True Service

“It is not the urge to surpass all others at whatever cost, but the urge to serve others at whatever cost.”

— Arthur Ashe

Service Providers

As I wrote at the beginning of the year in my blog post declaring karma as my theme word for 2010, we all have some way of expressing the concept of what we expect to happen when we help other people—when we provide a service for them.

In one way or another, in both our professional and our personal lives, we are all service providers. 

The most commonly used differentiation between professional and personal service is the exchange of money.  Your employer pays you to do your job, and not because you’re a wonderful human being—even though you are.  Your friends and family will help you whenever necessary, and not because you’re paying them—nice dinners, birthday presents, and other gifts don’t count.

Service Contracts

Whether it is a formal written document or an informal social agreement, all service is based upon some type of contract.

Once again because the exchange of money is typically involved, a professional service most commonly uses a written document, whereas a personal service most commonly uses a social agreement, which is often unwritten and frequently also unspoken.

A written document details the terms of service, which is the service level agreement that contractually binds the service provider to whomever they are providing service.  This service contract allows the parties involved to discuss any dissatisfaction or dispute in a relatively straightforward and civilized manner—or if lawyers get involved, in a needlessly complex and expensive manner.

Professional service contracts tend to focus on the minimum requirement necessary to fulfill the contractual commitment and therefore normally do not engender either party to go beyond the specific terms since nothing would be explicitly gained.

However, the party that is paying (i.e., “the party of the second part” for my lawyer readers) will normally attempt to exert subtle pressure on “the party of the first part” (i.e., the service provider for my non-lawyer readers) to deliver above and beyond the minimum requirement dictated by the service contract.  This is one aspect of what I like to refer to as “service psychology.”

Service Psychology

First of all, everyone prefers to get as much as possible without paying anything.  And when you do have to pay for something, everyone wants at the very least to “get what you paid for” while getting more than what you paid for is considered even better. 

These truths are universal and they do not automatically turn all of us into bad people (or all companies into evil corporations).

We also usually want to provide good service whether or not we are being paid, but when we are, there is a general tendency to be concerned about providing value worthy of our compensation.  This is the aspect of service psychology that can cause us to be receptive to the subtle pressure to exceed the minimum requirement dictated by the service contract.

Employers use it on employees.  Customers use it on companies (or more precisely, on their customer service representatives).  Business partners use it on each other.  And of course, this aspect of service psychology can be reversed to exert subtle pressure for encouraging acceptance that the minimum requirement dictated by the service contract has already been met.

We can also condition ourselves to resist these subtle pressures and even claim that we are simply defending ourselves from being taken advantage of by the other party—and regardless of which “side” of the service contract we currently find ourselves.

Such “service psychological warfare” will sometimes escalate until the lawyers eventually come crashing through the skylight, rappelling down ropes with one hand, while holding the original signed copy of the service contract in their other hand, and quoting aloud the terms, conditions, warranties, and indemnification from page 13, section 8, sub-section 3, paragraph 5.

Social Agreements

Since money is typically not involved and barring a few exceptions (e.g., a divorce or a contested will) no lawyers come into play, we tend not to view our (often unwritten, unspoken) social agreements with friends and family as “personal service contracts.” 

However, the underlying principles of service psychology apply just as much to social agreements where perhaps paradoxically, we have both a much higher expectation for those that serve us and a much greater sense of obligation to those we serve.

Therefore, our social agreements truly are personal service contracts.  There are terms and conditions, minimum requirements, and constant measurement of our costs, risks, and returns.  We all have a natural tendency to “keep score” one way or another.

All Service is a Stage

All service is a stage, and all of us are merely players, each having our exits and entrances, and in our time playing many parts, some professional and some personal, in many “service dramas” seemingly fraught with equal potential for tragedy and comedy. 

Forgive the Shakespearean flourish, but all of our narratives tend towards the dramatic and service is certainly no exception. 

Although we can easily turn our professional services into a drama (even without rappelling lawyers crashing through skylights), our social agreements generally involve more “emotional service” and are therefore far more inclined to become dramatic.

However, all service dramas are often simply a crisis of perspective—specifically our preference for our own above all others.

True Service

I began this blog post using only the second sentence from a famous Arthur Ashe quote, which in its entirety reads:

“True heroism is remarkably sober, very undramatic.

It is not the urge to surpass all others at whatever cost, but the urge to serve others at whatever cost.”

Although I am very fond of the original wording, I will end this blog post by paraphrasing the full quote:

“True service is remarkably rare, very undramatic, totally unconcerned with personal benefit, and completely content to serve others at whatever cost.”

Related Posts

The Game of Darts – An Allegory

“I can make glass tubes”

My #ThemeWord for 2010: KARMA

The Scarlet DQ

The Scarlet DQ is the superhero name of Jill Wanless (aka sheezaredhead).

Just kidding—I would never reveal a superhero’s secret identity.

Although I was never a big fan of the book, the title of this blog post is inspired by The Scarlet Letter by Nathaniel Hawthorne, where the novel’s protagonist Hester Prynne is forced to wear The Scarlet Letter A as a badge of shame for committing the act of adultery, which lead to the birth of her daughter Pearl.

The book came to mind while I was reading the commendable comments received last week on The Poor Data Quality Jar, where a recurring theme was the valid criticism of the “public humiliation” aspect of having employees put money into the jar when they contribute to the organization's poor data quality.

Using such an approach to help organizations illustrate the costs of poor data quality is equivalent to making the offenders wear The Scarlet DQ as a badge of shame, which will only make it far more likely that data quality issues will not be reported at all.

But even without my “swear jar” inspired idea, I think that the fear of public humiliation is what prevents poor data quality from being acknowledged by many organizations, which often leads to a major data quality related crisis that “no one saw coming.” 

For example, if you are in need of some quiet time alone for taking a good power nap in a conference room, then try scheduling a meeting to discuss known data quality issues and their root causes.  If your organization is like most, then you could probably book one of those really nice conference rooms with the big comfy reclining chairs—because nobody will attend your meeting.

Data quality can be somewhat of a taboo topic.  Many organization assume that their data quality must be “good enough” otherwise “how could we possibly still be in business?”  Nobody likes to talk about data quality for one simple reason:

If data quality issues exist (and they do), then no one wants to be blamed for causing or failing to fix them.

It’s as if everyone is afraid that they will be forced to wear The Scarlet DQ.

This is one of the many human dynamics that can render even the best technology and proven methodology completely useless. 

What Say You?

Please post a comment and share your recommendations about how to foster an environment in which poor data quality can be reported freely without fear of blame or reprisal.  All viewpoints are welcome.  Nathaniel Hawthorne references are not required.

Related Posts

The Poor Data Quality Jar

The Third Law of Data Quality

The Dumb and Dumber Guide to Data Quality

Not So Strange Case of Dr. Technology and Mr. Business

You're So Vain, You Probably Think Data Quality Is About You

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Wednesday Word: April 7, 2010

Wednesday Word is an OCDQ regular segment intended to provide an occasional alternative to my Wordless Wednesday posts.  Wednesday Word provides a word (or words) of the day, including both my definition and an example of recommended usage.

Vendor Asskisstic

Definition – whereas “vendor agnostic” describes a general methodology or solution that does not require the technology or services provided by a specific vendor, vendor asskisstic is the complete opposite.

Example – “Although we requested a vendor agnostic proposal from Acme Consulting, their recommendation was so blatantly vendor asskisstic that it might as well have been printed in Big Blue letters.”

Can Enterprise-Class Solutions Ever Deliver ROI?

The information technology industry has a great fondness for enterprise-class solutions and TLAs (two or three letter acronyms): ERP (Enterprise Resource Planning), DW (Data Warehousing), BI (Business Intelligence), MDM (Master Data Management), DG (Data Governance), DQ (Data Quality), CDI (Customer Data Integration), CRM (Customer Relationship Management), PIM (Product Information Management), BPM (Business Process Management), etc. — and new TLAs are surely coming soon.

But there is one TLA to rule them all, one TLA to fund them, one TLA to bring them all and to the business bind them—ROI.

Enterpri$e-Cla$$ $olution$

All enterprise-class solutions have one thing in common—they require a significant investment and total cost of ownership.

Most enterprise software/system licenses start in the six figures.  Due in large part to vendor consolidation, many are embedded within a consolidated enterprise application development platform with seamlessly integrated components offering an end-to-end solution that pushes the license well into seven figures. 

On top of the licensing, you have to add the annual maintenance fees, which are usually in the five figures—sometimes more.

Add to the total cost of the solution the professional services needed for training and consulting for installation, configuration, application development, testing, and production implementation, and you have another six figure annual investment.

With such a significant investment and total cost of ownership required, can enterprise-class solutions ever deliver ROI?

Should I refinance my mortgage?

As a quick (but relevant) tangent, let's use a simple analogy from the world of personal finance.

Similar to most homeowners, I get offers to refinance my mortgage all the time.  A common example is an offer that states I can reduce my monthly payments by $200 by refinancing.  Sounds great, $200 a month is an annual cost reduction of $2400. 

However, this great deal includes $3000 in refinancing costs.  Although I start paying $200 less a month immediately, I do not really start saving any money for 15 months, when the monthly “savings” break even with the $3000 in refinancing costs. 

Of course, saying only 15 months is ignoring possible tax implications as well as lost interest or returns that I could have earned since the $3000 likely came from either a savings or an investment account.

Additionally, refinancing might not be a good idea if I plan to sell the house in less than 15 months.  The $3000 could instead be invested in finishing my basement or repairing minor damages, which could help increase its value and therefore its sales price.

How does this analogy relate to enterprise-class solutions?

The Business Justification Paradox

Focusing solely on the technical features and ignoring the business benefits of an enterprise-class solution isn’t going to convince either the organization's executive management or its shareholders that the solution is required.

Therefore, emphasis has to placed on the need to make the business justification, where true ROI can only be achieved through tangible business impacts, such as mitigated risks, reduced costs, or increased revenues.

However, a legitimate business justification for any enterprise-class solution is often relatively easy to make.

The business justification paradox is that although an enterprise-class solution definitely has the long-term future potential to reduce costs, mitigate risks, and increase revenues, in the immediate future (and current fiscal year), it will only increase costs, decrease revenues, and therefore potentially increase risks.

In the mortgage analogy, the break even point on the opportunity cost of refinancing can be precisely calculated.  Is it even possible to accurately estimate the break even point on the opportunity cost of implementing an enterprise-class solution?

Furthermore, true ROI obviously has to be at least estimated to exceed simply breaking even on the investment.

Given the reality that the longer an initiative takes, the more likely its funding will either be reduced or completely cut, many advocate an agile methodology, which targets iterative cycles quickly delivering small, but tangible value.  However, the up-front costs of enterprise licenses and incremental costs of the ongoing efforts and maintenance still loom large on the balance sheet.

Even with “creative” accounting practices, the unquestionably real short-term “ROI high” of following an agile approach could still leave you “chasing the dragon” in search of at least breaking even on your enterprise-class solution's total cost of ownership.

A Call for Debate

My point in this blog post was neither to make the argument that organizations should not invest in enterprise-class solutions, nor to berate organizations for evaluating such possible investments using short-term thinking limited to the current fiscal year.

I am simply trying to encourage an open, honest, and healthy debate about the true ROI of enterprise-class solutions.

I am tired of hearing over-simplifications about how all you need to do is make a valid business justification, as well as attempting to decipher the mystical ROI and total cost of ownership calculations provided by vendors and industry analysts.

I am also tired of being told how emerging industry trends like open source, cloud computing, and software as a service (SaaS) are “less expensive” than traditional approaches.  Perhaps that is true, but can they deliver enterprise-class solutions and ROI?

This blog post is a call for debate.  Please post a comment.  All viewpoints are welcome.

Subterranean Computing

Cloud computing continues to receive significant industry buzz and endorsements from many industry luminaries:

• Tim O'Reilly of O'Reilly Media calls cloud computing “the platform for all computing.”
• Connor MacLeod of the Clan MacLeod says “there can be only one—and that one is cloud computing.”  
• Marc Benioff of SalesForce.com refers to companies in the “anti-cloud crowd” as “innovationless.”
• Lando Calrissian of Cloud City calls anyone not using cloud computing a “slimy, double-crossing, no-good swindler.”

Therefore, I was happy to hear a cogent alternative viewpoint from a member of the “anti-cloud crowd” when I recently interviewed Sidd Finch, the Founder and President of the New York based startup company Kremvax, which recently secured another $4.1 billion in venture capital to pursue an intriguing alternative to cloud computing called Subterranean Computing.

The Truth about Cloud Computing

Mr. Finch began the interview by discussing some of the common criticisms of cloud computing, which include issues such as data privacy, data protection, and data security.  However, I was most intrigued by the new research Mr. Finch cited from Professor Nat Tate of the College of Nephology at the University of Southern North Dakota at Hoople.

According to Professor Tate, here is the truth about cloud computing:

• Cloud computing's viability depends greatly on the type of cloud, not public or private, but rather cirrus, stratus, or cumulus.
• Cirrus clouds are not good for data privacy concerns because they tend to be wispy and therefore completely transparent.
• Stratus clouds are not good for data protection concerns because “data drizzling” occurs frequently and without warning. 
• Cumulus clouds are not good for data security concerns because “fair weather clouds” disperse at the first sign of trouble. 

The Underlying Premise of Subterranean Computing

Later in the interview, Mr. Finch described the underlying premise of subterranean computing:

“Instead of beaming your data up into the cloud, bury your data down underground.”  

According to Mr. Finch, here are the basic value propositions of subterranean computing:

• Subterranean computing's viability is limited only to your imagination (but real money is required, and preferably cash).
• Data privacy is not a concern because your data gets buried in its own completely (not virtually) private hole in the ground.
• Data protection is not a concern because once it is buried, your data will never be used again for any purpose whatsoever.
• Data security is not a concern, but for an additional fee, we bury your data where nobody will ever find it (we know a guy).

Brown is the new Green

Environmentally sustainable computing (i.e., “Green IT”) is another buzzworthy industry trend these days.  Reduce your carbon footprint, utilize electricity more efficiently, evaluate alternative power sources, and leverage recyclable materials. 

All great ideas.  But according to Mr. Finch, subterranean computing takes it to the next level by running entirely on geothermal power, a sustainable and renewable energy source, as well as converting your databases into Composting Data Stores (CDS).

In subterranean computing, your data is buried deep underground, where CDS can draw the very minimal amount of power it requires directly from the heat emanating from the Earth's core.  The CDS biodegradable data format (BDF) also minimizes your data storage requirements by automatically composting old data, which creates the raw material used to store your new data.

In the words of Kremvax customer and award-eligible environmentalist Isaac Bickerstaff: “brown is the new green.” 

Bickerstaff is the Lord Mayor of the English village of Spiggot, which has “gone subterranean” with its computing infrastructure.

Conclusion

So which new industry trend will your organization be implementing this year: cloud computing or subterranean computing? 

Well, before you make your final decision, please be advised that Industry Analyst Lirpa Sloof has recently reported rumors are circulating that Larry Ellison of Oracle is planning on announcing the first Cloud-Subterranean hybrid computing platform at the Oracle OpenWorld 2010 conference, which is also rumored to be changing its venue from San Francisco to Spiggot.

But whenever you're evaluating new technology, remember the wise words from Subterranean Homesick Blues by Bob Dylan:

“You don’t need a weatherman to know which way the wind blows.”

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Wordless Wednesday: March 31, 2010

Photo via Twitpic by: Grant Imahara of MythBusters

The Poor Data Quality Jar

Today I am pondering whether or not the venerable tradition of The Swear Jar could be adapted to help organizations illustrate the costs of poor data quality.

As an example for those unfamiliar with the concept, my family used a swear jar when I was growing up.  Anytime a family member swore (i.e., used profanity), they put an amount of money into the jar based on the severity of the swear.

Of course in my family, what exactly constituted “profanity” as well as what the severity of a particular swear should be would often cause considerable debate, which somewhat ironically lead to the increased use of unquestionable profanity.

Therefore, The Swear Jar was far from a perfect system (at least for my family). 

But I am still imaging every organization instituting The Poor Data Quality Jar.

When an employee contributes to the organization's poor data quality, they put an amount of money into the jar based on the severity of the data quality issue, and perhaps with an increasing scale to be more punitive to repeat offenders.

Do you think The Poor Data Quality Jar can help your organization?  If so, how much would you charge for different types of data quality issues?  How would you determine the severity (i.e., financial impact) of each data quality issue?

Photo via Flickr (Creative Commons License) by: Karen Roe

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

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

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

Wordless Wednesday: March 24, 2010

Recently Read: March 22, 2010

Recently Read is an OCDQ regular segment.  Each entry provides links to blog posts, articles, books, and other material I found interesting enough to share.  Please note “recently read” is literal – therefore what I share wasn't necessarily recently published.

Data Quality

For simplicity, “Data Quality” also includes Data Governance, Master Data Management, and Business Intelligence.

• The Data Quality Herald Magazine – Dylan Jones, the founder and community manager of Data Quality Pro, recently released the first edition of a unique new magazine focused squarely on the needs of the data quality community.

• Defining Master Data for Your Organization – Loraine Lawson recaps a recent David Loshin MDM vendor panel discussion, and looks at both the simple answer and the complex, but more useful, answer to the question “what is master data?”

• What is Data Quality anyway? – Henrik Liliendahl Sørensen asks two excellent questions in this blog post (which also received great comments): “is data quality an independent discipline?” and “is data quality an independent technology?”

• Business logic – Peter Thomas provides a hilarious adapted comic strip.

• Police Untelligence – from IQTrainwrecks.com, which is provided by the IAIDQ, read the story about the home of an elderly Brooklyn couple that has been raided by the New York City Police Department 50 times over the last 4 years.

• Julian Schwarzenbach on Implementation Challenges – in his Technology Today podcast, Phil Simon discusses implementation challenges with Julian Schwarzenbach, including the relationship between data quality and technology.

• Metadata and 3-D Glasses – David Loshin explains the data governance, data stewardship, and metadata/harmonization albatross hanging around the neck of the common question “what is the definition of ‘customer’?”

• No Enterprise wide Data Model – Ken O’Connor continues his excellent series about common enterprise wide data governance issues with this entry about the impact of not having an enterprise wide data model.

• Putting data on the web – Rich Murnane shares an excellent recent TED video by Tim Berners-Lee showing some of the benefits of shared data on the web.

• How Are You Creating a Pull for Data Quality in Your Organization? – Dylan Jones provides two brief case studies contrasting the “push” and “pull” approaches to getting the organization engaged in a data quality improvement initiative.

• Building your Data Governance Board – Marty Moseley continues his overview of agile data governance by discussing how you select a data governance board, and how you establish data governance priorities.

• Staring at the Lights: Your Data Warehouse Isn’t a Commodity – Evan Levy explains how far too many data warehouse development teams have completely lost sight of their success metrics—and have become entirely focused on loading data.

• The Change Paradox – Carol Newcomb examines the “change is good, but change is bad” paradox often encountered in consulting when recommended new technology or new methodology conflicts with your client's corporate culture.  

• Data Quality Non-Believers – Phil Simon takes on the data quality non-believers making “dataless decisions” by relying on gut instincts to explain such things as customer churn, employee turnover, and intelligent spending of corporate funds. 

• Data Cleansing to Achieve Information Quality – Jackie Roberts raises some interesting questions regarding the efforts needed to cleanse data though multiple stages of analytics and processes to achieve appropriate information quality.

• Data Quality Principles within the PMO – Phil Wright provides a list of six excellent principles that must be met in order to help embed a culture of data quality, data assurance, and data governance within each new project.

• Reduce Business Intelligence cost through better data migration – James Standen examines the challenges of cost control on business intelligence initiatives—and as usual, his blog post includes some great satirical cartoons.

• Is computer analysis accurate? – Julian Schwarzenbach considers the accuracy of computer analysis in decision making, especially automated decision making that attempts to mimic human logic, intuition, and insight.

Related Posts

Recently Read: March 6, 2010

Recently Read: January 23, 2010

Recently Read: December 21, 2009

Recently Read: December 7, 2009

Recently Read: November 28, 2009

Recently Read Resources

Data Quality via My Google Reader

Blogs about Data Quality, Data Governance, Master Data Management, and Business Intelligence

Books about Data Quality, Data Governance, Master Data Management, and Business Intelligence

Wordless Wednesday: March 17, 2010

Photo via Flickr (Creative Commons License) by: Stéfan

Social Karma (Part 8)

This post is the conclusion of a series about the art of effectively using social media in business, which is an essential strategy 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.

Social Karma: The Art of Effectively Using Social Media in Business

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

To download the presentation as an Adobe Acrobat Document (.pdf file) click on this link: Social Karma Presentation 

The Complete Series

Social Karma (Part 1) – Series Introduction

Social Karma (Part 2) – Social Media Preparation

Social Karma (Part 3) – Listening Stations, Home Base, and Outposts

Social Karma (Part 4) – Blogging Best Practices

Social Karma (Part 5) – Connection, Engagement, and ROI Basics

Social Karma (Part 6) – Social Media Books

Social Karma (Part 7) – Twitter 

Social Karma (Part 7)

In Part 6 of this series:  We discussed some of the books that have been the most helpful to my social media education.

In Part 7, we will discuss some recommended best practices and general guidelines for using Twitter.

Frosted Cheerios are Yummy

In social media, one of the most common features is some form of microblogging or short message service (SMS) that allows users to share brief status updates.  Twitter is currently built on only this feature and uses status updates (referred to as tweets) that are limited to a maximum of 140 characters, which at first glance may appear to indicate an obvious limitation. 

Twitter is a rather pithy platform that many people argue is incompatible with meaningful communication, especially of a professional nature.  Most people who have never (as well as some who have) tried it, assume Twitter is a source of nothing but inane babble such as what its users are eating for breakfast.  I must admit that this was my opinion as well—at least at first.

However, Twitter is not only one of the most popular microblogging and social networking services, but if used effectively, it can easily become one of the most powerful weapons in your social media arsenal.

Twitter as Research

In addition to a listening station and an outpost (concepts discussed in Part 2 and Part 3), I use Twitter as a research tool.

Twitter provides near real-time updates about my online community and my areas of professional interest.  For example, the above tweet alerted me to an excellent LinkedIn discussion about the business benefits of master data management (MDM).

I chose this particular tweet in order to clarify an important distinction about Twitter.

Unlike other social networking services, you do not need an account on Twitter for read-only access to its content, which means that anyone could have seen this tweet.  (Of course, Twitter does provide privacy options for both tweets and accounts).

However, in order to click on the URL in this tweet and read the discussion from the Master Data Management Interest Group, you would require both an account on LinkedIn and need your group membership request approved by the group's owner.

Therefore, because it's not a “walled garden” you could leverage Twitter as a listening station only without creating an account.

With or without an account, Twitter Search provides the ability to search for relevant content.  Tweets often include embedded search terms called “hashtags” since they are prefaced with the hash (#) symbol.  You can also save search queries as RSS feeds.

If you are not familiar with how to use it, then check out my video tutorial by following this link:  Twitter Search Tutorial

Twitter as Social Networking

As we discussed in Part 5, the difference between connection and engagement is going beyond simply establishing a presence and achieving active participation within the online community.

Active participation can take on many different forms.  However, as we also discussed, “social media is not about you.”

A focus on helping others is what separates social networking from (especially shameless) self-promotion. 

In the example above, I was helping a fellow Twitter user promote his new blog.  However, conversations are better examples of social networking—and not just on Twitter.  Tweets between users can be public or private (referred to as direct messages). 

As with any public conversation, you should use extreme caution and avoid sharing any sensitive or confidential information.

The Art of the Re-Tweet

Re-tweeting is the act of “forwarding” another user's tweet.  Many bloggers use Twitter to promote their content by tweeting links to their new blog posts.  Therefore, many re-tweets are attempts to share this content with your online community.

A simple re-tweet is easy to do.  However, a few recommended best practices include the following:

• Make your re-tweets (and tweets) re-tweetable by leaving enough unused characters to prevent truncation on re-tweet, which is important since a link is usually at or near the end of the message and truncation would send a broken link
• If you are re-tweeting a link, verify that the link is neither broken nor spam—and if you're not sure, then don't re-tweet it
• If the tweet uses a URL shortener (e.g., a bit.ly link), then reuse it since the user may be relying on its associated analytics
• Space permitting, add relevant hashtags to the re-tweet to make it more compatible with related Twitter searches
• Prove that you're not a robot by providing a meaningful description of what you're re-tweeting (as in the above example)

Following, Followers, and Lists

The Twitter term for connecting with other users is “following.”  Unlike other social networking services, Twitter is not permission based, which means connections do not have to be first requested and then approved.

This creates two different perspectives on your Twitter world—those following you and those you are following.

Unless you only follow a few people, it is a tremendous challenge to actually follow every user you follow.  Twitter Search as well as tools and services (see below) can help with making following a more manageable activity.  Twitter also has a list feature that helps organize the users you are following—and you can follow the lists created by other users.

However, as we discussed in Part 5, social media is not a popularity contest.  Therefore, Twitter is not about the quantity of followers you are able to collect and count, but instead the quality of relationships you are able to form and maintain.

Twitter Tools and Services

Twitter tools and services that I personally use (listed in no particular order):

• TweetDeck – Connecting you with your contacts across Twitter, Facebook, and LinkedIn
• Digsby – Digsby = Instant Messaging (IM) + E-mail + Social Networks
• HootSuite – The professional Twitter client
• Twitterfeed – Feed your blog to Twitter
• TweetMeme – Add a Retweet Button to your blog
• Ping.fm – Update all of your social networks at once 

“Thanks”

I haven't performed the actual analysis, but I am willing to bet the word that appears most often in my tweets is: “Thanks”

I named this series Social Karma for a reason—beyond simply being a cute pun for social media.

I view the “Social” in Social Karma as the technical variable in the social media equation.  Social is the strategy for accomplishing our goals, the creation of our own content, the effective use of the tools—the technology. 

I view the “Karma” in Social Karma as the human variable in the social media equation.  Karma is the transparency of our intentions, the appreciation of the content created by others, the sharing of ourselves—our humanity.

The most important variable in the social media equation is the human variable. 

In other words, I want to say thanks to all of you for being the most important aspect of my social media experience.

In Part 8 of this series:  The series concludes with my Social Karma presentation for Enterprise Data World 2010.

Related Posts

Social Karma (Part 1) – Series Introduction

Social Karma (Part 2) – Social Media Preparation

Social Karma (Part 3) – Listening Stations, Home Base, and Outposts

Social Karma (Part 4) – Blogging Best Practices

Social Karma (Part 5) – Connection, Engagement, and ROI Basics

Social Karma (Part 6) – Social Media Books

The Wisdom of the Social Media Crowd

The Twitter Clockwork is NOT Orange

Video: Twitter Search Tutorial

Live-Tweeting: Data Governance

Brevity is the Soul of Social Media

If you tweet away, I will follow

Wordless Wednesday: March 10, 2010

Photo via Flickr (Creative Commons License) by: Skip The Budgie

Data Quality Mad Libs (Part 2)

Data Quality Mad Libs is an ongoing OCDQ series.

For the uninitiated, Mad Libs are sentences with several of their key words or phrases left intentionally blank.

Next to each blank is indicated what type of word should be entered, but you get to choose the actual words.

The completed sentence can be as thought-provoking, comical, or nonsensical as you want to make it.

Data Quality Mad Lib

“If you want to

_______________ (verb or phrase)

your

_______________ (noun or phrase)

initiative, then

_______________ (verb)

your

_______________ (noun or phrase)

that

_______________ (phrase)

is highly recommended.”

My Version

“If you want to doom your data quality initiative, then advise your technical stakeholders that ignoring the business context is highly recommended.”

Share Your Version

Post a comment below and share your completed version of this Data Quality Mad Lib.

Related Posts

Data Quality Mad Libs (Part 1)