Golden Records in Multi-Domain MDM

The term golden record is a core concept within Master Data Management (MDM). A golden record is a representation of a real world entity that may be compiled from multiple different representations of that entity in a single or in multiple different databases within the enterprise system landscape.

GoldIn Multi-domain MDM we work with a range of different entity types as party (with customer, supplier, employee and other roles), location, product and asset. The golden record concept applies to all of these entity types, but in slightly different ways.

Party Golden Records

Having a golden record that facilitates a single view of customer is probably the most known example of using the golden record concept. Managing customer records and dealing with duplicates of those is the most frequent data quality issue around.

If you are not able to prevent duplicate records from entering your MDM world, which is the best approach, then you have to apply data matching capabilities. When identifying a duplicate you must be able to intelligently merge any conflicting views into a golden record.

In lesser degree we see the same challenges in getting a single view of suppliers and, which is one of my favourite subjects, you ultimately will want to have a single view on any business partner, also where the same real world entity have both customer, supplier and other roles to your organization.

Location Golden Records

Having the same location only represented once in a golden record and applying any party, product and asset record, and ultimately golden record, to that record may be seen as quite academic. Nevertheless, striving for that concept will solve many data quality conundrums.

GoldLocation management have different meanings and importance for different industries. One example is that a brewery makes business with the legal entity (party) that owns a bar, café, restaurant. However, even though the owner of that place changes, which happens a lot, the brewery is still interested in being the brand served at that place. Also, the brewery wants to keep records of logistics around that place and the historic volumes delivered to that place. Utility and insurance is other examples of industries where the location golden record (should) matter a lot.

Knowing the properties of a location also supports the party deduplication process. For example, if you have two records with the name “John Smith” on the same address, the probability of that being the same real world entity is dependent on whether that location is a single-family house or a nursing home.

Product Golden Record

Product Information Management (PIM) solutions became popular with the raise of multi-channel where having the same representation of a product in offline and online channels is essential. The self-service approach in online sales also drew the requirements of managing a lot more product attributes than seen before, which again points to a solution of handling the product entity centralized.

In large organizations that have many business units around the world you struggle with having a local view and a global view of products. A given product may be a finished product to one unit but a raw material to another unit. Even a global SAP rollout will usually not clarify this – rather the contrary.

GoldWhile third party reference data helps a lot with handling golden records for party and location, this is lesser the case for product master data. Classification systems and data pools do exist, but will certainly not take you all the way. With product master data we must, in my eyes, rely more on second party master data meaning sharing product master data within the business ecosystems where you are present.

Asset (or Thing) Golden Records

In asset master data management you also have different purposes where having a single view of a real world asset helps a lot. There are namely financial purposes and logistic purposes that have to aligned, but also a lot of others purposes depending on the industry and the type of asset.

With the raise of the Internet of Things (IoT) we will have to manage a lot more assets (or things) than we usually have considered. When a thing (a machine, a vehicle, an appliance) becomes intelligent and now produces big data, master data management and indeed multi-domain master data management becomes imperative.

You will want to know a lot about the product model of the thing in order to make sense of the produced big data. For that, you need the product (model) golden record. You will want to have deep knowledge of the location in time of the thing. You cannot do that without the location golden records. You will want to know the different party roles in time related to the thing. The owner, the operator, the maintainer. If you want to avoid chaos, you need party golden records.

We Need More Product Data Lake Ambassadors

ambassador

Product Data Lake is the new solution to sharing product information between trading partners. While we see many viable in-house solutions to Product Information Management (PIM), there is a need for a solution to exchange product information within cross company supply chains between manufacturers, distributors and retailers.

Completeness of product information is a huge issue for self-service sales approaches as seen in ecommerce. 81 % of e-shoppers will leave a webshop with lacking product information. The root cause of missing product information is often an ineffective cross company data supply chain, where exchange of product data is based on sending spreadsheets back and forth via email or based on biased solutions as PIM Supplier Portals.

However, due to the volume of product data, the velocity required to get data through and the variety of product data needed today, these solutions are in no way adequate or will work for everyone. Having a not working environment for cross company product data exchange is hindering true digital transformation at many organizations within trade.

As a Product Information Management professional or as a vendor company in this space, you can help manufacturers, distributors and retailers in being successful with product information completeness by becoming a Product Data Lake ambassador.

The Product Data Lake encompasses some of the most pressing issues in world-wide sharing of product data:

The first forward looking professionals and vendors in the Product Information Management realm have already joined. I would love to see you as well as our next ambassador.

Interested? Get in contact:

PIM Supplier Portals: Are They Good or Bad?

A recent discussion on the LinkedIn Multi-Domain MDM group is about vendor / supplier portals as a part of Product Information Management implementations.

A supplier portal (or vendor portal if you like) is usually an extension to a Product Information Management (PIM) solution. The idea is that the suppliers of products, and thus providers of product information, to you as a downstream participant (distributor or retailer) in a supply chain, can upload their product information into your PIM solution and thus relieving you of doing that. This process usually replace the work of receiving spreadsheets from suppliers in the many situations where data pools are not relevant.

In my opinion and experience, this is a flawed concept, because it is hostile to the supplier. The supplier will have hundreds of downstream receivers of products and thus product information. If all of them introduced their own supplier portal, they will have to learn and maintain hundreds of them. Only if you are bigger than your supplier is and is a substantial part of their business, they will go with you.

Broken data supply chainAnother concept, which is the opposite, is also emerging. This is manufacturers and upstream distributors establishing PIM customer portals, where suppliers can fetch product information. This concept is in my eyes flawed exactly the opposite way.

And then let us imagine that every provider of product information had their PIM customer portal and every receiver had their PIM supplier portal. Then no data would flow at all.

What is your opinion and experience?

Painting WWII Bombers and Product Data: It Is All in the Details

Today’s guest blog post is from Dan O’Connor, a United States based product data taxonomy guru. Here are Dan’s thoughts on product data quality:

I have had a few days off this past week while I transition to a new role. During that time, I’ve had time to reflect on many things, as well as pursue some personal interests. I talked with peers and former co-workers, added a fresh coat of paint to my basement, and worked on some WWII era bomber models I purchased before Christmas but never had time for.

bomberpic1The third pursuit was a rather interesting lesson in paying attention to details. The instructions would say to paint an individual piece one color, but that piece would comprise of several elements that should never be painted a single color. For example, the flight yokes on Mitchell were planned to be painted black, but in viewing pictures online I saw that certain parts were white, red and aluminum. I therefore painted them appropriately. These yokes are less than an inch long and a couple millimeters wide, but became much more impressive with an appropriate smattering of color.

Flight Yokes and Product Taxonomies

It is this attention to detail that made me think about how product taxonomies are developed. Some companies just follow the instructions, and end up with figurative “black flight yokes”. These taxonomies perform adequately, allowing a base level of product detail to be established. Web sites and catalogs can be fed with data and all is well.

Other companies see past the black flight yokes. They need the red buttons, the white grips, and the silver knobs because they know these data points are what make their product data more real. They could have followed the instructions, but being better than the instructions was more important.

Imagine for a second that the instructions were the mother of the data and the plane itself was the father. According to the mother plain black flight yokes are sufficient. The father, while capable of being so much more, ends up with the dull data the mother provides. Similarly, if the plane/father has no options that allow it to be more colorful the instructions from the mother are meaningless beyond the most basic interpretations.

The Mother and Father of Product Data

To some my analogy might be a stretch, but think of it in these terms: Your product taxonomy is the mother of your product data, and the architecture that supports that taxonomy is the father. If your taxonomy only supports a generic level of data, the architecture supporting it cannot add more detail. If the architecture is limited the most robust product taxonomy will still only support the most basic of data. Your product data quality is limited by the taxonomy you build and the systems you use to manage it. If both are well-developed beautiful product data is born. If one or both is limited your product data will be an ugly mess.

Why is this important? Product data does more than validate the image has the right color on a web site, or make sure an item will fit in your kitchen or TV room. Product data feeds faceting experiences so that customers to your web site can filter down to the perfect product. Without facets customers have to search manually through more products, and may get frustrated and leave your web site before finding the item they want.

Product data also can feed web site search, allowing customers to find your products using product descriptors instead of just product numbers and short descriptions. These search options also filter out unnecessary results, allowing a customer to find the perfect product faster.

Product data might also be used by the marketplaces that sell your data, your catalogs, product data sheets, and even your shelf tags in your retail locations. Having one consistent source of data for those usages avoids customer confusion when they approach your business from an omni-channel perspective. Having to find a product on a shelf when the mobile experience has a different description is painful and leads to bad customer experiences.

Lastly, moving data between your business and others is problematic at the best of times. Poor product data leads to bad data dissemination, which leads to bad customer experiences across your syndication channels. If you cannot represent your data in a single logically message internally your external message will be chaotic and confusing for your guests.

The Elements of a Product Data Program

Therefore, creating a good product taxonomy is not just about hiring a bunch of taxonomists and having them create a product taxonomy. It is about taxonomy best practices, data governance, and understanding your entire product data usage ecosystem, both internally and externally. It is understanding what role Product Information Management systems play in data management, and more importantly what role they do not.

Therefore, in the analogy of a mother product taxonomy and a father architecture creating data, there are siblings, aunts, uncles, and other relatives to understand as well. A lack of understanding in any one of these relationships can cause adverse data quality issues to shine through. It is estimated that companies lose an average of $8 Million US dollars a year (ROI on Data Quality, 2014) due to data quality issues. Can your business afford to keep ignoring your product data issues?

Dan O’Connor is a Product Taxonomy, Product Information Management (PIM), and Product Data Consultant and an avid blogger on taxonomy topics. He has developed taxonomies for major retails as well as manufacturers and distributors, and assists with the development of product data models for large and small companies. See his LinkedIn bio for more information.

Party and Product: The Core Entities in Most Data Models

Party and product are the most frequent master data domains around.

Often you meet party as one of the most frequent party roles being customer and supplier (or vendor) or by another term related to the context as for example citizen, patient, member, student, passenger and many more. These are the people and legal entities we are interacting with and with whom we usually exchange money – and information.

Product (or material) is the things we buy, make and sell. The goods (or services) we exchange.

In my current venture called Product Data Lake our aim to serve the exchange of information about products between trading partners who are customers and suppliers in business ecosystems.

For that, we have been building a data model. Below you see our first developed conceptual data model, which has party and product as the core entities.

PDL concept model.png

As this is a service for business ecosystems, another important entity is the partnership between suppliers and customers of products and the information about the products.

The product link entity in this data model is handling the identification of products by the pairs of trading partners. In the same way, this data model has link entities between the identification of product attributes at pair of trading partners (build on same standards or not) as well as digital asset types.

If you are offering product information management services, at thus being a potential Product Data Lake ambassador, or you are part of a business ecosystem with trading partners, I will be happy to discus with you about adding handling of trading partnerships and product information exchange to your current model.

 

The Intersections of 360 Degree MDM

In the Master Data Management (MDM) realm we have some common notions, being

  • 360 degree Customer Master Data Management, meaning how different views on customers in a company’s various business units and sales channels can be handled as a shared single view.
  • 360 degree Vendor (or Supplier) Master Data Management, meaning how different views on vendors/suppliers in a company’s various business units and supply chains can be handled as a shared single view.
  • 360 degree Vendor Product Master Data Management, meaning how different views on products in a company’s various business units, sales channels and supply chains can be handled as a shared single view.

Multi-Side MDM

Multi-Domain Master Data Management (MDM) is the discipline that brings all these views together. Here it is not enough that the same brand of technology is used for all three domains. Handling the intersections is the important part.

The intersection of Vendor/supplier and Customer is known as the Party Master Data domain. My recommendation is to have a common party (or business partner) structure for identification, names, addresses and contact data. This should be supported by data quality capabilities strongly build on external reference data (third party data). Besides this common structure, there should be specific structures for customer, vendor/supplier and other party roles.

The Vendor/supplier and Product Master Data intersection is related to buying products, namely how to on-board data about the vendor/supplier as a party, in the vendor role (financial stuff), the supplier role (logistic stuff) and then on-boarding his product information. My recommendation for on-boarding product information from suppliers being manufacturers is to make this a Win-Win solution for both parties as described in the post How a PLM-2-PIM Solution Becomes a WIN-WIN Solution.

The Customer and Product Master Data intersection is about supporting how you sell products. The term omnichannel is popular for that these days. Again, Product Information Management (PIM) plays a crucial role here and my recommendations for that is expressed in the post Adding Business Ecosystems to Omnichannel.