What is in a business directory?

When working with Party Master Data Management one approach to ensure accuracy, completeness and other data quality dimensions is to onboard new business-to-business (B2B) entities and enrich such current entities via a business directory.

While this could seem to be a straight forward mechanism, unfortunately it usually is not that easy peasy.

Let us take an example featuring the most widely used business directory around the world: The Dun & Bradstreet Worldbase. And let us take my latest registered company: Product Data Lake.

PDL at DnB

On this screen showing the basic data elements, there are a few obstacles:

  • The address is not formatted well
  • The country code system is not a widely used one
  • The industry sector code system shown is one among others

Address Formatting

In our address D&B has put the word “sal”, which is Danish for floor. This is not incorrect, but addresses in Denmark are usually not written with that word, as the number following a house number in the addressing standard is the floor.

Country Codes

D&B has their own 3-digit country code. You may convert to the more widely used ISO 2-character country code. I do however remember a lot of fun from my data matching days when dealing with United Kingdom where D&B uses 4 different codes for England, Wales, Scotland and Northern Ireland as well as mapping back and forth with United States and Puerto Rico. Had to be made very despacito.

Industry Sector Codes

The screen shows a SIC code: 7374 = Computer Processing and Data Preparation and Processing Services

This must have been converted from the NACE code by which the company has been registered:  63.11:(00) = Data processing, hosting and related activities.

The two codes do by the way correspond to the NAICS Code 518210 = Data processing, hosting and related activities.

The challenges in embracing the many standards for reference data was examined in the post The World of Reference Data.

What’s in an Address (and a Product)?

Our company Product Data Lake has relocated again. Our new address, in local language and format, is:

Havnegade 39
1058 København K
Danmark

If our address were spelled and formatted as in England, where the business plan was drafted, the address would have looked like this:

The Old Seed Office
39 Harbour Street
Copenhagen, 1058 K
Danelaw

Across the pond, a sunny address could look like this:

39 Harbor Drive
Copenhagen, CR 1058
U.S. Virgin Islands

copenhagen_havnegadeNow, the focal point of Product Data Lake is not the exciting world of address data quality, but product data quality.

However, the same issues of local and global linguistic and standardization – or should I say standardisation – issues are the same.

Our lovely city Copenhagen has many names. København in Danish. Köpenhamn in Swedish. Kopenhagen in German. Copenhague in French.

So have all the nice products in the world. Their classifications and related taxonomy are in many languages too. Their features can be spelled in many languages or be dependent of the country were to be sold. The documents that should follow a product by regulation are subject to diversity too.

Handling all this diversity stuff is a core capability for product data exchange between trading partners in Product Data Lake.

What Will you Complicate in the Year of the Rooster?

rooster-6Today is the first day in the new year. The year of the rooster according to the Lunar Calendar observed in East Asia. One of the characteristics of the year of the rooster is that in this year, people will tend to complicate things.

People usually likes to keep things simple. The KISS principle – Keep It Simple, Stupid – has many fans. But not me. Not that I do not like to keep things simple. I do. But only as simple as it should be as Einstein probably said. Sometimes KISS is the shortcut to getting it all wrong.

When working with data quality I have come across the three below examples of striking the right balance in making things a bit complicated and not too simple:

Deduplication

One of the most frequent data quality issues around is duplicates in party master data. Customer, supplier, patient, citizen, member and many other roles of legal entities and natural persons, where the real world entity are described more than once with different values in our databases.

In solving this challenge, we can use methods as match codes and edit distance to detect duplicates. However, these methods, often called deterministic, are far too simple to really automate the remedy. We can also use advanced probabilistic methods. These methods are better, but have the downside that the matching done is hard to explain, repeat and reuse in other contexts.

My best experience is to use something in between these approaches. Not too simple and not too overcomplicated.

Address verification

You can make a good algorithm to perform verification of postal and visit addresses in a database for addresses coming from one country. However, if you try the same algorithm on addresses from another country, it often fails miserably.

Making an algorithm for addresses from all over the world will be very complicated. I have not seen one yet, that works.

My best experience is to accept the complication of having almost as many algorithms as there are countries on this planet.

Product classification

Classifications of products controls a lot of the data quality dimensions related to product master data. The most prominent example is completeness of product information. Whether you have complete product information is dependent on the classification of the product. Some attributes will be mandatory for one product but make no sense at all to another product by a different classification.

If your product classification is too simple, your completeness measurement will not be realistic. A too granular or other way complicated classification system is very hard to maintain and will probably seem as an overkill for many purposes of product master data management.

My best experience is that you have to maintain several classification systems and have a linking between them, both inside your organization and between your trading partners.

Happy New Lunar Year

Did You Mean Potato or Potahto?

As told in the post Where the Streets have Two Names one aspect of address validation is the fact, that in some parts of the world, a given postal address can be presented in more than one language.

I experienced that today when using Google Maps for directions to a Master Data Management (MDM) conference in Helsinki, Finland. When typing in the address I got this message:

Helsinki

The case is that the two addresses proposed by Google Maps are exactly the same address, just spelled in Swedish and Finnish, the two official languages used in this region.

I think Google Maps is an example of a splendid world-wide service. But even the best world-wide services sometimes don’t match local tailored services. This is in my experience the case when it comes to address management solutions as address validation and assistance whether they come as an integrated part of a Master Data Management (MDM) solution, a stand-alone data quality tool or a general service as Google Maps.

Bringing the Location to Multi-Domain MDM

When we talk about multi-domain Master Data Management (MDM) we often focus on the two dominant MDM domains being customer (or rather party) MDM and product (or maybe things) MDM.

The location domain is the third bigger domain within MDM. Location management can be more or less complex depending on the industry vertical we are looking at. In the utility and telco sectors location management is a big thing. Handling installations, assets and networks is typically supported by a Geographical Information System (GIS).

Master Data Management is much about supporting that different applications can have a unified view of the same core business entities. Therefore, in the utility and telco sectors a challenge is to bring the GIS application portfolio into the beat with other applications that also uses locations as explained in the post Sharing Big Location Reference Data.

Location2

The last couple of days I enjoyed taking part in the Nordic user conference for a leading GIS solution in the utility and telco sector. This solution is called Smallword.

It is good to see that at least one forward looking organization in the utility and telco sector is working with how location master data management can be shared between business functions and applications and aligned with party master data management and product master data management.

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Reading the right Reading

TripItIn order to have all my travel arrangements in one place I use a service called TripIt. When I receive eMail confirmations from airlines, hotels, train planners and so, I simply forward those to plans@tripit.com, and within seconds they build or amend to an itinerary for me that is available in an app.

Today I noticed a slight flaw though. I was going by train from London, UK up to the Midlands via a large town in the UK called Reading.

The strange thing in the itinerary was that the interchanges in Reading was placed in chronology after arriving at and leaving the final destination.

A closer look at the data revealed two strange issues:

  • Reading was spelled Reading, PA
  • The time zone for the interchange was set to EST

Hmmm…  There must be a town called Reading in Pennsylvania across the pond. Tripit must, when automatically reading the eMail, have chosen the US Reading for this ambiguous town name and thereby attached the Eastern American time zone to the interchange.

Picking the right Reading for me in the plan made the itinerary look much more sensible.

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Cleansing International Addresses

A problem in data cleansing I have come across several times is when you have some name and address registrations where it is uncertain to which country the different addresses belong.

Many address-cleansing tools and services requires a country code as the first parameter in order to utilize external reference data for address cleansing and verification. Most business cases for address cleansing is indeed about a large number of business-to-consumer (B2C) addresses within a particular country. But sometimes you have a batch of typical business-to-business (B2B) addresses with no clear country registration.

The problem is that many location names applies to many different places. That is true within a given country – which was the main driver for having postal codes around. If a none-interactive tool or service have to look for a location all over the world that gets really difficult.

For example I’m in Richmond today. That could actually be a lot of places all over the world as seen on Wikipedia.

popeI am actually in the Richmond in the London, England, UK area. If I were in the state capital of the US state of Virginia, I could have written I’m in “Richmond, VA”. If an international address-cleansing tool looked at that address, I guess it would first look for a country code, quickly find VA as a two-character country code in the end of the string and firmly conclude I’m at something called Richmond in the Vatican City State.

Have you tried using or constructing an international address cleansing process? Where did you end up?

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