Within the upcoming EU General Data Protection Regulation (GDPR) the term data subject is used for the persons for whom we must protect the privacy.
These are the persons we handle as entities within party Master Data Management (MDM).
In the figure below the blue area covers the entity types and roles that are data subjects in the eyes of GDPR
While GDPR is of very high importance in business-to-consumer (B2C) and government-to-citizen (G2C) activities, GDPR is also of importance for business-to-business (B2B) and government-to-business (G2B) activities.
GDPR does not cover unborn persons which may be a fact of interest in very few industries as for example healthcare. When it comes to minors, there are special considerations within GDPR to be aware of. GDPR does not apply to deceased persons. In some industries like financial services and utility, the handling of the estate after the death of a person is essential, as well as knowing about that sad event is of importance in general as touched in the post External Events, MDM and Data Stewardship.
One tough master data challenge in the light of GDPR will be to know the status of your registered party master data entities. This also means knowing when it is a private individual, a contact at an organization or an organization or department hereof as such. From my data matching days, I know that heaps of databases do not hold that clarity as reported in the post So, how about SOHO homes.
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.
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
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.
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 Code518210 = Data processing, hosting and related activities.
Within Product Information Management (PIM) there is a growing awareness about that sharing product information between trading partners is a very important issue.
So, how do we do that? We could do that, on a global scale, by using:
2,345,678 customer data portals
901,234 supplier data portals
Spreadsheets is the most common mean to exchange product information between trading partners today. The typical scenario is that a receiver of product information, being a downstream distributor, retailer or large end user, will have a spreadsheet for each product group that is sent to be filled by each supplier each time a new range of products is to be on-boarded (and potentially each time you need a new piece of information). As a provider of product information, being a manufacturer or upstream distributor, you will receive a different spreadsheet to be filled from each trading partner each time you are to deliver a new range of products (and potentially each time they need a new piece of information).
Customer data portals is a concept a provider of product information may have, plan to have or dream about. The idea is that each downstream trading partner can go to your customer data portal, structured in your way and format, when they need product information from you. Your trading partner will then only have to deal with your customer data portal – and the 1,234 other customer data portals in their supplier range.
Supplier data portals is a concept a receiver of product information may have, plan to have or dream about. The idea is that each upstream trading partner can go to your supplier data portal, structured in your way and format, when they have to deliver product information to you. Your trading partner will then only have to deal with your supplier data portal – and the 567 other supplier data portals in their business-to-business customer range.
Product Data Lake is the sound alternative to the above options. Hailstorms of spreadsheets does not work. If everyone has either a passive customer data portal or a passive supplier data portal, no one will exchange anything. The solution is that you as a provider of product information will push your data in your structure and format into Product Data Lake each time you have a new product or a new piece of product information. As a receiver you will set up pull requests, that will give you data in your structure and format each time you have a new range of products, need a new piece of information or each time your trading partner has a new piece of information.
Master Data Management (MDM) is increasingly being about supporting systems of engagement in addition to the traditional role of supporting systems of record. This topic was first examined on this blog back in 2012 in the post called Social MDM and Systems of Engagement.
The best known systems of engagement are social networks where the leaders are Facebook for engagement with persons in the private sphere and LinkedIn for engagement with people working in or for one or several companies.
But what about engagement between companies? Though you can argue that all (soft) engagement is neither business-to-consumer (B2C) nor business-to-business (B2B) but human-to-human (H2H), there are some hard engagement going on between companies.
One of the most important ones is exchange of product information between manufacturers, distributors, resellers and large end users of product information. And that is not going very well today. Either it is based on fluffy emailing of spreadsheets or using rigid data pools and portals. So there are definitely room for improvement here.
One of the ways to ensure data quality for customer – or rather party – master data when operating in a business-to-business (B2B) environment, is to on-board new entries using an external defined business entity identifier.
By doing that, you tackle some of the most challenging data quality dimensions as:
Accuracy, by having names, addresses and other information defaulted from a business directory and thus avoiding those spelling mistakes that usually are all over in party master data.
Conformity, by inheriting additional data as line-of-business codes and descriptions from a business directory.
Having an external business identifier stored with your party master data helps a lot with maintaining data quality as pondered in the post Ongoing Data Maintenance.
When selecting an identifier there are different options as national IDs, LEI, DUNS Number and others as explained in the post Business Entity Identifiers.
At the Product Data Lake service I am working on right now, we have decided to use an external business identifier from day one. I know this may be something a typical start-up will consider much later if and when the party master data population has grown. But, besides being optimistic about our service, I think it will be a win not to have to fight data quality issues later with guarantied increased costs.
For the identifier to use we have chosen the DUNS Number from Dun & Bradstreet. The reason is that this currently is the only worldwide covered business identifier. Also, Dun & Bradstreet offers some additional data that fits our business model. This includes consistent line-of-business information and worldwide company family trees.
As reported in the post Gravitational Waves in the MDM World there is a tendency in the MDM (Master Data Management) market and in MDM programmes around to encompass both the party domain and the product domain.
The party domain is still often treated as two separate domains, being the vendor (or supplier) domain and the customer domain. However, there are good reasons for seeing the intersection of vendor master data and customer master data as party master data. These reasons are most obvious when we look at the B2B (business-to-business) part of our master data, because:
You will always find that many real world entities have a vendor role as well as a customer role to you
The basic master data has the same structure (identification, names, addresses and contact data
You need the same third party validation and enrichment capabilities for customer roles and vendor roles.
When we look at the product domain we also have a huge need to connect the buy side and the sell side of our business – and the make side for that matter where we have in-house production.
Multi-Domain MDM has a side effect, so to speak, about bringing the sell-side together with the buy- and make-side. PIM (Product Information Management), which we often see as the ancestor to product MDM, has the same challenge. Here we also need to bring the sell-side and and the buy-side together – on three frontiers:
Bringing the internal buy-side and sell-side together not at least when looking at product hierarchies
Bringing our buy-side in synchronization with our upstream vendors/suppliers sell-side when it comes to product data
Bringing our sell-side in synchronization with our downstream customers buy-side when it comes to product data