When you buy stuff one of the characteristics you may emphasis on is where the stuff is made: The country of origin.
Buying domestic goods has always been both a political issue and something that in people’s mind may be an extra quality sign. When I lived in The UK I noticed that meat was promoted as British (maybe except from Danish bacon). Now when back in Denmark all meat seems to be best when made in Denmark (maybe except from an Argentinian beef). However, regulations have already affected the made in marking for meat, so you have to state several countries of origins in the product lifecycle.
Country of origin is a product data element that you need to handle for regulatory reasons not at least when moving goods across borders. Here it is connected with commodity codes telling what kind of product it is in the custom way of classifying products as examined in the post Five Product Classification Standards.
When working with product data management for products that moves cross border you are increasingly asked to be more specific about the country of origin. For example, if you have a product consisting of several parts, you must specify the country of origin for each part.
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.
When 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.
Facebook is set to fight fake news by using artificial intelligence. A good way to practice may be by playing a bit more around with their geolocation intelligence.
Today I, as far as I know, are on the Canary Islands. This is a part of Spain, though a little bit away from the motherland down the Atlantic Ocean off the North African coast. A main town on the islands is called Las Palmas.
However, according to Facebook I seem to be in a place called Las Palmas Subdivision on Hawaii in the Pacific Ocean on the other side of the globe with Hawaii being a bit away from where it were last time I looked on a map.
Welcome in the class room to Rick Buijserd from The Netherlands as the next guest blog post author:
As a child you were happy when the bell ranged and the school day ended. It was time to play with your friends and don’t think about learning anymore, just play! Most of us look back at this time as the best time of our lives. A time without any worries and enjoying every moment of it. Even though it wasn’t the main focus as a child it was also the time that we learned new ideas and things every day. Are we still learning every day? Are you learning new things about data management every day? You should and here is why…
Data is the new oil and many of us make a decent living by advising or consulting companies in this area of expertise. But when time goes by so are the developments and in the technology world this goes fast, very fast. In the last couple of years the data environment has become bigger and bigger. First there was just data in companies, now you have the combine sources of data to get a clear view about. And the sources keep on changing. Big data used to be a word that was undefined and unable to use. And for many it still is, but others use big data to enrich and enable growth for their companies. By just summing this up you see the changes that happened in the last couple of years and you have to keep up to stay relevant. Learn and gain knowledge is the only key to success in the long term. Artificial Intelligence and Machine Learning powered by optimal use of data and data management will take over many tasks but in the end human creativity and the ability to learn will provide success and the power to make the difference.
Data Management is never finished and neither is learning about it
As you have been in the world of data management you should know that data management is never finished and so is the possibility of gaining knowledge. New books about data management are published recently, research firms keep on researching and find new discoveries. And many companies use the evolution of the technology to grow. Also Communities are built around topics on many different platforms. The possibility to learn is everywhere! Use it in your benefit, data management is never finished…
Rick Buijserd is author and owner of the platform Data Management Experts and a young professional with experience in the world of data. He started his career at a well-known software vendor as channel manager where he learned the skills of indirect sales and managing partners. Financial, HR, Logistics, Warehousing and PSA were the main elements of his software sales. Building relationships with experts and other vendors are part of his DNA.
After a couple of years he decided to make a switch and landed in the world of accountancy firms. In this period he enabled himself to become a trusted advisor of many accountancy firms in The Netherlands. The area of finance, financial reporting, tax, auditing and other accountancy related activities are no secret to him. Together with his clients he developed many solutions to solve their challenges. In this period the love for data management came above. Accountancy firms are the ultimate example of being data driven. It is all they know.
In the most recent period of his career he stepped into the world of multinationals and as off today he is still active in this world advising around data management and selling software solutions to multinationals who have challenges in the area of data management. Also he is an expert in the area of social selling via LinkedIn and this knowledge has been brought into practice via a LinkedIn Group for Dutch Data Management Experts in which he gathers the top data management experts from the largest companies in The Netherlands to discuss all kind of data related topics.
The below figure shows the cross border data flows on this planet. There are inter-regional data flows and there are flows between the worldwide regions:
Now, a small part of this data will be product data exchanged between trading partners participating in global business ecosystems. While I have no data on if product data are distributed by the same proportions as data in general, it will be a qualified guess, that the picture will look somewhat the same.
Exchanging product data across borders has some challenges:
Language is an issue. Product data will eventually have to be translated into the language of the end buyer, if this is not the language in which the product data originally are provided. The definitions (metadata) of product data will also be subject to translation. Even the language of the transmission tools would not be in English all over.
Regulations around product data are different from country to country.
The cultural content of the optimal data describing a product in structured data elements and related digital assets are different between countries and regions.
Our company Product Data Lake has relocated again. Our new address, in local language and format, is:
1058 København K
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
Across the pond, a sunny address could look like this:
39 Harbor Drive
Copenhagen, CR 1058
U.S. Virgin Islands
Now, 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.
In this blog post Gautam examines the challenges, the key questions and the concept options an organization have when embarking on a journey to go from a national (or regional) scale to an international scale in Product Information Management.
In his post Gautam states: “A Global PIM is not a consolidation exercise. Variance is the reality, and it has to be supported.”
This resonates very well with my findings. Very low practical this means that you will not win by translating all product descriptions into English. Even the metadata has to be multilingual, as you will interact with trading partners using different languages. While one public standard for product information may be king in one region, this will most likely not be the case in another region, which again effects how you collaborate with trading partners in different geographies.
In my eyes the global PIM journey does not end with consensus and a common platform of either concept inside your organization. You have to embrace your business ecosystem of trading partners. How to do that is explained in the post What a PIM-2-PIM Solution Looks Like.