I have earlier written about how search terms are a way people gets to my blog in the post Picture This.

Another way is being referred from other sources. Lately WordPress, which is my blog service, improved the statistics so the referring sources are consolidated which gives you much more meaningful information about your referrers.

My current all time statistics looks like this:

At the time the total number of pageviews was 46,263.

LinkedIn seems to be my main supplier of readers. I am regularly sharing my posts as status updates and as news items in different LinkedIn groups.

But I do think that the figures for Twitter is lying though as they are counted based on where from the tweets and re-tweets are read. Twitter is probably only the twitter site. Hootsuite is another way of reading and clicking on links to a blog in a tweet. People who read and click via TweetDeck is as I understand it not counted as a referring source as TweetDeck is a desktop application.

Though I write in English I do from time to time post user blogs and comments with links on Danish language sources as the local Computerworld and another IT online news site called Version2.   

When someone, which in my case mainly is Rich Murnane I think, StumblesUpon a blog post you sometimes get a lot of pageviews within an hour or so.

Else Jim Harris’s blog called OCDQ Blog is a constant source of referring either due to Jim’s kind links to my blog posts or my self-promoting links in my comments on Jim’s blog posts.

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My Secret

Yesterday I followed a webinar on DataQualityPro with ECCMA ISO 8000 project leader Peter Benson.

Peter had a lot of good sayings and fortunately Jim Harris as a result of his live tweeting has documented a sample of good quotes here.

My favorite:

“Quality data does NOT guarantee quality information, but quality information is impossible without quality data.”

I have personally conducted an experiment that supports that hypothesis. It goes as this:

First, I found a data file on my computer. Lots of data in there being numbers and letters. And sure, what is interesting is the information I can derive for different purposes.

Then I deleted the data file and tried to see how much information was left behind.

Guess what? Not a bit.

I first published that experiment as a comment to one of Jim’s blog posts: Data Quality and the Cupertino Effect.

As documented in the comments on this blog post the subject of data (quality) versus information (quality) is ever recurring and almost always guarantees a fierce discussion among data/information management professionals.

So, I’ll just tell you this secret: My work in achieving quality information is done by fixing data quality.

And guess what? I have disabled comments on this blog post.

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What’s In a Given Name?

I use the term ”given name” here for the part of a person name that in most western cultures is called a ”first name”.

When working with automation of data quality, master data management and data matching you will encounter a lot of situations where you will like to mimic what we humans do, when we look at a given name.  And when you have done this a few times you also learn the risks of doing so.

Here is some of the learning I have been through:


Most given names are either for males or for females. So most times you instinctively know if it is a male or a female when you look at a name. Probably you also know those given names in your culture that may be both. What often creates havoc is when you apply rules of one culture to data coming from a different culture.  The subject was discussed on DataQualityPro here.


In some cultures salutation is paramount – not at least in Germany. A correct salutation may depend on knowing the gender. The gender may be derived from the given name. But you should not use the given name itself in your greeting.

So writing to “Angela Merkel” will be “Sehr geehrte Frau Merkel” – translates to “Very honored Mrs. Merkel”.

If you have a small mistake as the name being “Angelo Merkel”, this will create a big mistake when writing “Sehr geehrter Herr Merkel” (Very honored Mr. Merkel) to her.


In a recent post on the DataFlux Community of Experts Jim Harris wrote about how he received tons of direct mails assuming he was retired based on where he lives.

I have worked a bit with market segmentation and data (information) quality. I don’t know how it is with first names in the United States, but in Denmark you may have a good probability with estimating an age based on your given name. The statistical bureau provides statistics for each name and birth year. So combining that with the location based demographic you will get a better response rate in direct marketing.


Nicknames are used very different in various cultures. In Denmark we don’t use them that much and definitely very seldom in business transactions. If you meet a Dane called Jim his name is actually Jim. If you have a clever piece of software correcting/standardizing the name to be James, well, that’s not very clever.

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The Next Level

A quote about data quality from Thomas Redman says:

“It is a waste of effort to improve the quality (accuracy) of data no one ever uses.”

I have learned the quote from Jim Harris who mentioned the quote latest in his post: DQ-Tip: “There is no point in monitoring data quality…”

In a comment Phil Simon said: I love that. I’m jealous that I didn’t think of something so smart.

I’m guessing Phil was into some irony. If so, I can see why. The statement seems pretty obvious and at first glance you can’t imagine anyone taking the opposite stance: Let’s cleanse some data no one ever uses.

Also I think it was meant as being obvious in Redman’s book: Data Driven.

Well, taking it to the next level I can think of the following elaboration:

  1. If you found some data that no one ever uses you should not only avoid improving the quality of that data, you should actually delete the data and make sure that no one uses time and resources for entering or importing the same data in the future.
  2. That is unless the reason that no one ever uses the data is that the quality of the data is poor.  Then you must compare the benefits of improving the data against the costs of doing so. If costs are bigger, proceed with point 1. If benefits are bigger, go to point 3.
  3. It is not  a waste of effort to improve the quality of some data no one ever uses.

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Data Quality Tools Revealed

To be honest: Data Quality tools today only solves a very few of the data quality problems you have. On the other hand, the few problems they do solve may be solved very well and can not be solved by any other line of products or in any practically way by humans in any quantity or quality.

Data Quality tools mainly support you with automation of:

• Data Profiling and
• Data Matching

Data Profiling

Data profiling is the ability to generate statistical summaries and frequency distributions for the unique values and formats found within the fields of your data sources in order to measure data quality and find critical areas that may harm your business. For more description on the subject I recommend reading the introduction provided by Jim Harris in his post “Getting Your Data Freq On”, which is followed up by a series of posts on the “Adventures in Data Profiling part 1 – 8”

Saying that you can’t use other product lines for data profiling is actually only partly true. You may come a long way by using features in popular database managers as demonstrated in Rich Murnanes blog post “A very inexpensive way to profile a string field in Oracle”. But for full automation and a full set of out-of-the-box functionality a data profiling tool will be necessary.

The data profiling tool market landscape is – opposite to that of data matching – also characterized by the existence of open source tools. Talend is the leading one of those, another one is DataCleaner created by my fellow countryman Kasper Sørensen.

I take the emerge of open source solutions in the realm of data profiling as a sign of, that this is the technically easiest part of data quality tool invention.

Data Matching

Data matching is the ability to compare records that are not exactly the same but are so similar that we may conclude, that they represent the same real world object.

Also here some popular database managers today have some functionality like the fuzzy grouping and lookup in MS SQL. But in order to really automate data matching processes you need a dedicated tool equipped with advanced algorithms and comprehensive functionality for candidate selection, similarity assignment and survivorship settlement.

Data matching tools are essential for processing large numbers of data rows within a short timeframe for example when purging duplicates before marketing campaigns or merging duplicates in migration projects.

Matching technology is becoming more popular implemented as what is often described as a firewall, where possible new entries are compared to existing rows in databases as an upstream prevention against duplication.

Besides handling duplicates matching techniques are used for correcting postal addresses against official postal references and matching data sets against reference databases like B2B and B2C party data directories as well as matching with product data systems all in order to be able to enrich with and maintain more accurate and timely data.

Automation of matching is in no way straightforward and solutions for that are constantly met with the balancing of producing a sufficient number of true positives without creating just that number of too many false positives.

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Sharing data is key to a single version of the truth

This post is involved in a good-natured contest (i.e., a blog-bout) with two additional bloggers:  Charles Blyth and Jim Harris. Our contest is a Blogging Olympics of sorts, with the Great Britain, United States and Denmark competing for the Gold, Silver, and Bronze medals in an event we are calling “Three Single Versions of a Shared Version of the Truth.”

Please take the time to read all three posts and then vote for who you think has won the debate (see poll below). Thanks!

My take

According to Wikipedia data may be of high quality in two alternative ways:

  • Either they are fit for their intended uses
  • Or they correctly represent the real-world construct to which they refer

In my eyes the term “single version of the truth” relates best to the real-world way of data being of high quality while “shared version of the truth” relates best to the hard work of making data fit for multiple intended uses of shared data in the enterprise.

My thesis is that there is a break even point when including more and more purposes where it will be less cumbersome to reflect the real world object rather than trying to align all known purposes.  

The map analogy

In search for this truth we will go on a little journey around the world.

For a journey we need a map.

Traditionally we have the challenge that the real-world being the planet Earth is round (3 dimensions) but a map shows a flat world (2 dimensions). If a map shows a limited part of the world the difference doesn’t matter that much. This is similar to fitting the purpose of use in a single business unit.

MercatorIf the map shows the whole world we may have all kind of different projections offering different kind of views on the world having some advantages and disadvantages. A classic world map is the rectangle where Alaska, Canada, Greenland, Svalbard, Siberia and Antarctica are presented much larger than in the real-world if compared to regions closer to equator. This is similar to the problems in fulfilling multiple uses embracing all business units in an enterprise.

Today we have new technology coming to the rescue. If you go into Google Earth the world indeed looks round and you may have any high altitude view of a apparently round world. If you go closer the map tends to be more and more flat. My guess is that the solutions to fit the multiple uses conondrum will be offered from the cloud.  

Exploiting rich external reference data

But Google Earth offers more than powerfull technolgy. The maps are connected with rich information on places, streets, companies and so on obtained from multiple sources – and also some crowdsourced photos not always placed with accuracy. Even if external reference data is not “the truth” these data, if used by more and more users (one instance, multiple tenants), will tend to be closer to “the truth” than any data collected and maintained solely in a single enterprise.

Shared data makes fit for pupose information

You may divide the data held by an enterprise into 3 pots:

  • Global data that is not unique to operations in your enterprise but shared with other enterprises in the same industry (e.g. product reference data) and eventually the whole world (e.g. business partner data and location data). Here “shared data in the cloud” will make your “single version of the truth” easier and closer to the real world.
  • Bilateral data concerning business partner transactions and related master data. If you for example buy a spare part then also “share the describing data” making your “single version of the truth” easier and more accurate.    
  • Private data that is unique to operations in your enterprise. This may be a “single version of the truth” that you find superior to what others have found, data supporting internal business rules that make your company more competitive and data referring to internal events.

While private and then next bilateral data makes up the largest amount of data held by an enterprise it is often seen that it is data that could be global that have the most obvious data quality issues like duplicated, missing, incorrect and outdated party master data information.

Here “a global or bilateral shared version of the truth” helps approaching “a single version of the truth” to be shared in your enterprise. This way accurate raw data may be consumed as valuable information in a given context at once when needed.  

Call to action

If not done already, please take the time to read posts from fellow bloggers Charles Blyth and Jim Harris and then vote for who you think has won the debate. A link to the same poll is provided on all three blogs. Therefore, wherever you choose to cast your vote, you will be able to view an accurate tally of the current totals.

The poll will remain open for one week, closing at midnight on 19th November so that the “medal ceremony” can be conducted via Twitter on Friday, 20th November. Additionally, please share your thoughts and perspectives on this debate by posting a comment below.  Your comment may be copied (with full attribution) into the comments section of all of the blogs involved in this debate.

Vote here.

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Master Data Survivorship

A Master Data initiative is often described as making a “golden view” of all Master Data records held by an organization in various databases used by different applications serving a range of business units.

In doing that (either in the initial consolidation or the ongoing insertion and update) you will time and again encounter situations where two versions of the same element must be merged into one version of the truth.

In some MDM hub styles the decision is to be taken at consolidation time, in other styles the decision is prolonged until the data (links) is consumed in a given context.

In the following I will talk about Party Master Data being the most common entity in Master Data initiatives.

mergeThis spring Jim Harris made a brilliant series of articles on DataQualityPro on the subject of identifying duplicate customers ending with part number 5 dealing with survivorship. Here Jim describes all the basic considerations on how some data elements survives a merge/purge and others will be forgotten and gives good examples with US consumer/citizens.

Taking it from there Master Data projects may have the following additional challenges and opportunities:

  • Global Data adds diversity into the rule set of consolidation data on record level as well as field level. You will have to comprise on simple global rules versus complex optimized rules (and supporting knowledge data) for each country/culture.
  • Multiple types of Party Master Data must be handled when Business Partners includes business entities having departments and employees and not at least when they are present together with consumers/citizens.
  • External Reference Data is becoming more and more common as part of MDM solutions adding valid, accurate and complete information about Business Partners. Here you have to set rules (on field level) of whether they override internal data, fills in the blanks or only supplements internal data.
  • Hierarchy building is closely related to survivorship. Rules may be set for whether two entities goes into two hierarchies with surviving parts from both or merges as one with survivorship. Even an original entity may be split into two hierarchies with surviving parts.

What is essential in survivorship is not loosing any valuable information while not creating information redundancy.

An example of complex survivorship processing may be this:

A membership database holds the following record (Name, Address, City):

  • Margaret & John Smith, 1 Main Street, Anytown

An eShop system has the following accounts (Name, Address, Place):

  • Mrs Margaret Smith, 1 Main Str, Anytown
  • Peggy Smith, 1 Main Street, Anytown
  • Local Charity c/o Margaret Smith, 1 Main Str, Anytown

A complex process of consolidation including survivorship may take place. As part of this example the company Local Charity is matched with an external source telling it has a new name being Anytown Angels. The result may be this “golden view”:

ADDRESS in Anytown on Main Street no 1 having
• HOUSEHOLD having
– CONSUMER Mrs. Margaret Smith aka Peggy
– CONSUMER Mr. John Smith
• BUSINESS Anytown Angels having
– EMPLOYEE Mrs. Margaret Smith aka Peggy

Observe that everything survives in a global applicable structure in a fit hierarchy reflecting local rules handling multiple types of party entities using external reference data.

But OK, we didn’t have funny names, dirt, misplaced data…..

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LinkedIn Group Statistics

LinkedInI am currently a member of 40 LinkedIn groups mostly targeted at Master Data Management, Data Quality and Data Matching.

As I have noticed that some groups covers the same topic I wondered if they have the same members.

So I did a quick analysis.

With Master Data Management the largest groups seems to be:

Using the LinkedIn Profile Organizer I found that 907 are members at both groups. This is not as many as I would have guessed.

With Data Quality the largest groups seems to be:

Using the LinkedIn Profile Organizer I found that 189 are members at both groups. This is not as many as I would have guessed despite the renaming of the last group.

As for Data Matching I have founded the Data Matching group. The group has 235 members where:

  • 77 are members in the two large Master Data Management groups also.
  • 80 are members in the two large Data Quality groups also.

Also this is not as many as I would have guessed.

You may find many other similar groups on my LinkedIn profile – among them:

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Follow Friday Master Data Hub

Social Networking needs Master Data Management.

brownbird_leftA recurring event every Friday on Twitter is the #FollowFriday with the acronym #FF, where people on Twitter tweets about who to follow.

I do it too and as every one else sometimes I perhaps forget someone, and then (s)he gets angry and don’t #FF me and that’s bad. Bad Data Management. Bad #mdm.

So now I have started building a Master Data Hub fit for the purpose of doing consistent #FF. I do see other purposes for this as well as I recognize the advantages of combining data sources, so I did a #datamatching with LinkedIn connections to improve #dataquality through Identity Resolution.

This is as far I am now (very convenient that WordPress lets me edit my blog posts):

@ReferenceData where is Staff Writer

@KenOConnorData is

@ocdqblog is a blog where is blogger-in-chief

@dataqualitypro is a community founded by

Dylan was a @Datanomic partner where @SteveTuck is

@InitiateSystems has a CTO = @wmmarty who is

@VishAgashe is

@KeithMesser is running @GlobalMktgPros

@fionamacd is at @TrilliumSW as seen here

So is @stevesarsfield being

Trillium is owned by Harte-Hanks where @MarkGoloboy also was

@biknowledgebase is operated by

@Dataexperts has a managing director who is

@IDResolution (Infoglide) has several Data Matching members in including

@rdrijsen is with possible duplicate

@grahamrhind is

@omathurin is

@zzubbuzz is probably

@CharlesBurleigh is

@wesharp is doing @dqchronicle

@decisionstats has an editor being

@jeric40 is my colleague at Omikron as shown here

Alignment of business and IT

teamworkBeing a Data Quality professional may be achieved by coming from the business side or the technology side of practice. But more important in my eyes is the question whether you have made serious attempts and succeeded in understanding the side from where you didn’t start.

Many blog posts made around the data quality conundrum discusses the role of the business side versus the role of the technology side and various weights in different contexts are given to these sides. It should not be surprising for a Data Quality professional that there is no absolute true or absolute false simple answer to such a question. Fortunately I find most discussions, when they are taken, ends up with the “peace on earth” sentiment:

  • Of course it’s the business requirements striving for business value that governs any initiative using technology in order to improve business performance
  • Of course the emerge (or discovery) of new technology may change the way you arrange business processes in order to gain on competitive business performance

From that point of view I am looking forward to continued discussions over all the important issues around data and information quality improvement and prevention as, but not limited to:

  • What is the business value of better information quality
  • How to gather business requirement related to information quality in order to make data fit for purpose(s)
  • Who is needed to accomplish the data quality improvement tasks – probably people from business, IT and all those mixed ones (credit: Jim Harris of OCDQblog)
  • When is the data quality technology so mature that it will cope with issues in a way not seen before
  • Which different kinds of methodologies and techniques are best for different sort of data quality challenges
  • Where on earth is the answers to all these questions

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