The methodology and lingo differ a bit, however the ranking is, as with all these kinds of analyst rankings, based on that the vendors are assessed more positive the closer they are to the top right corner.
The two analyst firms are in more or less agreement about some vendors while some vendors are assessed quite different. These are in particular:
Informatica, who is assessed much more negative by Forrester than by Ventana. It is a part of the story that Informatica for a long time has declined to participate in Forrester’s PIM assessments.
Akeneo, who is a new vendor among the major players, and has a better debut at Ventana than at Forrester.
Stibo Systems, who has been a leader at Forrester for some years but has moved down to a modest position at Ventana in the latest ranking.
Looking at assessing the vendors against the others is close to me as part of the Select Your Solution service on The Disruptive MDM / PIM / DQM List. Here the assessment is based on the actual context, scope and requirements for you as a potential buyer (or someone who is helping a potential buyer). When doing that it is natural that a given vendor can be closest to the top right corner in some cases and not in other cases.
That analysts in a generic ranking reaches a different result only underpins that solution selection is not easy and requires a substantial knowledge about the available solutions, where they come from and where they are heading.
If you need help navigating in this jungle, ping me here:
The Forrester Wave™ Product Information Management Q2 2021 is out.
In here, Forrester has identified the in their eyes 10 most significant solutions — Akeneo, Contentserv, IBM, Informatica, inRiver, Riversand, Salsify, Stibo Systems, Syndigo, and Winshuttle — and researched, analyzed, and scored them.
Stibo Systems is still close to the right top corner and thereby cementing their role as a leader in PIM.
Informatica still has a dysfunctional relationship with Forrester and has not participated in this report either. This has not helped with their positioning in the ranking.
IBM is still in the lower rankings.
Changed
Salsify has moved up and grown.
Riversand has moved up and grown a bit – and has been accompanied by Syndigo who by the way just bought them today.
Enterworks, now as part of Winshuttle, has moved down – but grown.
Contentserv has moved down and shrunk. So has inRiver.
Akeneo has entered the PIM wave.
SAP and Agility Multichannel (now part of Magnitude) has been dropped from this report.
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.
Vendor capabilities.
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.
The vendors included are both the major players on the market as well as emerging solutions with innovative offerings.
When working with Product Information Management (PIM) I usually divide the different kinds of information to be managed into some levels and groups as elaborated in the post 5 Product Data Levels to Consider.
The 10 groups of data in this 5-level scheme are all relevant for personalization of product data in the following way:
A (prospective) customer may have some preferred brands which are recognized either by collection of preferences or identified through previous behaviour.
The shopping context may dictate that some product codes like GTIN/UPC/EAN and industry specific product codes are relevant as part of the product presentation or if these codes will only be noise.
The shopping context may guide the use of variant product descriptions as touched in the post What’s in a Product Name?
The shopping context may guide the use of various product image styles.
The shopping context may guide the range of product features (attributes) to be presented typically either on a primary product presentation screen and on a detailed specification screen.
The shopping context and occasion may decide the additional product description assets (as certificates, line drawings, installation guides and more) to be presented.
The shopping occasion may decide the product story to be told.
The shopping occasion may decide the supplementary products as accessories and spare parts to be presented along with the product in focus.
The shopping occasion may decide the complementary products as x-sell and up-sell candidates to be presented along with the product in focus.
The shopping occasion may decide the advanced digital assets as brochures and videos to be presented.
The data collection track that can enable customization and personalization of product information is examined in the post The Roles of MDM in The Data Supply Chain.
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?
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.
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.
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.
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:
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.
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.
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, Repair and Operation). 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.
When working with Product Information Management (PIM) and Product Master Data Management (Product MDM) one of the most important and challenging areas is how you effectively onboard product master data / product information for products that you do not produce inhouse.
There are 4 main scenarios for that:
Onboarding product data for resell products
Onboarding product data for raw materials and packaging
Onboarding product data for parts used in MRO (Maintenance, Repair and Operation)
Onboarding product data for indirect products
Onboarding product data for resell products
This scenario is the main scenario for distributors/wholesalers, retailers and other merchants. However, most manufactures also 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 a buying decision by the end customer. With the increase of online sales, the buying decision today is often self-serviced. This has dramatically increased the demand for product information throughput.
Onboarding product data for raw materials and packaging
This scenario exists at manufacturers of products. 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.
Onboarding product data for parts used in MRO
Product data for parts used in Maintenance, Repair and Operation is a main scenario at manufacturers related to running the production facilities. However, most organizations have 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.
Onboarding product data for 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 onboarding calls for increased collaboration between suppliers and Business-to-Business (B2B) customers. It is here worth noticing, that many organizations have both roles in one or the other scenario. The discipline that is most effectively applied to solve the challenges is Product Data Syndication. This is further explained in the post Inbound and Outbound Product Data Syndication.
Liliendahl on Data Quality 