OCDQ Blog

The Data Encryption Keeper

The Algebra of Collaboration

September 18, 2012

Turning the M Upside Down

September 18, 2012/ Jim Harris

I am often asked about the critical success factors for enterprise initiatives, such as data quality, master data management, and data governance.

Although there is no one thing that can guarantee success, if forced to choose one critical success factor to rule them all, I would choose collaboration.

But, of course, when I say this everyone rolls their eyes at me (yes, I can see you doing it now through the computer) since it sounds like I’m avoiding the complex concepts underlying enterprise initiatives by choosing collaboration.

The importance of collaboration is a very simple concept but, as Amy Ray and Emily Saliers taught me, “the hardest to learn was the least complicated.”

The Pronoun Test

Although all organizations must define the success of enterprise initiatives in business terms (e.g., mitigated risks, reduced costs, or increased revenue), collaborative organizations understand that the most important factor for enduring business success is the willingness of people all across the enterprise to mutually pledge to each other their communication, cooperation, and trust.

These organizations pass what Robert Reich calls the Pronoun Test. When their employees make references to the company, it’s done with the pronoun We and not They. The latter suggests at least some amount of disengagement, and perhaps even alienation, whereas the former suggests the opposite — employees feel like part of something significant and meaningful.

An even more basic form of the Pronoun Test is whether or not people can look beyond their too often self-centered motivations and selflessly include themselves in a collaborative effort. “It’s amazing how much can be accomplished if no one cares who gets the credit” is an old quote for which, with an appropriate irony, it is rather difficult to identify the original source.

Collaboration requires a simple, but powerful, paradigm shift that I call Turning the M Upside Down — turning Me into We.

The Business versus IT—Tear down this wall!

The Road of Collaboration

Dot Collectors and Dot Connectors

No Datum is an Island of Serendip

The Three Most Important Letters in Data Governance

The Stakeholder’s Dilemma

Shining a Social Light on Data Quality

Data Quality and the Bystander Effect

The Family Circus and Data Quality

The Year of the Datechnibus

Being Horizontally Vertical

The Collaborative Culture of Data Governance

Collaboration isn’t Brain Surgery

Are you Building Bridges or Digging Moats?

September 13, 2012

Open MIKE Podcast — Episode 03

September 13, 2012/ Jim Harris

Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management, which provides a comprehensive methodology that can be applied across a number of different projects within the Information Management space. For more information, click on this link: openmethodology.org/wiki/What_is_MIKE2.0

The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

Episode 03: Data Quality Improvement and Data Investigation

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

MIKE2.0 Content Featured in or Related to this Podcast

Enterprise Data Management: openmethodology.org/wiki/Enterprise_Data_Management_Offering_Group

Data Quality Improvement: openmethodology.org/wiki/Data_Quality_Improvement_Solution_Offering

Data Investigation: openmethodology.org/wiki/Category:Data_Investigation_and_Re-Engineering

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

September 10, 2012

Social Media for Midsize Businesses

September 10, 2012/ Jim Harris

OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Paul Gillin and I discuss social media for midsize businesses, including how the less marketing you do, the more effective you will be with social media marketing, the war of generosity, where the more you give, the more you get, and the importance of the trust equation, which means the more people trust you, the more they will want to do business with you.

Paul Gillin is a veteran technology journalist and a thought leader in new media. Since 2005, he has advised marketers and business executives on strategies to optimize their use of social media and online channels to reach buyers cost-effectively. He is a popular speaker who is known for his ability to simplify complex concepts using plain talk, anecdotes, and humor.

Paul Gillin is the author of four books about social marketing: The New Influencers (2007), Secrets of Social Media Marketing (2008), Social Marketing to the Business Customer (2011), co-authored with Eric Schwartzman, and the forthcoming book Attack of the Customers (2012), co-authored with Greg Gianforte.

Paul Gillin was previously the founding editor of TechTarget and editor-in-chief of Computerworld. He writes a monthly column for BtoB magazine and is an active blogger and media commentator. He has appeared as an expert commentator on CNN, PBS, Fox News, MSNBC, and other television outlets. He has also been quoted or interviewed for hundreds of news and radio reports in outlets such as The Wall Street Journal, The New York Times, NPR, and the BBC. Paul Gillin is a Senior Research Fellow and member of the board of directors at the Society for New Communications Research.

This podcast was sponsored by the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.

Word of Mouth has become Word of Data

Information Asymmetry versus Empowered Customers

OCDQ Radio - Social Media Strategy

Listening and Broadcasting

The Challenging Gift of Social Media

Demystifying Social Media

Social Karma

The Wisdom of the Social Media Crowd

Brevity is the Soul of Social Media

The Two U’s and the Three C’s

Quality is more important than Quantity

Can Social Media become a Universal Translator?

September 06, 2012

The Weakest Link in Enterprise Security

September 06, 2012/ Jim Harris

This blog post is sponsored by the Enterprise CIO Forum and HP.

As a recent Techopedia article noted, one of the biggest challenges for IT security these days is finding a balance among three overarching principles: availability (i.e., that information is accessible when authorized users need it), confidentiality (i.e., that information is only being seen or used by people who are authorized to access it), and integrity (i.e., that any changes to information by an unauthorized user are impossible — or at least detected — and changes by authorized users are tracked).

Finding this balance has always been a complex challenge for enterprise security since the tighter you lock an IT system down, the harder it can become to use for daily business activities, which sometimes causes usability to be prioritized over security.

“I believe those who think security isn’t a general IT priority are wrong,” Rafal Los recently blogged in a post about the role of Chief Information Security Officer (CISO). “Pushing the security agenda ahead of doing business seems to be something poor CISOs are known for, which creates a backlash of executive push-back against security in many organizations.”

According to Los, IT leaders need to balance the business enablement of IT with the need to keep information secure, which requires better understanding both business risks and IT threats, and allowing the organization to execute its business goals in a tactical fashion while simultaneously working out the long-term enterprise security strategy.

Although any security strategy is only as strong as its weakest link, the weakest link in enterprise security might not be where you’d expect to find it. A good example of this came from perhaps the biggest personal data security disaster story of the year, the epic hacking of Mat Honan, during which, as he described it, “in the space of one hour, my entire digital life was destroyed.”

The biggest lesson learned was not the lack of a good security strategy (though that obviously played a part, not only with Honan personally, but also with the vendors involved). Instead, the lesson was that the weakest link in any security strategy might be its recovery procedures — and that hackers don’t need to rely on Hollywood-style techno-wizardry to overcome security protocols.

Organizations are rightfully concerned about mobile devices containing sensitive data getting stolen — in fact, many make use of the feature provided by Apple that enables you to remotely delete data on your iPhone, iPad, and MacBook in the event of theft.

In Honan’s case, the hackers exploited this feature by accessing his Apple iCloud account (for the details of how that happened, read his blog post), wiping clean his not-stolen mobile devices, resetting his passwords, including for his email accounts, which prevented him from receiving any security warnings and password reset notifications, and bought the hackers the time needed to redirect everything — essentially all by doing what Honan would have done if his mobile devices had actually been stolen.

The hackers also deleted all of Honan’s data stored in the cloud, which was devastating since he had no off-line backups (yes, he admits that’s his fault). Before you’re tempted to use this as a cloud-bashing story, as Honan blogged in a follow-up post about how he resurrected his digital life, “when my data died, it was the cloud that killed it. The triggers hackers used to break into my accounts and delete my files were all cloud-based services — iCloud, Google, and Amazon. Some pundits have latched onto this detail to indict our era of cloud computing. Yet just as the cloud enabled my disaster, so too was it my salvation.”

Although most security strategies are focused on preventing a security breach from happening, as the Honan story exemplifies, the weakest link in your enterprise security could actually be the protocols enacted in the event of an apparent security breach.

This blog post is sponsored by the Enterprise CIO Forum and HP.

Enterprise Security is on Red Alert

Securing your Digital Fortress

The Data Encryption Keeper

The UX Factor

Securing your Digital Fortress

September 04, 2012

Cooks, Chefs, and Data Governance

September 04, 2012/ Jim Harris

In their book Practical Wisdom, Barry Schwartz and Kenneth Sharpe quoted retired Lieutenant Colonel Leonard Wong, who is a Research Professor of Military Strategy in the Strategic Studies Institute at the United States Army War College, focusing on the human and organizational dimensions of the military.

“Innovation,” Wong explained, “develops when an officer is given a minimal number of parameters (e.g., task, condition, and standards) and the requisite time to plan and execute the training. Giving the commanders time to create their own training develops confidence in operating within the boundaries of a higher commander’s intent without constant supervision.”

According to Wong, too many rules and requirements “remove all discretion, resulting in reactive instead of proactive thought, compliance instead of creativity, and adherence instead of audacity.” Wong believed that it came down to a difference between cooks, those who are quite adept at carrying out a recipe, and chefs, those who can look at the ingredients available to them and create a meal. A successful military strategy is executed by officers who are trained to be chefs, not cooks.

Data Governance’s Kitchen

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.

Because of this complexity, many organizations think the only way to run data governance’s kitchen is to institute a bureaucracy that dictates policies and demands compliance. In other words, data governance policies are recipes and employees are cooks.

Although implementing data governance policies does occasionally require a cook-adept-at-carrying-out-a-recipe mindset, the long-term success of a data governance program is going to also require chefs since the dynamic challenges faced, and overcome daily, by business analysts, data stewards, technical architects, and others, exemplify today’s constantly changing business world, which can not be successfully governed by forcing employees to systematically apply rules or follow rigid procedures.

Data governance requires chefs who are empowered with an understanding of the principles of the policies, and who are trusted to figure out how to best implement the policies in a particular business context by combining rules with the organizational ingredients available to them, and creating a flexible procedure that operates within the boundaries of the policy’s principles.

But, of course, just like a military can not be staffed entirely by officers, and a kitchen can not be staffed entirely by chefs, in order to implement a data governance program successfully, an organization needs both cooks and chefs.

Similar to how data governance is neither all-top-down nor all-bottom-up, it’s also neither all-cook nor all-chef.

Only the unique corporate culture of your organization can determine how to best staff your data governance kitchen.

August 30, 2012

Open MIKE Podcast — Episode 02

August 30, 2012/ Jim Harris

Episode 02: Information Governance and Distributing Power

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

MIKE2.0 Content Featured in or Related to this Podcast

Information Governance: openmethodology.org/wiki/Information_Governance_Solution_Offering

Governance 2.0: openmethodology.org/wiki/Governance_2.0_Solution_Offering

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

August 28, 2012

Cloud Computing is the New Nimbyism

August 28, 2012/ Jim Harris

NIMBY is an acronym for “Not In My Back Yard” and its derivative term Nimbyism usually refers to the philosophy of opposing construction projects or other new developments, which would be performed too close to your residence or your business, because even though those new developments could provide widespread benefits, they might just be a disruption to you. So, for example, yes, please build that new airport or hospital or power plant that our city needs — just don’t build it too close to my back yard.

For a long time, midsize businesses viewed their information technology (IT) department as a Nimbyistic disruption, meaning that they viewed IT as a necessary cost of doing business, but one that also took up valuable space and time, and distracted their focus away from their core competencies, which, for most midsize businesses, are supported by but not directly related to IT.

Nowadays, cloud computing is providing a new — and far more positive — spin on Nimbyism by allowing midsize businesses to free up space in their back yard (where, in my experience, many midsize businesses keep their IT department) as well as free up their time to focus on mission-critical business activities by leveraging more cloud-based IT services, which also allows them to scale up their IT during peak business periods without requiring them to first spend time and money building a bigger back yard.

Shifting to a weather analogy, stratus clouds are characterized by horizontal layering with a uniform base, and nimbostratus clouds are stratus clouds of moderate vertical development, signifying the onset of steady, moderate to heavy, precipitation.

We could say cloud computing is the nimby-stratus IT clouds providing midsize businesses with a uniform base of IT services, which can quickly scale horizontally and/or vertically with the agility to adapt to best serve their evolving business needs.

The nimbleness of the new Nimbyism facilitated by cloud computing is providing another weather-related business insight that’s helping midsize businesses forecast a promising future, hopefully signifying the onset of steady, moderate to heavy, profitability.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.

August 23, 2012

Enterprise Security is on Red Alert

August 23, 2012/ Jim Harris

This blog post is sponsored by the Enterprise CIO Forum and HP.

Enterprise security is becoming an even more important, and more complex, topic of discussion than it already was. Especially when an organization focuses mostly on preventing external security threats, which is somewhat like, as in the photo to the left, telling employees to keep the gate closed but ignore the cloud floating over the gate and the mobile devices walking around it.

But that doesn’t mean we need to build bigger and better gates. The more open business environment enabled by cloud and mobile technologies is here to stay, and it requires a modern data security model that can protect us from the bad without being overprotective to the point of inhibiting the good.

“Security controls cost money and have an impact on the bottom line,” Gideon Rasmussen recently blogged. Therefore, “business management may question the need for controls beyond minimum compliance requirements. However, adherence to compliance requirements, control frameworks, and best practices may not adequately protect sensitive or valuable information because they are not customized to the unique aspects of your organization.”

This lack of a customized security solution can also be introduced when leveraging cloud providers. “Transparency is the capability to look inside the operational day-to-day activity of your cloud provider,” Rafal Los recently blogged. “As a consumer, transparency means that I have audit-ability of the controls, systems, and capabilities that directly impact my consumed service.”

A further complication for enterprise security is that many cloud-based services are initiated as Shadow IT projects. “There are actually good reasons why you may want to take a hard look at Shadow IT, as it may fundamentally put you at risk of breaching compliance,” Christian Verstraete recently blogged. “Talking to business users, I’m often flabbergasted by how little they know of the potential risks encountered by putting information in the public cloud.”

In the science fiction universe of Star Trek, the security officers aboard the starship Enterprise, who wore red shirts, often quickly died on away missions. Protecting your data, especially when it goes on away missions in the cloud or on mobile devices, requires your enterprise security to be on red alert — otherwise everyone in your organization might as well be wearing a red shirt.

This blog post is sponsored by the Enterprise CIO Forum and HP.

The Data Encryption Keeper

The UX Factor

The Diffusion of the Consumerization of IT

August 21, 2012

Data and its Relationships with Quality

August 21, 2012/ Jim Harris

The title of this blog post is an allusion to the graphic (shown above) that accompanied an Information Management column by Malcolm Chisholm, in which he wrote that data quality is not fitness for use as it is most commonly defined, stating he thinks “a strong case can be made that the definition is indeed inappropriate and should be replaced with a better one.”

“Before we get into the definition of data quality, let us take a brief look at what data is related to,” Chisholm opened, explaining that “data represents something — a thing, event, or concept.”

As I blogged in my post Plato’s Data, whether it’s an abstract description of real-world entities (i.e., “master data”) or an abstract description of real-world interactions (i.e., “transaction data”) among entities, data is an abstract description of reality. Although data shapes our perception of the real world, sometimes we forget that data is only a partial reflection of reality.

“Data is understood,” Chisholm continued, “by something, for which the best term I can find is the interpretant.”

“The interpretant applies the data to one or more uses, which achieve objectives the interpretant has. The interpretant is independent of the data. It understands the data and can put it to use. But if the interpretant misunderstands the data, or puts it to an inappropriate use, that is hardly the fault of the data, and cannot constitute a data quality problem.”

As I blogged in my post Quality is the Higgs Field of Data, independent from use, data is as carefree as the mass-less photon whizzing around at the speed of light. But once we interact with it, data begins to feel the effects of our use. We give data mass so that it can become the basic building blocks of what matters to us. Some data is affected more by our use than others. The more subjective our use, the more we weigh data down. The more objective our use, the less we weigh data down.

“A more fundamental problem is that data can have many uses,” Chisholm continued. “If we think data quality is fitness for use, then data quality must be assessed independently for each use we put it to.” Instead, Chisholm contends that data quality is “an expression of the relationship between the thing, event, or concept and the data that represents it. This is a one-to-one relationship, unlike the one-to-many relationship between data and uses.”

Therefore, Chisholm proposes that a better definition of data quality is “the extent to which the data actually represents what it purports to represent. This definition can be used to think of data quality as a property of the data itself, and then our diagnosis and remediation efforts will focus on the special problems of the relationship between data and what it represents.”

But, of course, although Chisholm doesn’t like it as a definition for data quality, he is not denying that fitness for use describes “a set of valid concepts that deal with types of problems around the use of data.” Two examples he cites are when the interpretant misunderstands the data, or when the interpretant uses data for a purpose that is incompatible with the data.

In his conclusion, Chisholm states that “the special problems of the relationships between data and what it is used for requires a different set of approaches and should be called something other than data quality.”

And this is exactly why, as I blogged in my post Data Myopia and Business Relativity, many data professionals prefer to define data quality as real-world alignment and information quality as fitness for the purpose of use. However, I have found that adding the nuance of data versus information only further complicates data quality discussions with business professionals.

Chisholm also suggests that his proposed definition of data quality is not only better, but that “it also alludes to the existence of metadata that links the data to what it is representing.” The important role that metadata plays in supporting data and its relationships with information and quality is something I blogged about in my post You Say Potato and I Say Tater Tot.

The irony is the metadata that links the data management industry to what it is representing that it manages suffers from the one-to-many relationships we’ve created by seemingly never agreeing on how data, information, and quality should be defined.

August 16, 2012

Open MIKE Podcast — Episode 01

August 16, 2012/ Jim Harris

Episode 01: Information Management Principles

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

MIKE2.0 Content Featured in or Related to this Podcast

Information Management Principles: openmethodology.org/wiki/Economic_Value_of_Information

Information Economics: openmethodology.org/wiki/Information_Economics

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

August 14, 2012

The Age of the Mobile Device

August 14, 2012/ Jim Harris

Bob Sutor recently blogged about mobile devices, noting that “the power of these gadgets isn’t in their touchscreens or their elegant design. It’s in the variety of apps and communication services we can use on them to stay connected. By thinking beyond the device, companies can prepare themselves and figure out how to make the most of this age of the mobile device.”

The disruptiveness of mobile devices to existing business models — even Internet-based ones — is difficult to overstate. In fact, I believe the age of the mobile device will be even more disruptive than the age of the Internet, which, during the 1990s and early 2000s, disrupted entire industries and professions — the three most obvious examples being music, journalism, and publishing.

However, during those disruptions, mobile devices were in their nascent phase. Laptops were still the dominant mobile devices and most mobile phones only made phone calls, though text messaging and e-mail soon followed. It’s only been about five years — with the notable arrivals of the iPhone and the Kindle in 2007, the Android operating system in 2008, and the iPad in 2010 — since mobile devices started to hit their stride. The widespread availability of connectivity options (Wi-Fi and 3G/4G broadband), the shift to more cloud-based services, and, as Sutor noted, in 2011, for the first time ever, shipments of smartphones exceeded total PC shipments, all appears to forecast that the age of the mobile device will be an age of massive — and rapid — disruption.

The IBM Midmarket white paper A Smarter Approach to Customer Relationship Management (CRM) notes that “mobile is becoming the customers’ preferred communications means for multiple channels. As customers go mobile and sales teams strive to meet customers’ needs, midsize companies are enabling mobile CRM. They are optimizing Web sites for wireless devices and deploying mobile apps directly linked into the contact centers. They are purchasing apps for particular devices and are buying solutions that store CRM data on them when offline, and update the information when Internet access is restored. This enables sales teams to quickly acquire customer histories and respond with offerings tailored to their desires.”

As Sutor concluded, “mobile devices are a springboard into the future, where the apps can significantly improve the quality of our personal or business lives by allowing us to do things we have never done before.” I agree that mobile devices are a springboard into a future that allows us, as well as our businesses and our customers, to do things we have never done before.

The age of the mobile device is the future — and the future is now. Is your midsize business ready?

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.

August 09, 2012

Balancing the IT Budget

August 09, 2012/ Jim Harris

This blog post is sponsored by the Enterprise CIO Forum and HP.

While checking out the new Knowledge Vaults on the Enterprise CIO Forum, I came across the Genefa Murphy blog post How IT Debt is Crippling the Enterprise, which included three recommendations for alleviating some of that crippling IT debt.

The first recommendation was application retirement. As I have previously blogged, applications become retirement-resistant because applications and data have historically been so tightly coupled, making most of what are referred to as data silos actually application silos. Therefore, in order to help de-cripple IT debt, organizations need to de-couple applications and data, not only by allowing more data to float up into the cloud, but also, as Murphy noted, instituting better procedures for data archival, which helps more easily identify applications for retirement that have become merely containers for unused data.

The second recommendation was cutting the IT backlog. “One of the main reasons for IT debt,” Murphy explained, “is the fact that the enterprise is always trying to keep up with the latest and greatest trends, technologies and changes.” I have previously blogged about this as The Diderot Effect of New Technology. By better identifying how up-to-date the IT backlog is, and how well — if at all — it still reflects current business needs, an organization can skip needless upgrades and enhancement requests, and not only eliminate some of the IT debt, but also better prioritize efforts so that IT functions as a business enabler.

The third recommendation was performing more architectural reviews, which, Murphy explained, “is less about getting rid of old debt and more about making sure new debt does not accumulate. Since IT teams don’t often have the time to do this (as they are concerned with getting a working solution to the customer ASAP), it is a good idea to have this as a parallel effort led by a technology or architectural review group outside of the project teams but still closely linked.”

Although it’s impossible to completely balance the IT budget, and IT debt doesn’t cause an overall budget deficit, reducing costs associated with business-enabling technology does increase the potential for a surplus of financial success for the enterprise.

This blog post is sponsored by the Enterprise CIO Forum and HP.

Why does the sun never set on legacy applications?

Are Applications the La Brea Tar Pits for Data?

The Diffusion of the Consumerization of IT

The UX Factor

Lightning Strikes the Cloud

The Partly Cloudy CIO