across the corporate culture, is indicative of the bounded socialist view of enterprise data. In other words, almost no one in the enterprise data management space is suggesting that data should be shared beyond the boundary of the organization. In this sense, advocates, including myself, of data governance are advocating socializing data within the enterprise so that data can be better capitalized as a true corporate asset.
This mindset makes sense because sharing data with the world, especially for free, couldn’t possibly be profitable — or could it?
By encouraging their users to freely share their own personal data, Google and Facebook ingeniously answer what David Loshin calls the most dangerous question in data management: What is the definition of customer?
How do Google and Facebook answer the most dangerous question?
A customer is a product.
This is the first step that begins what I call the Master Data Management Magic Trick.
Instead of trying to manage the troublesome master data domain of customer and link it, through sales transaction data, to the master data domain of product (products, by the way, have always been undeniably accepted as a corporate asset even though product data has not been), Google and Facebook simply eliminate the need for customers (and, by extension, eliminate the need for customer service because, since their product is free, it has no customers) by transforming what would otherwise be customers into the very product that they sell — and, in fact, the only “real” product that they have.
And since what their users perceive as their product is virtual (i.e., entirely Internet-based), it’s not really a product, but instead a free service, which can be discontinued at any time. And if it was, who would you complain to? And on what basis?
After all, you never paid for anything.
This is the second step that completes the Master Data Management Magic Trick — a product is a free service.
Therefore, Google and Facebook magically make both their customers and their products (i.e., master data) disappear, while simultaneously making billions of dollars (i.e., transaction data) appear in their corporate bank accounts.
(Yes, the personal data of their users is master data. However, because it is used in an anonymized and aggregated format, it is not, nor does it need to be, managed like the master data we talk about in the enterprise data management industry.)
Google and Facebook have Capitalized Socialism
By “empowering” us with free services, Google and Facebook use the power of our own personal data against us — by selling it.
However, it’s important to note that they indirectly sell our personal data as anonymized and aggregated demographic data.
Although they do not directly sell our individually identifiable information (because, truthfully, it has very limited, and mostly no legal, value, i.e., that would be identity theft), Google and Facebook do occasionally get sued (mostly outside the United States) for violating data privacy and data protection laws.
However, it’s precisely because we freely give our personal data to them, that until, or if, laws are changed to protect us from ourselves, it’s almost impossible to prove they are doing anything illegal (again, their undeniable genius is arguably evil genius).
Google and Facebook are the exact same kind of company — they are both Internet advertising agencies.
They both sell online advertising space to other companies, which are looking to demographically target prospective customers because those companies actually do view people as potential real customers for their own real products.
The irony is that if all of their users stopped using their free service, then not only would our personal data be more private and more secure, but the new revenue streams of Google and Facebook would eventually dry up because, specifically by design, they have neither real customers nor real products. More precisely, their only real customers (other companies) would stop buying advertising from them because no one would ever see and (albeit, even now, only occasionally) click on their ads.
Essentially, companies like Google and Facebook are winning the Data Cold War because they have capitalized socialism.
In other words, the bottom line is Google and Facebook have socialized data in order to capitalize data as a true corporate asset.
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
DAMA International is a non-profit, vendor-independent, global association of technical and business professionals dedicated to advancing the concepts and practices of information and data management.
Loretta Mahon Smith is a visionary and influential data management professional known for her consistent awareness of trends in the forefront of the industry. Since 1983, she has worked in international financial services, and been actively involved in the maturity and growth of Information Architecture functions, specializing in Data Stewardship and Data Strategy Development.
Loretta Mahon Smith has been a member of DAMA for more than 10 years, with a lifetime membership to the DAMA National Capitol Region Chapter. As President of the chapter she has the opportunity to help the Washington DC and Baltimore data management communities. She serves the world community by her involvement on the DAMA International Board as VP of Communications. She additionally volunteers her time to work on the ICCP Certification Council, most recently working on the development of the Zachman and Data Governance examinations.
In the past, Loretta has facilitated Special Interest Group sessions on Governance and Stewardship and presented Stewardship training at numerous local chapters for DAMA, IIBA, TDWI, and ACM, as well as major conferences including Project World (IIBA), INFO360 (AIIM), EDW (DAMA) and the IQ. She earned Certified Computing Professional (CCP), Certified Business Intelligence Professional (CBIP), and Certified Data Management Professional (CDMP) designations, achieving mastery level proficiency rating in Data Warehousing, Data Management, and Data Quality.
Popular OCDQ Radio Episodes
Clicking on the link will take you to the episode’s blog post:
Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”
Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.
The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.
Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.
Julie Hunt recently blogged about how “line-of-business (LOB) groups have been turning to cloud-based services to quickly set up technology solutions that support their business needs and objectives,” which is especially true when “IT teams are already carrying heavy workloads with ever-shrinking staffing levels, and frequently don’t have the resources to immediately respond to time-sensitive LOB needs.”
As I have previously blogged, speed and agility are the most common business drivers for implementing new technology, and the consumer technologies of cloud computing and software-as-a-service (SaaS) enable business users to directly purchase solutions.
When on-premises IT teams cannot solve their time-sensitive business problems, organizations use off-premises cloud providers, which are essentially the Bounty Hunters of IT.
The Bounty Hunters of IT
In The Empire Strikes Back, frustrated by the inability of on-premises IT teams (in this case, IT stood for Imperial Troops) to solve a time-sensitive business problem (in this case, crushing the competitive rebellion), Darth Vader uses the off-premises force.
“There will be a substantial reward for the one who finds the Millennium Falcon,” Vader explains to a group of bounty hunters. “You are free to use any methods necessary, but I want them alive. No disintegrations.” That last point was specifically directed at Boba Fett, the bounty hunter who would later provide a cloud-based solution by tracking the Millennium Falcon to Cloud City.
Cloud providers are the Bounty Hunters of IT, essentially free to use any technology methods necessary to solve time-sensitive business problems. Although, in the short-term, cloud providers can help, in the long-term, if their solutions are not integrated into the IT Delivery strategy of the organization, they can also hurt. One example is creating new data integration challenges.
“No Data Disintegrations”
“It’s clear,” Hunt explained, “that enterprises will continue to increase usage of cloud and SaaS offerings to find new ways to operate more competitively and efficiently.” However, she noted it’s also clear that “the same challenges that enterprises face for on-premises data management obviously apply to data repositories in the cloud.” And one new challenge is “data that cannot be aligned with enterprise datasets will destroy the value and cost savings that enterprises want from cloud services.”
“Moving any business relevant functionality to the cloud,” Christian Verstraete recently blogged, “requires addressing the issue of integrating the cloud-based applications with the enterprise IT systems.” In his blog post, Verstraete examines three options for integrating cloud data and enterprise data: remote access, synchronization, and dynamic migration.
Although there will always be times and places for leveraging the Bounty Hunters of IT, before Boba Fett sells you a solution in Cloud City, make sure you emphasize that there should be “no data disintegrations.”
In other words, your cloud strategy must include a plan to prevent data in the cloud from becoming disintegrated in the sense that it is not integrated with the rest of the organization’s data.
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
On this episode of OCDQ Radio, we leave the corporate world, where data quality and master data management is mostly focused on the challenges of managing data about customers, products, and revenue, and we get schooled in the higher education of data quality. In other words, we discuss data quality and master data management in higher education, which is mostly focused on the challenges of managing data about students, courses, and tuition.
Our guest lecturer will be Mark Horseman, who has been working at the University of Saskatchewan for over 10 years and has been on the implementation team of many of the University’s enterprise software solutions. Mark Horseman now works in Information Strategy and Analytics leveraging his knowledge to assist the University in managing its data quality challenges.
Clicking on the link will take you to the episode’s blog post:
Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”
Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.
The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.
Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.
Farscape was one of my all-time favorite science fiction television shows. In the weird way my mind works, the recent blog post (which has received great comments) Four Steps to Fixing Your Bad Data by Tom Redman, triggered a Farscape analogy.
“The notion that data are assets sounds simple and is anything but,” Redman wrote. “Everyone touches data in one way or another, so the tendrils of a data program will affect everyone — the things they do, the way they think, their relationships with one another, your relationships with customers.”
The key word for me was tendrils — like I said, my mind works in a weird way.
Moya and Pilot
On Farscape, the central characters of the show travel through space aboard Moya, a Leviathan, which is a species of living, sentient spaceships. Pilot is a sentient creature (of a species also known as Pilots) with the vast capacity for multitasking that is necessary for the simultaneous handling of the many systems aboard a Leviathan. The tendrils of a Pilot’s lower body are biologically bonded with the living systems of a Leviathan, creating a permanent symbiotic connection, meaning that, once bonded, a Pilot and a Leviathan can no longer exist independently for more than an hour or so, or both of them will die.
Leviathans were one of the many laudably original concepts of Farscape. The role of the spaceship in most science fiction is analogous to the role of a boat. In other words, traveling through space is most often imagined like traveling on water. However, seafaring vessels and spaceships are usually seen as a technological object providing transportation and life support, but not actually alive in its own right (despite the fact that both types of ship are usually anthropomorphized, and usually as a female).
Because Moya was alive, when she was damaged, she felt pain and needed time to heal. And because she was sentient, highly intelligent, and capable of communicating with the crew through Pilot (who was the only one who could understand the complexity of the Leviathan language, which was beyond the capability of a universal translator), Moya was much more than just a means of transportation. In other words, there truly was a symbiotic relationship between, not only Moya and Pilot, but also between Moya and Pilot, and their crew and passengers.
Enterprise and Data
(Sorry, my fellow science fiction geeks, but it’s not thatEnterprise and thatData. Perfectly understandable mistake, though.)
Although technically not alive in the biological sense, in many respects, an organization is like a living, sentient organism, and like space and seafaring ships, often anthropomorphized. An enterprise is much more than just a large organization providing a means of employment and offering products and/or services (and, in a sense, life support to its employees and customers).
As Redman explains in his book Data Driven: Profiting from Your Most Important Business Asset, data is not just the lifeblood of the Information Age, data is essential to everything the enterprise does, from helping it better understand its customers, to guiding its development of better products and/or services, to setting a strategic direction toward achieving its business goals.
So the symbiotic relationship between Enterprise and Data is analogous to the symbiotic relationship between Moya and Pilot.
Data is the Pilot of the Enterprise Leviathan. The enterprise can not survive without its data. A healthy enterprise requires healthy data — data of sufficient quality capable of supporting the operational, tactical, and strategic functions of the enterprise.
Returning to Redman’s words, “Everyone touches data in one way or another, so the tendrils of a data program will affect everyone — the things they do, the way they think, their relationships with one another, your relationships with customers.”
So the relationship between an enterprise and its data, and its people, business processes, and technology, is analogous to the relationship between Moya and Pilot, and their crew and passengers. It is the enterprise’s people, its crew (i.e., employees), who, empowered by high quality data and enabled by technology, optimize business processes for superior corporate performance, thereby delivering superior products and/or services to the enterprise’s passengers (i.e., customers).
So why isn’t data viewed as an asset?
So if this deep symbiosis exists, if these intertwined and symbiotic relationships exist, if the tendrils of data are biologically bonded with the complex enterprise ecosystem — then why isn’t data viewed as an asset?
In Data Driven, Redman references the book The Social Life of Information by John Seely Brown and Paul Duguid, who explained that “a technology is never fully accepted until it becomes invisible to those who use it.” The term informationalization describes the process of building data and information into a product or service. “When products and services are fully informationalized,” Redman noted, then data, “blends into the background and people do not even think about it anymore.”
Perhaps that is why data isn’t viewed as an asset. Perhaps data has so thoroughly pervaded the enterprise that it has become invisible to those who use it. Perhaps it is not an asset because data is invisible to those who are so dependent upon its quality.
OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.
On this episode of OCDQ Radio, I discuss the sometimes mysterious world of international name and address data quality, which is why I am pleased to be joined by, not an international man of mystery, but instead, an international man of data quality.
Graham Rhind is an acknowledged expert in the field of data quality. Graham runs GRC Database Information, a consultancy company based in The Netherlands, where he researches postal code and addressing systems, collates international data, runs a busy postal link website, writes data management software, and maintains an online Data Quality Glossary.
On this episode of OCDQ Radio, Graham Rhind and I discusses the international challenges of postal address and person name data quality, including its implications for web forms and other data entry interfaces.
Popular OCDQ Radio Episodes
Clicking on the link will take you to the episode’s blog post:
Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”
Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.
The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses data matching concepts and practices, including different match techniques, candidate selection, presentation of match results, and business applications of data matching.
Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.
In his essay Regrets on Parting with My Old Dressing Gown, the 18th century French philosopher Denis Diderot described what is now referred to as the Diderot Effect.
After Diderot was given the gift of an elegant scarlet robe, he not only parted with his old dressing gown, but he also realized that his new robe clashed with his scruffy old study. Therefore, he started replacing more and more of his study. First, he replaced his old desk, then he replaced the tapestry, and eventually he replaced all of the furniture until the elegance of his study matched the elegance of his new robe.
I have recently fallen prey to what I refer to as the Diderot Effect of New Technology.
Regrets on Parting with My Old Laptop Computer
A few months ago, after finally succumbing to the not-so-subtle pressure from my friend, fellow technology writer, and Mac guy, Phil Simon, I purchased a MacBook Air.
Now, of course, there was absolutely nothing wrong with my three-year-old Dell Latitude laptop computer. It provided a sufficient amount of memory, speed, and storage. Its applications for writing and blogging, web browsing and social networking, as well as audio and video editing were productively supporting my daily business activities. Additionally, all of my peripherals (printer/scanner, flat screen monitor, microphone, speakers) were also getting their jobs done quite nicely, thank you very much.
However, as soon as the elegant, but not scarlet, MacBook Air was introduced into my scruffy old home office, the Diderot Effect began, well, affecting my perception of the technology that I was using on a daily basis.
Initially, I continued to use my Dell for my daily business activities, and dedicated only a small amount of work time to becoming accustomed to using my new MacBook. (I had once been an Apple affectionado, but it had been 10 years since I owned a Mac).
But it didn’t take long before I would have to describe myself as, to paraphrase the 19th century American poet Emily Dickinson, inebriate of MacBook Air am I.
(For the less poetically-minded reader, that’s just a fancy way of saying that I became addicted to using my new MacBook Air.)
So, much like Diderot before me, I have begun replacing more and more of my home office. The only difference being that I am trying to match the elegance (and, yes, of course, also the powerful and easy-to-use functionality) of my new technology.
The Diderot Effect of New Technology
The consumerization of IT has become a significant contributing factor to the increasingly rapid pace at which new technology is introduced into the enterprise. These elegant modern applications seemingly clash with our scruffy old legacy applications, and can evoke a desire to start replacing more and more of the organization’s technology.
However, donning the scarlet robes of new technology can become an expensive endeavor. (The subtitle of Diderot’s essay was “a warning to those who have more taste than fortune.”) Genefa Murphy has blogged that “one of the main reasons for IT debt is the fact that the enterprise is always trying to keep up with the latest and greatest trends, technologies, and changes.”
“In our race to remain competitive,” Murphy concluded, “we have in essence become addicted to the latest and greatest technologies. We need to acknowledge we have a problem before we can take action to rectify it.”
Diderot was able to both acknowledge and take action to rectify his addiction. “Don’t fear that the mad desire to stock up on beautiful things has taken control of me,” he reassures us at the conclusion of his essay.
Hopefully, the mad desire to stock up on new technological things hasn’t taken control of either you or your organization.
