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.
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?
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.
Disciplines come and go in the data management world. Here is a mind map of the disciplines on top of my mind today. Some of the disciplines goes back to the emerge of IT in the previous millennium and some have risen during the latest years.
Today the 11th January 2021 we should, according to the Gartner publishing schedule, expect a refreshed Gartner Magic Quadrant for Master Data Management (MDM) Solutions.
Historically these quadrants have been delayed possibly due to fighting with vendors objecting to the results herein.
An observation is that the thorough process applied by Gartner makes the results in here a bit behind what is currently happening on the market as touched in the post Why are Analyst Rankings Behind the MDM Market Dynamics? If say the information used in a fresh published quadrant is between a half to a full year old, the latest quadrant to be used in a given tool assessment can be founded on up to 2 years old data.
The last Magic Quadrant for MDM was mentioned in this post.
As touched in the post the two advancing vendors in here were Informatica, who extended their lead, and Semarchy, who became top challenger.
With Informatica it is hard to confirm their position in other analyst reports. Informatica has a dysfunctional relationship with Forrester, so they are not included in their latest MDM reports. Information Difference did not assess Informatica that favourable in their ranking as seen in the post Who is in the MDM Landscape Q2 2020? Will be interesting to see if Gartner keeps having a view on Informatica MDM which is different from most other sources.
Semarchy seems to keep up their momentum from what I hear from the market. Let us see if Gartner reflects that too.
The fastest growing vendor last time was Reltio as reported in the post What has Changed with the Gartner MDM Magic Quadrant? I hope Gartner keeps publishing these revenue estimates, so we can see who has grown the most and who has grown not so much or even shrunk as it happened with IBM and Riversand in the previous check.
Stay tuned for a summary of and link to first free reprints of the refreshed MDM Magic Quadrant.
I am running a service where organizations on the look for a Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) solution can get a list of the best fit solutions for their context, scope and requirements. The service is explained in more details in the post Get Your Free Bespoke MDM / PIM / DQM Solution Ranking.
2020 was a busy year for this service. There were 176 requests for a list. About half of them came, as far as I can tell, from end user organizations and the other half came from consultancies who are helping end user organizations with finding the right tool vendor. Requests came from all continents (except Antarctica) with North America and Europe as the big chunks. There were requests from most industries thus representing a huge span in context.
Also, there where requests from a variety in organization sizes which has given insights beyond what the prominent analyst firms obtain.
It has been a pleasure also to receive feedback from requesters which has helped calibrating the selection model and verifying the insights derived from the context, scope and requirements given.
The variety in context, scope and requirements resulted in having 8 different vendor logos in top-right position and 25 different logos in all included in the 7 to 9 sized best fit extended longlists in the dispatched Your Solution Lists during 2020.
If you are on the look for a solution, you can use the service here.
If you are a vendor in the MDM / PIM / DQM space, you can register your solution here.
A given master data domain as customer, supplier, employee, other/all party, product (beyond PIM), location or asset
A given business unit
You must eat an elephant one bite at a time. Therefore, contextual MDM makes a good concept for getting achievable wins.
However, in an organization with high level of data management maturity the range of contextual MDM use cases, and the solutions for them, will be encompassed by a common enterprise-wide, global, multidomain MDM framework – either as one solution or a well-orchestrated set of solutions.
One example with dependencies is when working with personalization as part of Product Experience Management (PXM). Here you need customer personas. The elephant in the room, so to speak, is that you have to get the actual personas from Customer MDM and/or the Customer Data Platform (CDP).
In having that common MDM solution/framework there are some challenges to be solved in order to cater for all the contextual MDM use cases. One such challenge, being context-aware customer views, was touched upon in the post There is No Single Customer 360 View.