Direct Customers and Indirect Customers

When working with Master Data Management (MDM) for the customer master data domain one of the core aspects to be aware of is the union, intersection and difference between direct customers and indirect customers.

Direct customers are basically those customers that your organization invoice.

Indirect customers are those customers that buy your organizations products and services from a reseller (or marketplace). In that case the reseller is a direct customer to your organization.

The stretch from your organization via a reseller organization to a consumer is referred to as Business-to-Business-to-Consumer (B2B2C). This topic is told about in the post B2B2C in Data Management. If the end user of the product or service is another organization the stretch is referred to as Business-to-Business-to-Business (B2B2B).

The short stretch from your organization to a consumer is referred to as Direct-to-Consumer (D2C).

It does happen, that someone is both a direct customer and an indirect customer either over time and/or over various business scenarios.

IT Systems Involved

If we look at the typical IT systems involved here direct customers are managed in an ERP system where the invoicing takes place as part of the order-to-cash (O2C) main business process. Products and services sold through resellers are part of an order-to-cash process where the reseller place an order to you when their stock is low and pays you according to the contract between them and you. In ERP lingo, someone who pays you has an account receivable.

Typically, you will also handle the relationship and engagement with a direct customer in a CRM system. However, there are often direct customers where the relationship is purely administrative with no one from the salesforce involved. Therefore, these kinds of customers are sometimes not in the CRM system. They are purely an account receivable.

More and more organizations want to have a relationship with and engage with the end customer. Therefore, these indirect customers are managed in the CRM system as well typically where the salesforce is involved and increasingly also where digital sales services are applied. However, most often there will be some indirect customers not encompassed by the CRM system.

The Role of Master Data Management (MDM) in the context of customer master data is to be the single source for all customer data. So, MDM holds the union of customer master data from the ERP world and the CRM world.

An MDM platform also has the capability of encompassing other sources both internal ones and external ones. When utilized optimally, an MDM platform will be able to paint a picture of the entire space of where your direct customers and indirect customers are.

Business Opportunities

Having this picture is of course only interesting if you can use it to obtain business value. Some of the opportunities I have stumbled upon are:

  • More targeted product and service development by having more insight into the whole costumer space leading to growth advancements
  • Optimized orchestration of supply chain activities by having complete insight into the whole costumer space and thereby fostering cost savings
  • Improved ability to analyse the consequences of market change and changes in the economic environment in geographies and industries covered leading to better risk management.

Which business opportunities do you see arise for your organization by having a complete overview of the union, intersection and difference between your direct customers and indirect consumers?

Digital Twin and MDM

A digital twin is in short digital data representing a physical object.

Master Data Management (MDM) has since the discipline emerged in the 00’s been about managing data representing some very common physical objects like persons, products and locations though with a layer of context in between:

  • Persons are traditionally described with data aimed for a given role like a person being a customer, patient, student, contact, employee, and many more specific roles.
  • Products are traditionally described as a product model with data that are the same for a product being mass produced.
  • Locations are typically described as a postal address and/or a given geocode.

With the rise of digitalization and Internet of Things (IoT) / Industry 4.0 the need for having a more real-world view of persons, a broader view of products, and more useful views of locations arise together with the need of similar digital twins for other object types.

The Enterprise Knowledge Graph tool provider Stardog has described this topic in the post Create your Digital Twin with an Enterprise Knowledge Graph.

As Knowledge Graph and (extended) MDM can coexist very well, the same objectives are true for MDM as well.

Some of the use cases I have stumbled on are:

  • Manage generic data about a person and belonging organizations as a digital twin encompassing all historic, current, and sought roles related to your organization. Data privacy must be adhered to here, however issues as opt-in and opt-out must also be handled across roles.
  • Manage specific data about each instance of a product that is a smart device, which is true for more and more products models. Such a digital twin is described in the post 3 Old and 3 New Multi-Domain MDM Relationship Types.
  • Manage complex data about a location as boundaries, placements in geographic hierarchies, location names and property descriptions as a digital twin.
  • Manage data about plants, machines, vehicles, warehouses, stores as digital twins using an MDM approach.     

Which digital twins have you stumbled upon where an MDM approach is useful?

Deduplication as Part of MDM

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 while this 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 in source and target applications.

Further, there are more advanced ways of using the linkage as described in the post Three 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 citizen

With 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 company

Organizations 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 hierachy

Products 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 North America and European (now International) Article Number (EAN) popular in Europe. These identifiers are applied by each producer (and in some cases distributor) 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 Disruptive MDM / PIM /DQM List has the most innovative candidates here.

Four Ways You as a Merchant Can Exploit Product Data Syndication

Product Data Syndication has become an essential capability to manage within digital transformation at merchants as wholesalers and retailers. There are 4 main scenarios.

1: Inbound product data syndication of resell (direct) products

The process involves getting the most complete set of product information available from the supplier in order to fit the optimal set of product information needed to support the often self-service based buying decision by your customers.

This can be done by direct feeds from suppliers or through feeds via the various data pools that exist in different industries and geographies.

2: Inbound product data syndication for indirect products

You also need product data for parts used in Maintenance, Repair and Operation within facility management around logistic facilities, offices, and other constructions where products for MRO are needed. With the rise of the Internet of Things (IoT) these products are becoming more and more intelligent and are operated in an automatic way. For that, product information is needed in an until now unseen degree.

Every organization needs products and services as furniture, office supplies, travel services and much more. The need for onboarding product data for these purchases is still minimal compared to the above-mentioned scenarios. However, a foreseeable increased use of Artificial Intelligence (AI) in procurement operations will ignite the requirement for product data onboarding for this scenario too in the coming years.

3: Outbound product data syndication to marketplaces

Selling products on marketplaces has become a popular alternative to selling via ones own ecommerce site. While price and delivery options are main drivers here there are still more business to win via this channel if you can provide better and more unique product information than other resellers of the same product.

4: Outbound product data syndication to customers using products as parts

Your business-to-business (B2B) customers may also need product data for parts used directly in production or in Maintenance, Repair and Operation in production facilities and within facility management around logistic facilities, offices, and other constructions where products for MRO are needed. With the rise of the Internet of Things (IoT) these products are becoming more and more intelligent and are operated in an automatic way. For that, product information is needed in an until now unseen degree.

The Need for Collaborative Product Data Syndication

The sharp rise of the need product data syndication calls for increased collaboration through data partnerships in business ecosystems.

In the Product Data Lake venture I am working on now, we have made a framework – and a piece of Software as a Service – that is able to leverage the concepts of inbound and outbound Product Data Syndication and enable the four mentioned ways of utilizing product data syndication to create better business outcomes for you as a merchant.

Product Data Lake acts as a single point of digital contact for suppliers and customers in the product data supply chain which also provide you as a merchant with single place in the cloud from where your Product Information Management (PIM), ERP and eCommerce applications get and put external product data feeds.

This concept enables automated self-service by suppliers and customers who also can subscribe to Product Data Lake. In The Product Data Lake platform you can control the product portfolio and the product attribute set you are sharing with each business partner.

Learn more about Product Data Lake here.

Product Data Supply Chain Management in Resell

Analyst MDM / PIM / DQM Solution Reports Update March 2021

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:

MDM PIM DQM solutions analyst firms

3 ways to learn more:

  • You can check out many of the included solutions on The Disruptive MDM / PIM / DQM List.
  • 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.
  • You can book a free short online meeting with me for further discussion on your business case as part of my engagement at the consultancy firm Astrocytia here.

Five Ways You as a Manufacturer Can Exploit Product Data Syndication

Product Data Syndication has become an essential capability to manage within digital transformation at manufacturers. There are 5 main scenarios.

1: Outbound product data syndication for finished products

As a manufacturer you need to ensure that self-service buying decisions by the end customer through the channel partner point-of-sale will result in choosing your product instead of a product provided by your competitor.

This is achieved through providing complete product information in a way that is easy onboarded by each of your channel partners – as well as direct customers and marketplaces where this apply.

2: Inbound product data syndication for 3rd party finished products

As a manufacturer you often have a range of products that are not produced inhouse but are essential supplements when selling own produced products.

The process involves getting the most complete set of product information available from the supplier in order to fit the optimal set of product information needed to support the buying decision by the end customer where your own produced products and 3rd party products makes a whole.

3: Inbound product data syndication for raw materials and packaging

Here the objective is to get product information needed to do quality assurance and in organic production apply the right blend in order to produce a consistent finished product.

Also, the increasing demand for measures of sustainability is driving the urge for information on the provenance of the finished product and the packaging including the origin of the ingredients and circumstances of the production of these components. 

4: Inbound product data syndication for parts used in MRO

Product data for parts used in Maintenance, Repair and Operation is an essential scenario related in running the production facilities as well as in facility management around logistic facilities, offices, and other constructions where products for MRO are needed.

With the rise of the Internet of Things (IoT) these products are becoming more and more intelligent and are operated in an automatic way. For that, product information is needed in an until now unseen degree.

5: Inbound product data syndication for other indirect products

Every organization needs products and services as furniture, office supplies, travel services and much more. The need for onboarding product data for these purchases is still minimal compared to the above-mentioned scenarios. However, a foreseeable increased use of Artificial Intelligence (AI) in procurement operations will ignite the requirement for product data onboarding for this scenario too in the coming years.

The Need for Collaborative Product Data Syndication

The sharp rise of the need product data syndication calls for increased collaboration through data partnerships in business ecosystems.

In the Product Data Lake venture I am working on now, we have made a framework – and a piece of Software as a Service – that is able to leverage the concepts of outbound and inbound Product Data Syndication and enable the five mentioned ways of utilizing product data syndication to create better business outcomes for you as a manufacturer.

Product Data Lake acts as a single point of digital contact for suppliers and channel partners in the product data supply chain which also provide you as a manufacturer with single place in the cloud from where your Product Lifecycle Management (PLM), ERP and Product Information Management (PIM) applications get and put external product data feeds.

This concept enables automated self-service by suppliers and channels partners who also can subscribe to Product Data Lake. In The Product Data Lake platform you can control the product portfolio and the product attribute set you are sharing with each business partner.

Learn more about Product Data Lake here.

Product Data Supply Chain Management in Manufacturing

Constellation Research MDM Shortlist Q1 2021

There is a new MDM market report with vendor assessment out. It is the Constellation ShortList™ Master Data Management Q1 2021.

The report highlights a shortlist of the solutions you have to know. This one has 6 solutions:

Compared to the previous shortlist, Stibo Systems has been dropped. The explanation is: “This Q1 2021 update removes Stibo Systems from this ShortList due to what Constellation sees as slow progress on cloud deployment options.”

I find this a bit peculiar.

While cloud MDM is an important theme and Stibo Systems has not been a front runner in this game, it is by far not the only important theme, which strangely also is stated in the reports threshold criteria.

In my work with selecting a longlist/shortlist/PoC candidate for actual MDM considerations at 250 organizations per year via The Disruptive MDM/PIM/DQM List, Stibo Systems is part of many shortlists and is the best fit in some cases.

Also, Stibo Systems is a front runner in some other important MDM themes. One example is Interenterprise MDM through Product Data Syndication.

Interenterprise MDM Will be Hot

Interenterprise Master Data Management is about how organizations can collaborate by sharing master data with business partners in order to optimize own master data and create new data driven revenue models together with business partners.

It is in my eyes one of the most promising trends in the MDM world. However, it is not going to happen tomorrow. The quest of breaking down internal data and knowledge silos within organizations around is still not completed in most enterprises. Nevertheless, there is a huge business opportunity to pursue for the enterprises who will be in the first wave of interenterprise data sharing through interenterprise MDM.

A poll in the LinkedIn MDM – Master Data Management group revealed that MDM practitioners are aware of that Interenterprise MDM will be hot sooner or later:

For the range of industries that work with tangible products, one of the most obvious places to start with Interenterprise MDM is by excelling – in the meaning of eliminating excel files exchange – in Product Data Syndication (PDS). Learn more in the post The Role of Product Data Syndication in Interenterprise MDM.

From Data Quality to Business Outcome

Explaining how data quality improvement will lead to business outcome has always been difficult. The challenge is that there very seldom is a case where you with confidence can say “fix this data and you will earn x money within y days”.

Not that I have not seen such bold statements. However, they very rarely survive a reality check. On the other hand, we all know that data quality problems seriously effect the healthiness of any business.

A reason why the world is not that simple is that there is a long stretch from data quality to business outcome. The stretch goes like this:

  • First, data quality must be translated into information quality. Raw data must be put into a business context where the impact of duplicates, incomplete records, inaccurate values and so on is quantified, qualified and related within affected business scenarios.
  • Next, the achieved information quality advancements must be actionable in order to cater for better business decisions. Here it is essential to look beyond the purpose of why the data was gathered in the first place and explore how a given piece of information can serve multiple purpose of actions.
  • Finally, the decisions must enable positive business outcomes within growth, cost reductions, mitigation of risks and/or time to value. Often these goals are met through multiple chains of bringing data into context, making that information actionable and taking the right decisions based on the achieved and shared knowledge.

Stay tuned – and also look back – on this blog for observations and experiences for proven paths on how to improve data quality leading to positive business outcome.