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.

Takeaways from MDM Summit Europe 2016

Yesterday I popped in at the combined Master Data Management Summit Europe 2016 and Data Governance Conference Europe 2016.

This event takes place Monday to Thursday, but unfortunately I only had time and money for the Tuesday this year. Therefore, my report will only be takeaways from Tuesday’s events. On a side note the difficulties in doing something pan-European must have troubled the organisers of this London event as avoiding the UK May bank holidays has ended in starting on a Monday where most of the rest of Europe had a day off due to being Pentecost Monday.

MDM

Tuesday morning’s highlight for me was Henry Peyret of Forrester shocking the audience in his Data Governance keynote by busting the myth about the good old excuse for doing nothing, being the imperative of top-level management support, is not true.

Back in 2013 I wondered if graph databases will become common in MDM. Certainly graph databases has become the talk of the town and it was good to learn from Andreas Weber how the Germany based figurine manufacturer Schleich has made a home grown PIM / Product MDM solution based on graph database technology.

Ivo-Paul Tummers of Jibes presented the MDM (and beyond) roadmap for the Dutch food company Sligro. I liked the alley of embracing multi-channel, then omnichannel with self-service at the end of the road and how connect will overtake collect during this journey. This is exactly the reason of being for the Product Data Lake venture I am working on right now.

Bookmark and Share

Multilingual? Mais oui! Natürlich.

Is that piece of data wrong or right? This may very well be a question about in what language we are talking about.

In an earlier double post on this blog I had a small quiz about the name of the Pope in the Catholic church. The point was that all possible answers were right as explained in post When Bad Data Quality isn’t Bad Data. The thing is that the Pope over the wold has local variants over the English name Francis. François in French, Franziskus in German, Francesco in Italian, Francisco in Spanish Franciszek in Polish, Frans in Danish and Norwegian and so on.

In today’s globalized, or should I say globalised, world, it is important that our data can be represented in different languages and that the systems we use to handle the data is built for that. The user interface may be in a certain flavor/flavour of English only, but the data model must cater for storing and presenting data in multiple languages and even variants of languages as English in its many forms. Add to that the capability of handling other characters than Latin in other script systems than alphabets as examined in the post called Script Systems.

This challenge is very close to me right when we are building a service for sharing product information in business ecosystems. So will the Product Data Lake be multilingual? Mais oui! Natürlich. Jo da.

PDL Example

PS: The Product Data Lake will actually help with collecting product information in multiple languages through the supply chains of product manufacturers, distributors, retailers and end users.

Bookmark and Share