Error tolerance on Hollywood Walk of Fame

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Hmmm how is her name spelled?

Julia Louis-Dreyfus or Julia Luis Dreyfus ?

The correct answer is number one, but on her new star on Holywood wlak of fame or, was it Hollywood Walk of Fame they used the 2nd one.   First time the concept of  Error Tolerance was introduced on the walk

Typos and misspellings in the data entry phase is maybe the most important reason for poor data quality.  If I put Julia Louis-Dreyfus through a typo generator takes care of the most common typos like skipping letter, use double letters, reverse letters, skip spaces, misses keys or insert keys, you get 319 different variation of her name.   Some random examples: Julia Lois-Dreyfus,  Julia Louis-Dryefus, Julia Louis-Dryfus, Julija Loujis-Dreyfus and so on.

Do you Customer Search and Data Quality systems cover this?

Read more here in English and here in Norwegian


Royal Wedding Data Quality Challenges

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Now we have the proof, having blue blood in your veins, is not a vaccine against the challenges of Data Quality.  H.M Carl XVI Gustaf has now experienced this the hard way.  His daughter H.R.H Crown Princess Victoria will marry Daniel Westling in Stockholm on June 19th 2010.   The wedding will be grand with many guests from near and far.

One invited guest is not welcome, a journalist Håkan Kjellberg was invited.  The invitation has now been retracted, and he is no longer welcome in the wedding.  No, he has not written about Royal scandals or anything like that, he is just the wrong Håkan Kjellberg.  The right Håkan is a colleague of Mr. Westling and work in his Gym.

It is not so good when you think about the security the Royal Family is set under. This common mistake could have been avoided with the proper Data Quality checks in order.

Here is an article about it in Swedish, and one in Norwegian.  You can use Google Translate to get the meaning!

Anyway, congratulations to the happy couple!

Detecting Scam and Fraud

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It was a big story in the news yesterday, how Norwegian Air Shuttle was scammed by their Competitor Cimber Sterling.  Norwegian had put out super bargains in the Danish market to attract new customers. Cimber employees bought huge number of tickets in the name of Anders And (Danish version of Donald Duck), Alotto Fagina (Character from Austin Power movies) and Bjørn Kjos, the CEO of Norwegian.  The result was that Norwegian had a tremendous amount of no shows.

The question arises, could this be avoided?

The simple answer is yes, it is quite simple.  With an automated Data Quality Solution, this whole scam would have been stopped in the making. I don’t know the full extent of the Cimber Scam, but here are some of my assumptions based on what has come out in the media.  Here are some red flags that should have been raised:

Red flag 1. “Non Real Life Subjects”
Data Quality solutions are set up to find names people use to trick you it can be:

–          Cartoon Characters like Donald Duck, Batman and Superman

–          Film Characters like Alotta Fagina and Clark Kent

–          Random letters.  Aaaaa Bbbbb and Eeeeff Ghhhh

The DQ solution would have cleansed out these orders.

Red flag 2. Multiple use of same credit card.
It is said that a Cimber Sterling employee used the same credit card to book several hundred ticket over short period of time. This should have raised concerns in the fraud detection team, and be manually checked.

They should also cross check information.  Is it Natural that their CEO will use a Danish Credit Card to book his flights?

Red flag 3. Duplicate Check
DQ solutions run duplicate checks.  Unless Cimber employees have a very vivid imagination in inventing names, I am sure they must have used some names over again.   This should have been caught while ordering.

The Cimber Employees might have used a very common way of committing fraud.  Use variations and typos of the names.  Unless you have an error tolerant solution, it is difficult to catch these. In this post, I have explained how the name Christopher Quist can be written in almost 400 natural versions.

Red flag 4. Sanction List
I just mention this, because I am sure Norwegian has a system for this because of legal requirements.  In DQ solutions we also check according to the EU sanction list, to see that terrorists like Osama Bin Laden and other non wanted individuals cannot purchase from you.

It is normal that when you get an order to check all above red flags.  Most red flag warnings will be handled by an automated process.  When there are dubious entries, it will go to a operator that will handle this manually.

A Data Quality Solution is a cheap way to insure your company against such scams as encountered by Norwegian.  I think we will see more stories like this in the news, since most companies have not have focus on Data Quality.  I have written more about this post.

Most companies are not aware of the high cost of bad data quality, I hope this scam will help rise the awareness.

Here is my post from yesterday about the scam

Here is an article in Norwegian about the scam.

The scam continues. Here is another article in Norwegian

Donald Duck tricked Norwegian Air Shuttle

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The competition in the air is fierce, and Donald Duck entered the fight this weekend.

Norwegian Air Shuttle ( has entered the Danish market for international flight successfully.  Their next step is trying to be a big domestic player in Denmark.  They offered several thousand tickets for 1.DKK a piece.  They were instant sold out.

It seems it is their competitor Cimber Sterling that has bought most of the tickets. On one flight there were 118 “no shows”.  One no show seems to be “Anders And” which is the Danish name of Donald Duck.

Another no show was Alotta Fagina, from Austin Power movies and the CEO of Norwegian Bjørn Kjos

In Data Quality sales, we use the name Donald Duck as an example to look for when people want to trick you, and we talk about fraud detection and data analysis. This scam is not new, but can be easily detected and avoided with Data Quality solutions.

Read my follow up post. “Detecting Scam and Fraud”

Read more in Norwegian her:

With Google Translate:

A couple of stories about Calendar troubles

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Ohh – is it 53 weeks in 2009?

A little Data Quality Glitch story from Norway again.  Skagerak Elektro which provides the street lights in the Municipality of Tønsberg had not taken into the account that 2009 has 53 weeks – not only 52.  So when the clock passed midnight on Sunday, the streetlights blacked out.  They had forgotten to fill in week 53 into the operating system.

Not a big story, but maybe some Data Quality Processes should be in place??

Link to story in Norwegian:

Link to story with using Google Translate

Calling Information for 17 seconds costs 100 Euro

Daylight saving time enden on oct 25, and the time was turned back from 0300 to 0200.  A Norwegian Telecom provider could not cope with this.

One person who called the information for a number to the Taxi service, called 17 seconds at 02:14:28, but the bill said 1 hour and 17 seconds, and run up to about 100 Euro.

The story in Norwegian here:

The story using Google Translate here.

Data Quality problems in the Norwegian State Church.

BOrgund Stavkirke 1

I just read an article in the Norwegian paper Dagbladet.

In Norway there is a State Church. It means it is sponsored by the State and a vast majority (about 85%) of the citizens is member of it.

In the last weeks the Norwegian Church as sent out 3,12 mill election cards to Norwegians over 15 years of age.  The problem is that many members have elected to leave the church and select not to be a member of become members of other churches or organizations.  There seem to be a problem with the registrations of the ones who have left.

This has severe financial implications since you get support for each member. The other churches can miss this income. It might not sound as much – but when there is an estimated that more than 100.000 Norwegians is wrongly enrolled in the state church. For the organization Human Etisk Forbund – it is a loss of about 3,4 Mill NOK

It has also created hard feelings since this is an important issue for a lot of people.

There is no explanation of this Data Quality Failure, if it is Data Entry problems, cleansing problems or just pure sloppiness.    I am just waiting for them to ask for the heavenly powers the posess to solve the problem.

Voter fraud or just poor Data Quality?

What would we say if an African or South American government sent out election card that:

  • Says you will have to cast your vote in a non existing election location
  • Gives you the wrong address to the election location
  • Tells you to vote at two different location, but you should only vote at onel
  • Tells you to vote at two different locations, but you should actually vote at a third location?

With our prejudices, I think we would cry “voter fraud”. The above mentioned is not in Africa, but is happening in the Danish EU election. The newspapers bring new stories every day.

It is of course not voter fraud, but just plain poor Data Quality.  Is it much better that poor Data Quality influences the results of the democratic process? In Denmark the EU is already charged with having a democratic deficit, so this is the way to go!

The sad thing is that all this problems could be solved easily with the right process and tools.

You can read more about this in Danish on