The two most common master data types are:
- Party master data (customers, prospects, suppliers and other business partners)
- Product master data
When working with data quality within master data management you may of course encounter some similarities between these two master data types, but you will certainly also meet a range differences.
The basic activities as standardization, consolidation and hierarchy building are the same.
Some of the differences I have learned are:
Multi-cultural issues:
- Party master data is often stored in a single global format but should be transformed to embrace multi-cultural diversities.
- Product master data may have multi-cultural issues but should be transformed into a single global format (of course embracing multi-language hierarchies and so).
External reference data available:
- For party master data the possibilities for real world alignment with external data sources are plenty.
- For product master data the possibilities for real world alignment with external data sources are few.
Industry specific requirements:
- Requirements for party master data quality are pretty much the same across industries with few variations as B2B (corporate customers) or B2C (private customers) or both being the most prominent.
- Requirements for product master data quality vary tremendously across different industries.
Your say:
What are your examples of (similarities and) differences between party master data quality and product master data quality?
Liliendahl on Data Quality 
In the LinkedIn group “MDM – Master Data Management” Vincent Poncet says:
“Product Information Management and Customer Data Integration solutions are now under the same Master Data Management umbrella. But the needs they tried to fulfill were different.
Usually, when dealing with customer information, you are mostly aggregating data created from outside , but record in several systems in your IT. The name, address, etc.. of your customers are what they are, you didn’t created it. Same for the data model, what is a name, what is an address is fairly common. so, data quality tools could be predefined (fields to match, name/address standardization). Your pain is to make sure all your systems have the same information about your customers. You usually have a lot of customers, hundreds of thousands, even millions.
So, the focus was on integration, services, transactional speed, could be pre-defined data model for customer as we said it is fairly stable across industries.
Usually, when dealing with product information, you are aggregating data, right, but you are also adding a lot of your own information on the product data. Many people in your enterprise are involved about enhancing product information. What is a product data model is completely different across industries, and could evolve a lot even for one company.
So, the focus was data modeling flexibility – even on the fly, workflows, business user interfaces.
It is not that what could be done with one couldn’t be done with the other one. Both kind are trying to get functionalities from the other side, but they still have their strengths and weaknesses.”
So we can ad that:
• Party master data quality is relying on integration
• Product master data quality is based on modeling flexibility
On the MDM community Dan Power says:
“I wrote a piece on this topic, in the May 2009 issue of Information Management magazine.
Basic points were that product information is more complex than customer data, that product data usually cannot be standardized against an external reference source like a postal service or D&B, and that hierarchy management requirements can be more complex as well.”
Good points here- in my experience one of the interesting and challenging things about product information (particularly hierarchy management, categorization, attributes) is that there are often a VERY large number of different views.
Internally, the Finance, Sales and Manufacturing organisations can group products in a number of different ways. And let’s not even start to talk about the sorts of diverse concepts the Marketing department can hold in its mind about a product at the same time.
And while you are right that overall, external reference data is less available, in some industries (particularly those that are regulated) there are lots of external standards.
I spent quite a bit of time managing data in the Cement industry, and can tell you that not only are there lots and lots of product quality, use and regulatory specifications for cements of all types from external organisations, those rules change from country to country, as do the names- yet often a product can be manufactured in one country and shipped to another- as it crosses the border, the product magically changes everything- names, categories even specifications, as many specifications have areas where overlap exists.
Finally, just for fun, another interesting feature of products in process or bulk industries is that raw materials are also finished products, multiple finished products can be combined to make either finished products or raw materials, and blending can happen very close to the customer- in real time. So what? Well, those Marketing guys often want to know the product in terms of actual use- (and branding) so tracking all that makes for some interesting fun.
Great post Henrik!
Thanks James. My deeper product information management experiences include working at a:
• Insurance company (domestic)
• Seafood wholesale organization (with worldwide sale)
• Food additive manufacturer (with worldwide sale)
Many different perspectives indeed.
On the MDM Community Rob Small comments:
“On an orthogonal note, from a global implementation perspective the influence of data privacy laws has a huge impact on the approach to architecting, developing, testing and ongoing operation of customer-oriented MDM solutions that you just don’t find with product.”
I guess the dynamic nature of the product attribute significantly contribute to the complex nature of product information management projects. The first challenge is to come up with a data model which support these diverse attributes. Secondly, the categorization of products. In so many implementations I have worked, each involved categorization of items (or products) under multiple hierarchies (Product hierarchy, Approval hierarchy, financial hierarchy etc) to better manage products.
While the customer master is simple and often comprises of commonly used attribute sets across industries, the standardization and consistent representation of data (Ex: all address follow consistent format) take significant effort.
In one of my recent blog I discussed about challenges while implementing product master data management hub and the link to that is below.
Thanks for adding in Prashanta.