What is a Golden Record within Data Management?

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

A golden record is optimized towards meeting data quality dimensions as:

  • Being a unique representation of the real world entity described
  • Having a complete description of that entity covering all purposes of use in the enterprise
  • Holding the most current and accurate data values for the entity described

In Multidomain 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 Record

Having a golden record that facilitates a single view of a 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 as examined in the post Three Master Data Survivorship Approaches.

In lesser degree we see the same challenges in getting a single view of suppliers and 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.

There are party identification systems available. Most countries have national ID systems for both citizens (however in most countries mostly restricted to public administration) and organizations. There is Legal Entity Identifier (LEI) concept slowly penetrating in financial services. Also, there are commercial organization identifiers as the Duns Number available.

Location Golden Record

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.

Location 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 are 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 John Smith being the same real world entity is dependent on whether that location is a single-family house or a nursing home.

Location identification concepts revolves around postal adresses, which are fluffy and varies in format by country, and geocoding systems as latitude/longitude, UTM coordinates, WGS coordinates and more.

Golden Records

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.

While 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 you must rely more on second party master data meaning sharing product master data within the business ecosystems where you operate.

The none-profit organization GS1 has done a lot in implementing the Global Trade Item Number (GTIN) based on the Universal Product Code (UPC) and the European Article Number (EAN) concept. However there are still some challenges in this concept around packaging levels and more.

Asset (or Thing) Golden Record

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.

Tools That Can Help

This site has a list of innovative MDM and DQM solution that can help you mastering golden records. Check out the list here.

Analyst MDM / PIM / DQM Solution Reports Update Mid 2020

Analyst firms occasionally publish market reports with a generic solution overview for Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).

Here is an overview of the latest major reports:

Analyst Rankings

The next expected reports include:

  • Information Difference yearly MDM landscape probably later this month
  • Gartner Data Quality Tool Magic Quadrant scheduled for 31st July

PS: You can check out many of the included solutions on This Disruptive MDM / PIM / DQM List.

PPS: You can get a free ranking that also include the rising stars on the solution market and is based on your context, scope and requirements here.

Popular Entries on The Resource List

This site has a list of white papers, ebooks, reports and webinars from solution and service providers.

The aim is to give inspiration for organizations having the quest to implement or upgrade their Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) capability.

The list has now been online in a month and it is time to look at which entries that until now have been the most popular in terms of click through. These are:

ROI of MDM, PIM and DQM

Exploring The ROI of PIM and MDMHave you ever wondered how to effectively evaluate the return on investment (ROI) of a Product Information Management (PIM) and Master Data Management (MDM) implementation? Then, take a look at some real-life examples. Download the Enterworks ebook on Exploring The ROI of PIM and MDM.

MDM, PIM and DQM market overview

The State of Product Information Management 2020Get an overview of why PIM solutions are implemented in more and more organizations, which capabilities a 2020 PIM solution needs to cover, where the market is heading and who the PIM vendors in the market are and how this affect your purchase of PIM. Download the Dynamicweb PIM white paper The State of Product Information Management 2020. 

MDM, PIM and DQM implementation

virtual-conference-webcast-revConferences cancelled? Stuck working from home? Bring the conferences to you with an virtual MDM conference. Don’t miss this must see 6 week live webcast series and hear what other companies are doing in the world of MDM along with best practices and workshops by industry experts.. Register for this Enterworks webcast series at the Everything Master Data Management (MDM) Virtual Conference.

Extended MDM

Intelligent Data Hub - Taking MDM to the Next LevelMDM solutions have been instrumental in solving core data quality issues in a traditional way, focusing primarily on simple master data entities such as customer or product. Organizations now face new challenges with broader and deeper data requirements to succeed in their digital transformation. Help your organization through a successful digital transformation while taking your MDM initiative to the next level. Download the Semarchy white paper Intelligent Data Hub – Taking MDM to the Next Level.

Data Quality

4 Keys to Unlocking Data Quality with MDMBusinesses today face a rapidly growing mountain of content and data. Mastering this content can unlock a whole new level of Business Intelligence for your organization and impact a range of data analytics. It’s also crucial for operational excellence and digital transformation. Download the 1WorldSync and Enterworks ebook on 4 Keys to Unlocking Data Quality with MDM.

Next To Come

More resources from solution and service vendors are on the way. Additionally, there will also be a Case Story List with success stories from various industries. Stay tuned.

If you have comments, suggestions and/or entries to be posted (yes, there is a very modest fee), then get in touch here:

 

Deduplication and Master Data Management

A core intersection between Data Quality Management (DQM) and Master Data Management (MDM) is deduplication. The process here will basically involve:

  • Match master data records across the enterprise application landscape, where these records describe the same real-world entity most frequently being a person, organization, product or asset.
  • Link the master data records in the best fit / achievable way, for example as a golden record.
  • Apply the master data records / golden record to a hierarchy.

Data Matching

The classic data matching quest is to identify data records that refer to the same person being an existing customer and/or prospective customer. The first solutions for doing that emerged more than 40 years ago. Since then the more difficult task of identifying the same organization being a customer, prospective customer, vendor/supplier or other business partner has been implemented while also solutions for identifying products as being the same have been deployed.

Besides using data matching to detect internal duplicates within an enterprise, data matching has also been used to match against external registries. Doing this serves as a mean to enrich internal records which also helps in identifying internal duplicates.

Master Data Survivorship

When two or more data records have been confirmed as duplicates there are various ways to deal with the result.

In the registry MDM style, you will only store the IDs between the linked records so the linkage can be used for specific operational and analytic purposes.

Further, there are more advanced ways of using the linkage as described in the post Three Master Data Survivorship Approaches.

Master Data Survivorship Approaches

One relatively simple approach is to choose the best fit record as the survivor in the MDM hub and then keep the IDs of the MDM purged records as a link back to the sourced application records.

The probably most used approach is to form a golden record from the best fit data elements, store this compiled record in the MDM hub and keep the IDs of the linked records from the sourced applications.

A third way is to keep the sourced records in the MDM hub and on the fly compile a golden view for a given purpose.

Hierarchy Management

When you inspect records identified as a duplicate candidate, you will often have to decide if they describe the same real-world entity or if they describe two real-world entities belonging to the same hierarchy.

Instead of throwing away the latter result, this link can be stored in the MDM hub as well as a relation in a hierarchy (or graph) and thus support a broader range of operational and analytic purposes.

The main hierarchies in play here are described in the post Are These Familiar Hierarchies in Your MDM / PIM / DQM Solution?

Family consumer citizenWith persons in private roles a classic challenge is to distinguish between the individual person, a household with a shared economy and people who happen to live at the same postal address. The location hierarchy plays a role in solving this case. This quest includes having precise addresses when identifying units in large buildings and knowing the kind of building. The probability of two John Smith records being the same person differs if it is a single-family house address or the address of a nursing home.

Family companyOrganizations can belong to a company family tree. A basic representation for example used in the Dun & Bradstreet Worldbase is having branches at a postal address. These branches belong a legal entity with a headquarter at a given postal address, where there may be other individual branches too. Each legal entity in an enterprise may have a national ultimate mother. In multinational enterprises, there is a global ultimate mother. Public organizations have similar often very complex trees.

Product hierachyProducts are also formed in hierarchies. The challenge is to identify if a given product record points to a certain level in the bottom part of a given product hierarchy. Products can have variants in size, colour and more. A product can be packed in different ways. The most prominent product identifier is the Global Trade Identification Number (GTIN) which occur in various representations as for example the Universal Product Code (UPC) popular in Orth America and European (now International) Article Number (EAN) popular in Europe. These identifiers are applied by each producer at the product packing variant level.

Solutions Available

When looking for a solution to support you in this conundrum the best fit for you may be a best-of-breed Data Quality Management (DQM) tool and/or a capable Master Data Management (MDM) platform.

This list has the most innovative candidates here.

B2B2C in MDM, PIM and DQM

The Business-to-Business-to-Consumer (B2B2C) scenario is becoming of increasing importance in Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).

This scenario is usually seen in manufacturing including pharmaceuticals as examined in the post Six MDMographic Stereotypes.

One challenge here is how to extend the capabilities in MDM / PIM / DQM solutions that are build for Business-to-Business (B2B) and Business-to-Consumer (B2C) use cases. Doing B2B2C requires a Multidomain MDM approach with solid PIM and DQM elements either as one solution, a suite of solutions or as a wisely assembled set of best-of-breed solutions.

B2B2C MDM PIM DQM

In the MDM sphere a key challenge with B2B2C is that you probably must encompass more surrounding applications and ensure a 360-degree view of party, location and product entities as they have varying roles with varying purposes at varying times tracked by these applications. You will also need to cover a broader range of data types that goes beyond what is traditionally seen as master data.

In DQM you need data matching capabilities that can identify and compare both real-world persons, organizations and the grey zone of persons in professional roles. You need DQM of a deep hierarchy of location data and you need to profile product data completeness for both professional use cases and consumer use cases.

In PIM the content must be suitable for both the professional audience and the end consumers. The issues in achieving this stretch over having a flexible in-house PIM solution and a comprehensive outbound Product Data Syndication (PDS) setup.

As the middle B in B2B2C supply chains you must have a strategic partnership with your suppliers/vendors with a comprehensive inbound Product Data Syndication (PDS) setup and increasingly also a framework for sharing customer master data taking into account the privacy and confidentiality aspects of this.

This emerging MDM / PIM / DQM scope is also referred to as Multienterprise MDM.