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.
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.
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.
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.
Recently the folks at Winpure have embarked on a journey to take best-of-breed data matching into the contextual MDM world.
Data matching is often part of Master Data Management implementations, not at least when the party domain (customers, suppliers, other business partners) is encompassed.
However, it not always the best approach to utilize the data matching capabilities in MDM platforms. In some cases, these are not very effective. In other cases, the matching is needed before data is loaded into the MDM platform. And then many MDM initiatives do not include an MDM platform, but relies on capabilities in ERP and CRM applications.
Here, there is a need for a contextual MDM component with strong data matching capabilities as Winpure.
Interenterprise Master Data Management is on the rise as reported in the post Watch Out for Interenterprise MDM. Interenterprise MDM 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.
One of the most obvious places to start with Interenterprise MDM is Product Data Syndication (PDS). While PDS until now has been mostly applied when syndicating product data to marketplaces there is a huge potential in streamlining the flow of product from manufacturers to merchants and end users of product information.
Inbound and Outbound Product Data Syndication
There are two scenarios in interenterprise Product Data Syndication:
Inbound, where your organization as being part of a supply chain will receive product information from your range of suppliers. The challenge is that with no PDS functionality in between you must cater for many (hundreds or thousands) different structures, formats, taxonomies and exchange methods coming in.
Outbound, where your organization as being part of a supply chain will provide product information to your range of customers. The challenge is that with no PDS functionality in between you must cater for many (hundreds or thousands) different structures, formats, taxonomies and exchange methods requested by your customers.
There are these four main use cases for exchanging product data in supply chains:
Exchanging product data for resell products where manufacturers and brands are forwarding product information to the end point-of-sale at a merchant. With the rise of online sales both in business-to-consumer (B2C) and business-to-business (B2B) the buying decisions are self-service based, which means a dramatic increase in the demand for product data throughput.
Exchanging product data for raw materials and packaging. Here there is a rising demand for automating the quality assurance process, blending processes in organic production and controlling the sustainability related data by data lineage capabilities.
Exchanging product data for parts used in MRO (Maintenance, Operation and Repair). As these parts are becoming components of the Industry 4.0 / Industrial Internet of Things (IIoT) wave, there will be a drastic demand for providing rich product information when delivering these parts.
Exchanging product data for indirect products, where upcoming use of Artificial Intelligence (AI) in all procurement activities also will lead to requirements for availability of product information in this use case.
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 PDS and enable the four mentioned use cases for product data exchange.
The framework is based on reusing popular product data classifications (as GPC, UNSPSC, ETIM, eClass, ISO) and attribute requirement standards (as ETIM and eClass). Also, trading partners can use their preferred data exchange method (FTP file drop – as for example BMEcat, API or plain import/export) on each side.
All in all, the big win is that each upstream provider (typically a manufacturer / brand) can upload one uniform product catalogue to the Product Data Lake and each downstream receiver (a merchant or user organization) can download a uniform product catalogues covering all suppliers.
This is the fourth and final blog post on the main take away from the fresh published Gartner Magic Quadrant for Master Data Management Solutions 2021.
The first post here touched on the quadrant advancements being the vendors that have moved between the 4 quadrants.
Unfortunately, Gartner has not, as in previous years, stated the revenue for all the vendors, so that you can determine the growth directly. Gartner though mentions, that Semarchy, Reltio and Ataccama had 2-digit revenue growth and that IBM had shrinking MDM revenue – again. We may then assume that the other recurring vendors had 1-digit revenue growth. However, it is mentioned that Riversand had a 10m USD revenue growth, which could indicate a 2-digit revenue growth for them too.
Combining quadrant advancements and revenue growth statements results in this movement overview:
Interenterprise data sharing must be leveraged through interenterprise MDM, where master data are shared between many companies as for example in supply chains. The evolution of interenterprise MDM and the current state of the discipline was touched in the post MDM Terms In and Out of The Gartner 2020 Hype Cycle.
In the 00’s the evolution of Master Data Management (MDM) started with single domain / departmental solutions dominated by Customer Data Integration (CDI) and Product Information Management (PIM) implementations. These solutions were in best cases underpinned by third party data sources as business directories as for example the Dun & Bradstreet (D&B) world base and second party product information sources as for example the GS1 Global Data Syndication Network (GDSN).
In the previous decade multidomain MDM with enterprise-wide coverage became the norm. Here the solution typically encompasses customer-, vendor/supplier-, product- and asset master data. Increasingly GDSN is supplemented by other forms of Product Data Syndication (PDS). Third party and second party sources are delivered in the form of Data as a Service that comes with each MDM solution.
In this decade we will see the rise of interenterprise MDM where the solutions to some extend become business ecosystem wide, meaning that you will increasingly share master data and possibly the MDM solutions with your business partners – or else you will fade in the wake of the overwhelming data load you will have to handle yourself.
So, watch out for not applying interenterprise MDM.
PS: That goes for MDM end user organizations and MDM platform vendors as well.