The Problem with English

– and many other languages

This blog is in English. However, as a citizen in a country where English is not the first language, I have a problem with English. Which flavour or flavor of English should I use? US English? British English? Or any of the many other kinds of English?

It is, in that context, more a theoretical question than a practical one. Despite what Grammar Nazis might think, I guess everyone understands the meaning in my blend of English variants and occasional other spelling mistakes.

The variants of English, spiced up with other cultural and administrative differences, does however create real data quality issues as told in the post Cultured Freshwater Pearls of Wisdom.

EnglishWhen working with Product Data Lake, a service for sharing product information between trading partners, we also need to embrace languages. In doing that we cannot just pick English. We must make it possible to pick any combination of English and country where English is (one of) the official language(s). The same goes for Spanish, German, French, Portuguese, Russian and many other languages in the extend that products can be named and described with different spelling (in a given alphabet or script type).

You always must choose between standardization or standardisation.

I am afraid that Gartner does not help

“The average financial impact of poor data quality on organizations is $9.7 million per year.” This is a quote from Gartner, the analyst firm, used by them to promote their services in building a business case for data quality.

AverageWhile this quote rightfully emphasizes on that a lot of money is at stake, the quote itself holds a full load of data and information quality issues.

On the pedantic side, the use of the $ sign in international communication is problematic. The $ sign represents a lot of different currencies as CAD, AUD, HKD and of course also USD.

Then it is unclear on what basis this average is measured. Is it among the +200 million organizations in the Dun & Bradstreet Worldbase? Is it among organizations on a certain fortune list? In what year?

Even if you knew that this is an average in a given year for the likes of your organization, such an average would not help you justify allocation of resources for a data quality improvement quest in your organization.

I know the methodology provided by Gartner actually is designed to help you with specific return on investment for your organization. I also know from being involved in several business cases for data quality (as well as Master Data Management and data governance) that accurately stating how any one element of your data may affect your business is fiendishly difficult.

I am afraid that there is no magic around as told in the post Miracle Food for Thought.

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.

Cultured Freshwater Pearls of Wisdom

One of my current engagements is within jewelry – or is it jewellery? The use of these two respectively US English and British English words is a constant data quality issue, when we try to standardize – or is it standardise? – to a common set of reference data and a business glossary within an international organization – or is it organisation?

Looking for international standards often does not solve the case. For example, a shop that sells this kind of bijouterie, may be classified with a SIC code being “Jewelry store” or a NACE code being “Retail sale of watches and jewellery in specialised stores”.

shiny thingsA pearl is a popular gemstone. Natural pearls, meaning they have occurred spontaneously in the wild, are very rare. Instead, most are farmed in fresh water and therefore by regulation used in many countries must be referred to as cultured freshwater pearls.

My pearls of wisdom respectively cultured freshwater pearls of wisdom for building a business glossary and finding the common accepted wording for reference data to be used within your company will be:

  • Start looking at international standards and pick what makes sense for your organization. If you can live with only that, you are lucky.
  • If not, grow the rest of the content for your business glossary and reference data by imitating the international or national standards for your industry, and use your own better wording and additions that makes the most sense across your company.

And oh, I know that pearls of wisdom are often used to imply the opposite of wisdom 🙂

Bookmark and Share

Choosing the Best Term to Use in MDM

Right now I am working with a MDM (Master Data Management) service for sharing product data in the business ecosystems of manufacturers, distributors, retailers and end users of product information.

One of the challenges in putting such a service to the market is choosing the best term for the entities handled by the service.

Below is the current selection with the chosen term and some recognized alternate terms used frequently and found in various standards that exists for exchanging product data:

Terms

Please comment, if you think there are other English (or variant of English) terms that deserves to be in here.