The previous acquisitions have strengthened the Precisely offerings around data quality for the customer master data domain and the adjacent location domain.
The Winshuttle take over will make Precisely a multidomain vendor adding cross domain capabilities and specific product domain capabilities.
The original Winshuttle capabilities revolves around process automation for predominately SAP environments covering all master data domains and further Application Data Management (ADM).
As Winshuttle recently took over the Product Information Management (PIM) solution provider Enterworks, this will bring capabilities around product master data management and thus make Precisely a provider for a broad spectrum of master data domains.
The interesting question will be in what degree Precisely over the time will be willing to and able to integrate these different solutions so a one-stop-shopping experience will become a one-stop digital experience for their clients.
In theory, you should combine the concept for these two master domains in some degree. The reasons are:
There is always an overlap of the real-world entities that has both a customer and a supplier role to your organization. The overlap is often bigger than you think not at least if you include the overlap of company family trees that have members in one of these roles.
The basic master data for these master data domains are the same: Identification numbers, names, addresses, means of communication and more.
The third-party enrichment opportunities are the same. The most predominant possibilities are integration with business directories (as Dun & Bradstreet and national registries) and address validation (as Loqate and national postal services).
In practice, the problem is that the business case for customer MDM and supplier MDM may not be realized at the same time. So, one domain will typically be implemented before the other depending on your organization’s business model.
Most MDM solutions must coexist with an – or several – ERP solutions. All popular enterprise grade ERP solutions have adapted the business partner view with a common data model for basic supplier and customer data. This is the case with SAP S/4HANA and for example the address book in Microsoft Dynamics AX and Oracle JD Edwards.
MDM solutions themselves does also provide for a common structure. If you model one domain before the other, it is imperative that you consider all business partner roles in that model.
Data Governance Considerations
A data governance framework may typically be rolled out one master data domain at the time or in parallel. It is here essential that the data policies, data standards and business glossary for basic customer master data and basic supplier master data is coordinated.
Business Case Considerations
The business case for customer MDM will be stronger if the joint advantages with supplier MDM is incorporated – and vice versa.
This includes improvement in customer/supplier engagement and the derived supply/value chain effectiveness, cost sharing of third-party data enrichment service expenses and shared gains in risk assessment.
When working with the product domain in Master Data Management (MDM) and with Product Information Management (PIM) we have traditionally been working with the product model meaning that we manage data about how a product that can be produced many times in exactly the same way and resulting in having exactly the same features. In other words, we are creating a digital twin of the product model.
Serial number or other identification as for example the Unique Device Identification (UDI) known in healthcare
Manufacturing date and time
Current and historical position
Current and historical owner
Current and historical installer, maintainer and other caretaker
Produced sensor data if it is a smart device
There is a substantial business potential in being better than your competitor in managing product instances. This boils down to that data is power – if you use the data.
When managing this data, we are building a digital twin of the product instance.
Maintaining that digital twin is a collaborative effort involving the manufacturer, the logistic service provider, the owner, the caretaker, and other roles. For that you need some degree of Interenterprise MDM.
Many analyst market reports in the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space have a generic ranking of the vendors.
The trouble with generic ranking is that one size does not fit all.
On the sister site to this blog, The Disruptive MDM / PIM / DQM List, there is no generic ranking. Instead there is a service where you can provide your organization’s context, scope and requirements and within 2 to 48 hours get Your Solution List.
The selection model includes these elements:
Your context in terms of geographical reach and industry sector.
Your scope in terms of data domains to be covered and organizational scale stretching from specific business units over enterprise-wide to business ecosystem wide (interenterprise).
Your specific requirements covering the main capabilities that differentiate the vendors on market.
A model that combines those facts into a rectangle where you can choose to:
Go ahead with a Proof of Concept with the best fit vendor
Make an RFP with the best fit vendors in a shortlist
Examine a longlist of best fit vendors and other alternatives like combining more than one solution.
Master Data Management (MDM) and the overlapping Product Information Management (PIM) discipline is the centre of which the end-to-end data supply chain revolves around in your enterprise.
The main processes are:
Onboard Customer Data
It starts and ends with the King: The Customer. Your organization will probably have several touchpoints where customer data is captured. MDM was born out of the Customer Data Integration (CDI) discipline and a main reason of being for MDM is still to be a place where all customer data is gathered as exemplified in the post Direct Customers and Indirect Customers.
Onboard Vendor Data
Every organization has vendors/suppliers who delivers direct and indirect products as office supplies, Maintenance, Repair and Operation (MRO) parts, raw materials, packing materials, resell products and services as well. As told in a post on this blog, you have to Know Your Supplier.
Enrich Party Data
There are good options for not having to collect all data about your customers and vendors yourself, as there are 3rd party sources available for enriching these data preferable as close to capture as possible. This topic was examined in the post Third-Party Data and MDM.
Onboard Product Data
While a small portion of product data for a small portion of product groups can be obtained via product data pools, the predominant way is to have product data coming in as second party data from each vendor/supplier. This process is elaborated in the post 4 Supplier Product Data Onboarding Scenarios.
Transform Product Data
As your organization probably do not use the same standard, taxonomy, and structure for product data as all your suppliers, you have to transform the data into your standard, taxonomy, and structure. You may do the onboarding and transformation in one go as pondered in the post The Role of Product Data Syndication in Interenterprise MDM.
Consolidate Product Data
If your organization produce products or you combine external and internal products and services in other ways you must consolidate the data describing your finished products and services.
Enrich Product Data
Besides the hard facts about the products and services you sell you must also apply competitive descriptions of the products and services that makes you stand out from the crowd and ensure that the customer will buy from you when looking for products and services for a given purpose of use.
Customize Product Data
Product data will optimally have to be tailored for a given geography, market and/or channel. This includes language and culture considerations and adhering to relevant regulations.
Personalize Product Data
Personalization is one step deeper than market and channel customization. Here you at point-of-sale seek to deliver the right Customer Experience (CX) by exercising Product eXperience Management (PXM). Here you combine customer data and product data. This quest was touched in the post What is Contextual MDM?
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.