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.

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.

Product Information Sharing Issue No 2: No Viable Standard

A current poll on sharing product information with trading partners running on this blog has this question: As a manufacturer: What is Your Toughest Product Information Sharing Issue?

Some votes in the current standing has gone to this answer:

There is no viable industry standard for our kind of products

Indeed, having a standard that all your trading partners use too, will be Utopia.

This is however not the situation for most participants in supply chains. There are many standards out there, but each applicable for a certain group of products, geography or purpose as explained in the post Five Product Classification Standards.

At Product Data Lake we embrace all these standards. If you use the same standard in the same version as your trading partner, linking and transformation is easy. If you do not, you can use Product Data Lake to link and transform from your way to the way your trading partners handles product information. Learn more at Product Data Lake Documentation and Data Governance.

Attribute Types
The tagging scheme used in Product Data Lake attributes (metadata)

Five Product Classification Standards

When working with Product Master Data Management (MDM) and Product Information Management (PIM) one important facet is classification of products. You can use your own internal classification(s), being product grouping and hierarchy management, within your organization and/or you can use one or several external classification standards.

Five External Standards

Some of the external standards I have come across are:

UNSPSC

The United Nations Standard Products and Services Code® (UNSPSC®), managed by GS1 US™ for the UN Development Programme (UNDP), is an open, global, multi-sector standard for classification of products and services. This standard is often used in public tenders and at some marketplaces.

GPC

GS1 has created a separate standard classification named GPC (Global Product Classification) within its network synchronization called the Global Data Synchronization Network (GDSN).

Commodity Codes / Harmonized System (HS) Codes

Commodity codes, lately being worldwide harmonized and harmonised, represent the key classifier in international trade. They determine customs duties, import and export rules and restrictions as well as documentation requirements. National statistical bureaus may require these codes from businesses doing foreign trade.

eClass

eCl@ss is a cross-industry product data standard for classification and description of products and services emphasizing on being a ISO/IEC compliant industry standard nationally and internationally. The classification guides the eCl@ss standard for product attributes (in eClass called properties) that are needed for a product with a given classification.

ETIM

ETIM develops and manages a worldwide uniform classification for technical products. This classification guides the ETIM standard for product attributes (in ETIM called features) that are needed for a product with a given classification.

pdl-whyThe Competition and The Neutral Hub

If you click on the links to some of these standards you may notice that they are actually competing against each other in the way they represent themselves.

At Product Data Lake we are the neutral hub in the middle of everyone. We cover your internal grouping and tagging to any external standard. Our roadmap includes more close integration to the various external standards embracing both product classification and product attribute requirements in multiple languages where provided. We do that with the aim of letting you exchange product information with your trading partners, who probably do the classification differently from you.

Data Born Companies and the Rest of Us

harriThis post is a new feature here on this blog, being guest blogging by data management professionals from all over the world. First up is Harri Juntunen, Partner at Twinspark Consulting in Finland:

Data and clever use of data in business has had and will have significant impact on value creation in the next decade. That is beyond reasonable doubt. What is less clear is, how this is going to happen? Before we answer the question, I think it is meaningful to make a conceptual distinction between data born companies and the rest of us.

Data born born companies are companies that were conceived from data. Their business models are based  on monetising clever use of data. They have organised everything from their customer service to operations to be capable of maximally harness data. Data and capabilities to use data to create value is their core competency. These companies are the giants of data business: Google, Facebook, Amazon, Über, AirBnB. The standard small talk topics in data professionals’ discussions.

However, most of the companies are not data born. Most of the companies were originally established to serve a different purpose. They were founded to serve some physical needs and actually maintaining them physically, be it food, spare parts or factories. Obviously, all of these companies in  e.g. manufacturing and maintenance of physical things need data to operate. Yet, these companies are not organised around the principles of data born companies and capabilities to harness data as the driving force of their businesses.

We hear a lot of stories and successful examples about how data born companies apply augmented intelligence and other latest technology achievements. Surely, technologies build around of data are important. The key question to me is: what, in practice, is our capability to harness all of these opportunities in companies that are not data born?

In my daily practice I see excels floating around and between companies. A lot of manual work caused by unstandardised data, poor governance and bad data quality. Manual data work simply prevents companies to harness the capabilities created by data born companies. Yet, most of the companies follow the data born track without sufficient reflection. They adopt the latest technologies used by the data born companies. They rephrase same slogans: automation, advanced analytics, cognitive computing etc. And yet, they are not addressing the fundamental and mundane issues in their own capabilities to be able to make business and create value with data. Humans are doing machine’s job.

Why? Many things relate to this, but data quality and standardization are still pressing problems in every day practice in many companies. Let alone between companies. We can change this. The rest of us can reborn from data just by taking a good look of our mundane data practices instead of aspiring to go for the next big thing.

P.S. The Google Brain team had reddit a while ago and they were asked “what do you think is underrated?

The answer:

“Focus on getting high-quality data. “Quality” can translate to many things, e.g. thoughtfully chosen variables or reducing noise in measurements. Simple algorithms using higher-quality data will generally outperform the latest and greatest algorithms using lower-quality data.”

https://www.reddit.com/r/MachineLearning/comments/4w6tsv/ama_we_are_the_google_brain_team_wed_love_to/

About Harri Juntunen:

Harri is seasoned data provocateur and ardent advocate of getting the basics right. Harri says: People and data first, technology will follow.

You can contact Harri here:

+358 50 306 9296

harri.juntunen@twinspark.fi

www.twinspark.fi

 

Approaches to Sharing Product Information in Business Ecosystems

One of the most promising aspects of digitalization is sharing information in business ecosystems. In the Master Data Management (MDM) realm, we will in my eyes see a dramatic increase in sharing product information between trading partners as touched in the post Data Quality 3.0 as a stepping-stone on the path to Industry 4.0.

Standardization (or standardisation)

A challenge in doing that is how we link the different ways of handling product information within each organization in business ecosystems. While everyone agrees that a common standard is the best answer we must on the other hand accept, that using a common standard for every kind of product and every piece of information needed is quite utopic. We haven’t even a common uniquely spelled term in English.

Also, we must foresee that one organization will mature in a different pace than another organisation in the same business ecosystem.

Product Data Lake

These observations are the reasons behind the launch of Product Data Lake. In Product Data Lake we encompass the use of (in prioritized order):

  • The same standard in the same version
  • The same standard in different versions
  • Different standards
  • No standards

In order to link the product information and the formats and structures at two trading partners, we support the following approaches:

  • Automation based on product information tagged with a standard as explained in the post Connecting Product Information.
  • Ambassadorship, which is a role taken by a product information professional, who collaborates with the upstream and downstream trading partner in linking the product information. Read more about becoming a Product Data Lake ambassador here.
  • Upstream responsibility. Here the upstream trading partner makes the linking in Product Data Lake.
  • Downstream responsibility. Here the downstream trading partner makes the linking in Product Data Lake.

cross-company-data-governanceData Governance

Regardless of the mix of the above approaches, you will need a cross company data governance framework to control the standards used and the rules that applies to the exchange of product information with your trading partners. Product Data Lake have established a partnership with one of the most recommended authorities in data governance: Nicola Askham – the Data Governance Coach.

For a quick overview please have a look at the Cross Company Data Governance Framework.

Please request more information here.

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