History

Did They Put a Man on the Moon?

Henrik Gabs Liliendahl12 Comments

Recently I have been reading some blog posts circling around having a national ID for citizens in the United States including a post from Steve Sarsfield and another post from Jeffrey Huth of Initiate.

In Denmark where I live we have had such a national ID for about half a century. So if you are a vendor with a great solution for data matching and master data management in healthcare and wants to approach a Danish prospect in healthcare (which are mainly public sector here), they will tell you, that the solutions looks really nice, but they don’t have that problem. You can’t stay many seconds as a patient in a Danish hospital before you are asked to provide your national ID. And if you came in inside your mother you will be given an ID for life within seconds after you are born.

The same national ID is the basis when we have elections. Some weeks before the authorities will push the button and every person with the right status and age gets a ballot. Therefore we are in disbelief when we every fourth year are following when United States elects a president and we learn about all the mess in voter registration.

Is that happening in the nation that put a man on the moon in 1969?. Or did they? Was it after all a studio recording?

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The Tower of Babel

Henrik Gabs Liliendahl2 Comments

Brueghel-tower-of-babelSeveral old tales including in the Genesis and the Qur’an have stories about a great tower built by mankind at a time with a single language of all people. Since then mankind was confused by having multiple languages. And indeed we still are.

Multi-cultural issues is one of the really big challenges in data quality improvement. This includes not only language variations but also different character sets reflecting different alphabets and script systems, naming traditions, address formats, measure units, privacy norms, government registration practice to name the ones I have experienced.

As globalization moves forward these challenges becomes more and more important. Enterprises tend to standardize world wide on tools and services, shared service centres takes care of data covering many countries and so on. When an employee works with data from another country he often wrongly adapts his local standards to these data and thereby challenges the data quality more than seen before.

Recently I updated this site with pages around “The art of Matching”. One topic is “Match Techniques” and comments posted here were exactly very much around the need for methods that solves the problems arising from having multi-cultural data. Have a look.

International and multi-cultural aspects of data quality improvement has been a favourite topic of mine for a long time.

Whether and when an organisation has to deal with international issues is of course dependent on whether and in what degree that organisation is domestic or active internationally. Even though in some countries like Switzerland and Belgium having several official languages the multi-cultural topic is mandatory. Typically in large countries companies grows big before looking abroad while in smaller countries, like my home country Denmark, even many fairly small companies must address international issues with data quality. 

Some of the many different observations I have made includes the following:

  • Nicknames is a top issue in name matching in some cultures, but not of much importance in other cultures
  • Family names is key element in identifying households in some cultures, but not very useful in other cultures
  • Address verification and correction is very useful in some countries but close to impossible in other countries
  • Business directories are complete, consistent and available in some countries, but not that good in other countries
  • Citizen information is available for private entities in some countries, but is a no go in other countries

While working with data quality tools and services for many years I have found that many tools and services are very national. So you might discover that a tool or service will make wonders with data from one country, but be quite ordinary or in fact useless with data from another country.

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Qualities in Data Architecture

Henrik Gabs Liliendahl2 Comments

Data architecture describes the structure of data used by a business and its applications by mapping the data artifacts to data qualities, applications, locations etc.

Pont_du_gard2000 years ago the roman writer, architect and engineer Marcus Vitruvius Pollio wrote that a structure must exhibit the three qualities of firmitas, utilitas, venustas — that is, it must be strong or durable, useful, and beautiful.

I have worked with data quality for many years and always been a bit disappointed about the lack of (at)traction that has been around data quality issues. Perhaps the lack of attraction is due to that we focus so much on strength, durability and usefulness and too little about beauty – or at least attractiveness.

But how do the three qualities apply to data quality?

  • Firmitas, strength and durability, is connected to technology and how we tend to make our data be as close to reflecting real world objects as possible in terms as uniqueness, completeness, timeliness, validity, accuracy and consistency.  
  • Utilitas, usefulness, is connected to how we use data as information in business processes. Often “fit for purpose” is stated as a goal for data quality improvement – which makes it hard when multiple purposes exist in an organization.
  • Venustas – beauty or attractiveness – is connected to the mindset of people. Often we blame poor data quality on the people putting data into the data stores and direct initiatives that way using a whip called data governance. But probably we will get more attraction from people if we make or show quality data more attractive.

SidneyOperaHouseCompared to buildings data quality are often the sewers beneath the old cathedrals and new opera houses – which also may explain the lack of attraction.

If you consider yourself a data quality professional – being a tool maker, expert, whatever – you got to get up from the sewers and make and show some attractive data in the halls of the fine buildings. You know how hard it is to make quality data – but do tell about the success stories.

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