The Two U’s and the Three C’s

The question that I get asked most frequently about blogging is:

“Is there a simple formula for writing effective blog posts?”

And the only honest answer is:

“NO!  There is NOT a simple formula for writing effective blog posts.”

Well, okay . . . according to conventional blogging wisdom . . . maybe there is one simple formula:

BU2C3.png

The Two U’s

The first aspect of conventional blogging wisdom is to follow the Two U’s:

  1. Useful – Focus on your reader and provide them assistance with a specific problem

  2. Unique – Capture your reader’s attention and share your perspective in your own voice

Blogging is all about you.  No, not you meaning me, the blogger — you meaning you, the reader.

To be useful, blogging has to be all about the reader.  If you write only for yourself, then you will also be your only reader.

Useful blog posts often provide “infotainment” — a combination of information and entertainment — that, when it’s done well, can turn readers into raving fans.  Just don’t forget—your blog content has to be informative and entertaining to your readers.

One important aspect of being unique is writing effective titles.  Most potential readers scan titles to determine if they will click and read more.  There is a delicate balance between effective titles and “baiting” – which will only alienate potential readers.

If you write a compelling title that makes your readers click through to an interesting post, then “You Rock!”  However, if you write a “Shock and Awe” title followed by “Aw Shucks” content, then “You Suck!”

Your blog content also has to be unique—your topic, position, voice, or a combination of all three.

Consider the following when striving to write unique blog posts:

  • The easiest way to produce unique content is to let your blogging style reflect your personality

  • Don’t be afraid to express your opinion—even on subjects where it seems like “everything has already be said”

  • Your opinion is unique—because it is your opinion

  • An opinion—as long as it is respectfully given—is never wrong

  • Consistency in both style and message is important, however it’s okay to vary your style and/or change your opinion

The Three C’s

The second aspect of conventional blogging wisdom is to follow the Three C’s:

  1. Clear – Get to the point and stay on point

  2. Concise – No longer than absolutely necessary

  3. Consumable – Formatted to be easily read on a computer screen

Clear blog posts typically have a single theme or one primary topic to communicate.  Don’t run off on tangents, especially ones not related to the point you are trying to make.  If you have several legitimate sub-topics to cover, then consider creating a series.

Concise doesn’t necessarily mean “write really short blog posts.”  There is no specific word count to target.  Being concise simply means taking out anything that doesn’t need to be included.  Editing is the hardest part of writing, but also the most important.

Consumable content is extremely essential when people are reading off of a computer screen.

Densely packed text attacks the eyes, which doesn’t encourage anyone to keep reading.

Consumable blog posts effectively use techniques such as the following:

  • Providing an introduction and/or a conclusion

  • Using section headings (in a larger size or different font or both)

  • Varying the lengths of both sentences and paragraphs

  • Highlighting key words or phrases using bold or italics

  • Making or summarizing keys points in a short sentence or a short paragraph

  • Making or summarizing key points using numbered or bulleted lists

As a general rule, the longer (although still both clear and concise) the blog post, the more consumable you need to make it.

Conclusion

If writing is not your thing, and you’re podcasting or video blogging or using some combination of all three (and that’s another way to be unique), I still think the conventional blogging wisdom applies, which, of course, you are obviously free to ignore since blogging is definitely more art than science.

However, I recommend that you first learn and practice the conventional blogging wisdom.

After all, it’s always more fun to break the rules when you actually know what the rules are.

#FollowFriday Spotlight: @hlsdk

FollowFriday Spotlight is an OCDQ regular segment highlighting someone you should follow—and not just Fridays on Twitter.

Henrik Liliendahl Sørensen is a data quality and master data management (MDM) professional with over 30 years of experience in the information technology (IT) business working within a large range of business areas, such as government, insurance, manufacturing, membership, healthcare, and public transportation.

For more details about what Henrik has been, and is, working on, check out his My Been Done List and 2011 To Do List.

Henrik is also a charter member of the IAIDQ, and the creator of the LinkedIn Group for Data Matching for people interested in data quality and thrilled by automated data matching, deduplication, and identity resolution.

Henrik is one of the most prolific and popular data quality bloggers, regularly sharing his excellent insights about data quality, data matching, MDM, data architecture, data governance, diversity in data quality, and many other data management topics.

So check out Liliendahl on Data Quality for great blog posts written by Henrik Liliendahl Sørensen, such as these popular posts:

 

Related Posts

Delivering Data Happiness

#FollowFriday Spotlight: @DataQualityPro

#FollowFriday and Re-Tweet-Worthiness

#FollowFriday and The Three Tweets

Dilbert, Data Quality, Rabbits, and #FollowFriday

Twitter, Meaningful Conversations, and #FollowFriday

The Fellowship of #FollowFriday

Social Karma (Part 7) – Twitter

Connect Four and Data Governance

Connect Four was one of my favorite childhood games (I grew up in the early 1970s before video games and home computers).

The object of the game was to connect four of your checkers next to each other either vertically, horizontally, or diagonally, before your opponent could do the same with their checkers.  Hours of fun for ages 7 and up, as Milton Bradley would say.

Data Governance has its own version of Connect Four.

The central concept of data governance is its definition, implementation, and enforcement of policies, which connect four factors:

  1. People
  2. Business Process
  3. Technology
  4. Data

Data governance policies govern the complex interactions among people, business processes, technology, and data, which is a corporate asset because high quality data serves as a solid foundation for an organization’s success, empowering people, enabled by technology, to optimize business processes for superior business performance.

Connecting all four of these factors both vertically (within each business unit) and horizontally (across every business unit) is the only winning strategy for long-term success.

Data governance is not as simple (or as fun) as a board game, but if your data governance board doesn’t play Connect Four, then it could be Game Over for much more than just your data governance program:

Photo via Flickr by: Jeff Golden

 

Related Posts

Data Governance and the Social Enterprise

Podcast: Data Governance is Mission Possible

Quality and Governance are Beyond the Data

Video: Declaration of Data Governance

The Diffusion of Data Governance

Jack Bauer and Enforcing Data Governance Policies

The Prince of Data Governance

MacGyver: Data Governance and Duct Tape

Quality is more important than Quantity

Quantity/Quality lists shown above are from my social media presentation, which you can download by clicking on this link: Social Karma Presentation

Effectively using social media in a business context requires a significant commitment—mostly measured in time.

Since the “opportunity cost” of social media can be quite high, many understandably argue about how to effectively measure its return on investment (ROI), which can often feel like you are searching for the Sasquatch or the Loch Ness Monster.

No, social media ROI is not an urban myth.

However, the Albert Einstein quote “not everything that can be counted counts, and not everything that counts can be counted” is relevant to social media ROI because quality is more important than quantity—and quality is also more difficult to measure.

Social media ROI is not measured in followers, fans, recommendations, subscribers, comments or other feedback.  Although this quantitative analysis is useful and its metrics can be meaningful, it is important to realize that this only measures connection.

Qualitative analysis is more challenging because it attempts to measure your engagement with the online community.

Engagement is about going beyond establishing a presence and achieving active participation.  Engagement is measured by the quality of the relationships you are able to form and maintain—not the quantity of connections you are able to collect and count.

Although both quantitative and qualitative analysis are essential to forming a complete measurement of your social media ROI, quality is more important than quantity, because engagement is more important than connection.

Engagement requires a long-term investment in the community, but if you’re not willing to make a long-term investment, then don’t expect any meaningful returns from your social media efforts.

 

Don’t Ignore “The Man Behind the Curtain”

 

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

 

Related Posts

The Challenging Gift of Social Media

Listening and Broadcasting

Please don’t become a zombie in 2011

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

What Data Quality Technology Wants

This is a screen capture of the results of last month’s unscientific data quality poll where it was noted that viewpoints about the role of data quality technology (i.e., what data quality technology wants) are generally split between two opposing perspectives:

  1. Technology enables a data quality process, but doesn’t obviate the need for people (e.g., data stewards) to remain actively involved and be held accountable for maintaining the quality of data.
  2. Technology automates a data quality process, and a well-designed and properly implemented technical solution obviates the need for people to be actively involved after its implementation.

 

Commendable Comments

Henrik Liliendahl Sørensen voted for enable, but commented he likes to say enables by automating the time consuming parts, an excellent point which he further elaborated on in two of his recent blog posts: Automation and Technology and Maturity.

Garnie Bolling commented that he believes people will always be part of the process, especially since data quality has so many dimensions and trends, and although automated systems can deal with what he called fundamental data characteristics, an automated system can not change with trends or the ongoing evolution of data.

Frank Harland commented that automation can and has to take over the tedious bits of work (e.g., he wouldn't want to type in all those queries that can be automated by data profiling tools), but to get data right, we have to get processes right, get data architecture right, get culture and KPI’s right, and get a lot of the “right” people to do all the hard work that has to be done.

Chris Jackson commented that what an organization really needs is quality data processes not data quality processes, and once the focus is on treating the data properly rather than catching and remediating poor data, you can have a meaningful debate about the relative importance of well-trained and motivated staff vs. systems that encourage good data behavior vs. replacing fallible people with standard automated process steps.

Alexa Wackernagel commented that when it comes to discussions about data migration and data quality with clients, she often gets the requirement—or better to call it the dream—for automated processes, but the reality is that data handling needs easy accessible technology to enable data quality.

Thanks to everyone who voted and special thanks to everyone who commented.  As always, your feedback is greatly appreciated.

 

What Data Quality Technology Wants: Enable and Automate


“Data Quality Powers—Activate!”


“I’m sorry, Defect.  I’m afraid I can’t allow that.”

I have to admit that my poll question was flawed (as my friend HAL would say, “It can only be attributable to human error”).

Posing the question in an either/or context made it difficult for the important role of automation within data quality processes to garner many votes.  I agree with the comments above that the role of data quality technology is to both enable and automate.

As the Wonder Twins demonstrate, data quality technology enables Zan (i.e., technical people), Jayna (i.e., business people), and  Gleek (i.e., data space monkeys, er I mean, data people) to activate one of their most important powers—collaboration.

In addition to the examples described in the comments above, data quality technology automates proactive defect prevention by providing real-time services, which greatly minimize poor data quality at the multiple points of origin within the data ecosystem, because although it is impossible to prevent every problem before it happens, the more control enforced where data originates, the better the overall enterprise data quality will be—or as my friend HAL would say:

“Putting data quality technology to its fullest possible use is all any corporate entity can ever hope to do.”

Related Posts

What Does Data Quality Technology Want?

DQ-Tip: “Data quality tools do not solve data quality problems...”

Which came first, the Data Quality Tool or the Business Need?

Data Quality Industry: Problem Solvers or Enablers?

Data Quality Magic

The Tooth Fairy of Data Quality

Data Quality is not a Magic Trick

Do you believe in Magic (Quadrants)?

Pirates of the Computer: The Curse of the Poor Data Quality

DQ-BE: Single Version of the Time

Data Quality By Example (DQ-BE) is an OCDQ regular segment that provides examples of data quality key concepts.

Photo via Flickr by: Leo Reynolds

Like truth, beauty, and singing ability, data quality is in the eyes of the beholder.

Data’s quality is determined by evaluating its fitness for the purpose of use.  However, in the vast majority of cases, data has multiple uses, and data of sufficient quality for one use may not be of sufficient quality for other uses.

Therefore, to be more accurate, data quality is in the eyes of the user.

The perspective of the user provides a relative context for data quality.  Many argue an absolute context for data quality exists, one which is independent of the often conflicting perspectives of different users.

This absolute context is often referred to as a “Single Version of the Truth.”

As one example of the challenges inherent in this data quality key concept, let’s consider if there is a “Single Version of the Time.”

 

Single Version of the Time

I am writing this blog post at 10:00 AM.  I am using time in a relative context, meaning that from my perspective it is 10 o’clock in the morning.  I live in the Central Standard time zone (CST) of the United States. 

My friend in Europe would say that I am writing this blog post at 5:00 PM.  He is also using time in a relative context, meaning that from his perspective it is 5 o’clock in the afternoon.  My friend lives in the Central European time zone (CET).

We could argue that an absolute time exists, as defined by Coordinated Universal Time (UTC).  Local times around the world can be expressed as a relative time using positive or negative offsets from UTC.  For example, my relative time is UTC-6 and my friend’s relative time is UTC+1.  Alternatively, we could use absolute time and say that I am writing this blog post at 16:00 UTC.

Although using an absolute time is an absolute necessity if, for example, my friend and I wanted to schedule a time to have a telephone (or Skype) discussion, it would be confusing to use UTC when referring to events relative to our local time zone.

In other words, the relative context of the user’s perspective is valid and an absolute context independent of the perspectives of different users is also valid—especially whenever a shared perspective is necessary in order to facilitate dialogue and discussion.

Therefore, instead of calling UTC a Single Version of the Time, we could call it a Shared Version of the Time and when it comes to the data quality concept of a Single Version of the Truth, perhaps it’s time we started calling it a Shared Version of the Truth.

 

Related Posts

Single Version of the Truth

The Quest for the Golden Copy

Beyond a “Single Version of the Truth”

The Idea of Order in Data

DQ-BE: Data Quality Airlines

DQ-Tip: “There is no such thing as data accuracy...”

Data Quality and the Cupertino Effect

DQ-Tip: “Data quality is primarily about context not accuracy...”

Listening and Broadcasting

Photo via Flickr by: Colleen AF Venable

Photo via Flickr by: Anders Pollas

As we continue to witness the decline of traditional media and the corresponding rise of social media, the business world is attempting to keep up with the changing times.  Many organizations are “looking to do whatever it is that’s intended to replace advertising,” explained Douglas Rushkoff in a recent blog post about how marketing threatens the true promise of social media by “devolving to the limited applications of social marketing” and trying to turn the “social landscape back into a marketplace.”

We can all relate to Rushkoff’s central concern—the all-too-slippery slope separating social networking from social marketing.

The primary reason I started blogging was to demonstrate my expertise and establish my authority with regards to data quality and its related disciplines.  As an independent consultant, I am trying to help sell my writing, speaking, and consulting services.

You and/or your company are probably using social media to help sell your products and services as well.

Effective social networking is about community participation, which requires actively listening, inviting others to get involved, sharing meaningful ideas, contributing to conversations—and not just broadcasting your sales and marketing messages.

An often cited reason for the meteoric rise of social media is its exchange of a broadcast medium for a conversation medium.  However, some people, including Mitch Joel and Jay Baer, have pondered whether social media conversations are a myth.

“One of the main tenets of social media,” Joel blogged, “was the reality that brands could join a conversation, but by the looks of things there aren’t really any conversations happening at all.” 

Joel wasn’t being negative, just observational.  He pointed out that most blog comments provide feedback, not a back and forth conversation between blogger and reader, Twitter “feels more like everyone screaming a thought at once than a conversation that can be followed and engaged with” and “Facebook has some great banter with the wall posts and status updates, but it’s more chatty than conversational and it’s not an open/public environment.”

“To expect social media to truly emulate conversation as we know it is a fools errand,” Baer blogged.  “The information exchange is asynchronous.  However, there’s a difference between striving for conversation and settling for broadcasting.  The success path must lie somewhere in the middle of those two boundaries.”

Regardless of how we are striving for conversation, whether it be blogging, tweeting, Facebooking, or a face-to-face discussion, we must remember the importance of empathically listening to each other—and not just waiting for our turn to broadcast.

An effective social media strategy is essential for organizations as well as individual professionals, but it is a constant struggle to find the right balance between the headphones and the bullhorn—between listening and broadcasting.

 

Related Posts

Social Karma (Part 2) – Social Media Preparation

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

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

Please don’t become a zombie in 2011

Twitter, Meaningful Conversations, and #FollowFriday

The Importance of Envelopes

The Challenging Gift of Social Media

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

#FollowFriday Spotlight: @DataQualityPro

FollowFriday Spotlight is an OCDQ regular segment highlighting someone you should follow—and not just Fridays on Twitter.

Links for Data Quality Pro and Dylan Jones:

Data Quality Pro, founded and maintained by Dylan Jones, is a free and independent community resource dedicated to helping data quality professionals take their career or business to the next level.  Data Quality Pro is your free expert resource providing data quality articles, webinars, forums and tutorials from the world’s leading experts, every day.

With the mission to create the most beneficial data quality resource that is freely available to members around the world, the goal of Data Quality Pro is “winning-by-sharing” and they believe that by contributing a small amount of their experience, skill or time to support other members then truly great things can be achieved.

Membership is 100% free and provides a broad range of additional content for professionals of all backgrounds and skill levels.

Check out the Best of Data Quality Pro, which includes the following great blog posts written by Dylan Jones in 2010:

 

Related Posts

#FollowFriday and Re-Tweet-Worthiness

#FollowFriday and The Three Tweets

Dilbert, Data Quality, Rabbits, and #FollowFriday

Twitter, Meaningful Conversations, and #FollowFriday

The Fellowship of #FollowFriday

Social Karma (Part 7) – Twitter

DQ-View: New Data Resolutions

Data Quality (DQ) View is an OCDQ regular segment.  Each DQ-View is a brief video discussion of a data quality key concept.

 

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: DQ-View on Vimeo

The graphics shown in the video were created under a Creative Commons Attribution License using: Wordle

 

New Data Resolutions

If one of your New Year’s Resolutions was not to listen to my rambling, here is the video’s (spoiler alert!) thrilling conclusion:

Now, of course, in order for this to truly count as one of your New Data Resolutions for 2011, you will have to provide your own WHY and WHAT that is specific to your organization’s enterprise data initiative.

After all, it’s not like I can eat healthier or exercise more often for you in 2011.  Happy New Year!

 

Related Posts

“Some is not a number and soon is not a time”

Common Change

Video: Declaration of Data Governance

DQ View: Achieving Data Quality Happiness

Don’t Do Less Bad; Do Better Good

Data Quality is not a Magic Trick

DQ-View: Designated Asker of Stupid Questions

DQ-View: The Cassandra Effect

DQ-View: From Data to Decision

Video: Oh, the Data You’ll Show!

Please don’t become a zombie in 2011

If one of your New Year’s Resolutions is to start a blog, please be forewarned that the blogosphere has a real zombie problem.

No, not that kind of zombie.

“Zombie” is a slang term used to describe a blog that has stopped publishing new posts.  In other words, the blog has joined the Blogosphere of the Living Dead, which is comprised of blogs that still have a valid URL, but desperately crave new “Posts!”

 

It’s Not Personal—Zombies are Professional

If you’re considering starting a personal blog (especially one about “real zombies”), then please stop reading—and start blogging.

However, if you’re considering starting a professional blog, then please continue reading.  By a “professional blog” I do not mean a blog that makes money.  I simply mean a blog that’s part of the social media strategy for your organization or a blog that helps advance your professional career—which, yes, may also directly or (far more likely, if at all) indirectly make you money.

If you are seriously considering starting a professional blog, before you do anything else, complete the 20-10-5 plan.

 

The 20-10-5 Plan

  • Brainstorm 20 high level ideas for blog posts
  • Write 10 rough drafts based on those ideas
  • Finish 5 ready-to-publish posts from those drafts

If you are unable to complete this simple plan, then seriously reconsider starting a professional blog.

Please Note: I will add the caveat that if writing is not your thing, and you’re planning on podcasting or video blogging instead, I still adamantly believe you must complete the 20-10-5 plan.  In essence, the plan is simply a challenge to see if you can create five pieces of ready-to-publish content—BEFORE you launch your professional blog, since IMHO—if you can’t, then don’t.

 

Recommended Next Steps

If you completed the 20-10-5 plan, then after you launch your blog, consider the following recommendations:

  • Do not post more than once a week
  • Maintain an editorial calendar and schedule your future posts
  • Finish more ready-to-publish posts (you’re good until Week 6 because of the 20-10-5 plan)

Yes, you’ll be tempted to start posting more than once a week.  Yes, you’ll be eager to share your brilliance with the blogosphere.

However, just like many new things, blogging is really fun—when it’s new.

So let’s run the numbers:

  • Posting once a week = 52 blog posts a year
  • Posting twice a week = 104 blog posts a year
  • Posting five times a week (basically once every weekday) = 260 blog posts a year

I am not trying to harsh your mellow.  I am simply saying that you need to pace yourself—especially at the beginning.

 

I am not a Zombie—or a Social Media Expert

I am not a “social media expert.”  In fact, until late 2008, I wasn’t even interested enough to ask people what they meant when I heard them talking about “social media.”  I started blogging, tweeting, and using other social media in early 2009.

Do I practice what I preach?  Check my archives.

My blog was started in March 2009.  I published 5-8 posts per month (1-2 posts per week) for each of the first five months, and then I gradually increased my posting frequency.  Now, almost two years later, I have published 236 posts on this blog, which is an overall average of 10 posts per month (2-3 posts per week), without ever posting fewer than 5 times in one month.

So if you do decide to become a blogger, please don’t become a zombie in 2011—wait until the Zombie Apocalypse of 2012 :-)

 

Related Posts

Social Karma (Part 4) – Blogging Best Practices

The Mullet Blogging Manifesto

Collablogaunity

Brevity is the Soul of Social Media

Podcast: Your Blog, Your Voice

The Challenging Gift of Social Media

The Wisdom of the Social Media Crowd

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

The Best Data Quality Blog Posts of 2010

This year-end review provides summaries of and links to The Best Data Quality Blog Posts of 2010.  Please note the following:

  • For simplicity, “Data Quality” also includes Data Governance, Master Data Management, and Business Intelligence
  • Intentionally excluded from consideration were my best blog posts of the year — not counting that shameless plug :-)
  • The Data Roundtable was also excluded since I already published a series about its best 2010 blog posts (see links below)
  • Selection was based on a pseudo-scientific, quasi-statistical, and proprietary algorithm (i.e., I just picked the ones I liked)
  • Ordering is based on a pseudo-scientific, quasi-statistical, and proprietary algorithm (i.e., no particular order whatsoever)

 

The Best Data Quality Blog Posts of 2010

  • Data Quality is a DATA issue by Graham Rhind – Expounds on the common discussion about whether data quality is a business issue or a technical issue by explaining that although it can sometimes be either or both, it’s always a data issue.
  • Bad word?: Data Owner by Henrik Liliendahl Sørensen – Examines how the common data quality terms “data owner” and “data ownership” are used, whether they are truly useful, and generated an excellent comment discussion about ownership.
  • Predictably Poor MetaData Quality by Beth Breidenbach – Examines whether data quality and metadata quality issues stem from the same root source—human behavior, which is also the solution to these issues since technology doesn’t cause or solve these challenges, but rather, it’s a tool that exacerbates or aids human behavior in either direction.
  • WANTED: Data Quality Change Agents by Dylan Jones – Explains the key traits required of all data quality change agents, including a positive attitude, a willingness to ask questions, innovation advocating, and persuasive evangelism.
  • Profound Profiling by Daragh O Brien – Discusses the profound business benefits of data profiling for organizations seeking to manage risk and ensure compliance, including the sage data and information quality advice: “Profile early, profile often.”
  • The Importance of Scope in Data Quality Efforts by Jill Dyché – Illustrates five levels of delivery that can help you quickly establish the boundaries of your initial data quality project, which will enable you to implement an incremental approach for your sustained data quality program that will build momentum to larger success over time.
  • The Myth about a Myth by Henrik Liliendahl Sørensen – Debunks the myth that data quality (and a lot of other things) is all about technology — and it’s certainly no myth that this blog post generated a lengthy discussion in the comments section.
  • Definition drift by Graham Rhind – Examines the persistent problems facing attempts to define a consistent terminology within the data quality industry for concepts such as validity versus accuracy, and currency versus timeliness.
  • Data Quality: A Philosophical Approach to Truth by Beth Breidenbach – Examines how the background, history, and perceptions we bring to a situation, any situation, will impact what we perceive as “truth” in that moment, and we don’t have to agree with another’s point of view, but we should at least make an attempt to understand the logic behind it.
  • What Are Master Data? by Marty Moseley of IBM Initiate – Defines the differences between reference data and master data, providing examples of each, and, not surprisingly, this blog post also sparked an excellent discussion within its comments.
  • Data Governance Remains Immature by Rob Karel – Examines the results of several data governance surveys and explains how there is a growing recognition that data governance is not — and should never have been — about the data.
  • The Future – Agile Data-Driven Enterprises by John Schmidt on Informatica Perspectives – Concludes a seven-part series about data as an asset, which examines how successful organizations manage their data as a strategic asset, ensuring that relevant, trusted data can be delivered quickly when, where and how needed to support the changing needs of the business.
  • Data as an Asset by David Pratt – The one where a new guy in the data blogosphere (his blog launched in November 2010) explains treating data as an asset is all about actively doing things to improve both the quality and usefulness of the data.

 

PLEASE NOTE: No offense is intended to any of the great 2010 data quality blog posts not listed above.  However, if you feel that I have made a glaring omission, then please feel free to post a comment below and add it to the list.  Thanks!

I hope that everyone had a great 2010 and I look forward to seeing all of you around the data quality blogosphere in 2011.

 

Related Posts

The 2010 Data Quality Blogging All-Stars

Recently Read: May 15, 2010

Recently Read: March 22, 2010

Recently Read: March 6, 2010

Recently Read: January 23, 2010

 

Additional Resources

From the IAIDQ, read the 2010 issues of the Blog Carnival for Information/Data Quality:

From the Data Roundtable, read the 2010 quarterly review blog series:

I’m Gonna Data Profile (500 Records)

While researching my blog post (to be published on December 31) about the best data quality blog posts of the year, I re-read the great post Profound Profiling by Daragh O Brien, which recounted how he found data profiling cropping up in conversations and presentations he’d made this year, even where the topic of the day wasn’t “Information Quality” and shared his thoughts on the profound business benefits of data profiling for organizations seeking to manage risk and ensure compliance.

And I noticed that I had actually commented on this blog post . . . with song lyrics . . .

 

I’m Gonna Data Profile (500 Records) *

When I wake up, well I know I’m gonna be,
I’m gonna be the one who profiles early and often for you
When I go out, yeah I know I’m gonna be
I’m gonna be the one who goes along with data
If I get drunk, well I know I’m gonna be
I’m gonna be the one who gets drunk on managing risk for you
And if I haver up, yeah I know I’m gonna be
I’m gonna be the one who’s havering about how: “It’s the Information, Stupid!”

But I would profile 500 records
And I would profile 500 more
Just to be the one who profiles a thousand records
To deliver the profound business benefits of data profiling to your door

da da da da – ta ta ta ta
da da da da – ta ta ta ta – data!
da da da da – ta ta ta ta
da da da da – ta ta ta ta – data profiling!

When I’m working, yes I know I’m gonna be
I’m gonna be the one who’s working hard to ensure compliance for you
And when the money, comes in for the work I do
I’ll pass almost every penny on to improving data for you
When I come home (When I come home), well I know I’m gonna be
I’m gonna be the one who comes back home with data quality
And if I grow-old, (When I grow-old) well I know I’m gonna be
I’m gonna be the one who’s growing old with information quality

But I would profile 500 records
And I would profile 500 more
Just to be the one who profiles a thousand records
To deliver the profound business benefits of data profiling to your door

da da da da – ta ta ta ta
da da da da – ta ta ta ta – data!
da da da da – ta ta ta ta
da da da da – ta ta ta ta – data profiling!

When I’m lonely, well I know I’m gonna be
I’m gonna be the one who’s lonely without data profiling to do
And when I’m dreaming, well I know I’m gonna dream
I’m gonna dream about the time when I’m data profiling for you
When I go out (When I go out), well I know I’m gonna be
I’m gonna be the one who goes along with data
And when I come home (When I come home), yes I know I’m gonna be
I’m gonna be the one who comes back home with data quality
I’m gonna be the one who’s coming home with information quality

But I would profile 500 records
And I would profile 500 more
Just to be the one who profiles a thousand records
To deliver the profound business benefits of data profiling to your door

da da da da – ta ta ta ta
da da da da – ta ta ta ta – data!
da da da da – ta ta ta ta
da da da da – ta ta ta ta – data profiling!

___________________________________________________________________________________________________________________

* Based on the 1988 song I’m Gonna Be (500 Miles) by The Proclaimers.

 

Data Quality Music (DQ-Songs)

Over the Data Governance Rainbow

A Record Named Duplicate

New Time Human Business

People

You Can’t Always Get the Data You Want

A spoonful of sugar helps the number of data defects go down

Data Quality is such a Rush

I’m Bringing DQ Sexy Back

Imagining the Future of Data Quality

The Very Model of a Modern DQ General

What Does Data Quality Technology Want?

During a recent Radiolab podcast, Kevin Kelly, author of the book What Technology Wants, used the analogy of how a flower leans toward sunlight because it “wants” the sunlight, to describe what the interweaving web of evolving technical innovations (what he refers to as the super-organism of technology) is leaning toward—in other words, what technology wants.

The other Radiolab guest was Steven Johnson, author of the book Where Good Ideas Come From, who somewhat dispelled the traditional notion of the eureka effect by explaining that the evolution of ideas, like all evolution, stumbles its way toward the next good idea, which inevitably leads to a significant breakthrough, such as what happens with innovations in technology.

Listening to this thought-provoking podcast made me ponder the question: What does data quality technology want?

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

The data quality market continues to evolve away from esoteric technical tools and stumble its way toward the next good idea, which is business-empowering suites providing robust functionality with increasingly role-based user interfaces, which are tailored to the specific needs of different users.  Of course, many vendors would love to claim sole responsibility for what they would call significant innovations in data quality technology, instead of what are simply by-products of an evolving market.

The deployment of data quality functionality within and across organizations also continues to evolve, as data cleansing activities are being complemented by real-time defect prevention services used to greatly minimize poor data quality at the multiple points of origin within the enterprise data ecosystem.

However, viewpoints about the role of data quality technology generally remain split between two opposing perspectives:

  1. Technology enables a data quality process, but doesn’t obviate the need for people (e.g., data stewards) to remain actively involved and be held accountable for maintaining the quality of data.
  2. Technology automates a data quality process, and a well-designed and properly implemented technical solution obviates the need for people to be actively involved after its implementation.

Do you think that continuing advancements and innovations in data quality technology will obviate the need for people to be actively involved in data quality processes?  In the future, will we have high quality data because our technology essentially wants it and therefore leans our organizations toward high quality data?  Let’s conduct another unscientific data quality poll:

 

Additionally, please feel free to post a comment below and explain your vote or simply share your opinions and experiences.

 

Related Posts

DQ-Tip: “Data quality tools do not solve data quality problems...”

Which came first, the Data Quality Tool or the Business Need?

Data Quality Industry: Problem Solvers or Enablers?

Data Quality Magic

The Tooth Fairy of Data Quality

Data Quality is not a Magic Trick

Do you believe in Magic (Quadrants)?

Pirates of the Computer: The Curse of the Poor Data Quality