A recent post on this blog was called B2C vs B2B in Product Information Management. This post was my take on the differences, if any, between doing Product Information Management (PIM) in a Business-to-Consumer (B2C) scenario versus in a Business-to-Business (B2B) scenario.
“For many of our B2B customers information plays a bigger role in the Market-to-Order process than for consumer products. But most of our customers (Consumer & Professional Packaged Goods Manufacturers) serve both retail and professional/wholesale channels, which have different information needs, even regarding the same products. So, any manufacturer targeted solution should be able to serve both channels with the right content via the right data channels. In our vision a more relevant question is: What is your take on the differences on doing PIM in Manufacturing versus Wholesale / Retail.”
Indeed, there are several ways to slice the PIM space and the supply chain position of a company as a supply chain delegate is for sure very relevant. Exchanging product information between trading partners in upstream and downstream (and midstream) positions must be very flexible and one size fits all thinking will not work.
The different positions of a company as they are in my mind is illustrated below:
The possible combinations when exchanging product information between supply chain delegates are plentiful. To mention a few channels:
Manufacturer to wholesaler to retailer to end private consumer
Manufacturer to distributor to dealer to end business customer
Manufacturer to distributor to dealer to manufacturer as raw material
Manufacturer to merchant to marketplace to end customer
Manufacturer to marketplace to end customer
Manufacturer to/from brand owner to any midstream/downstream delegate
This variety is why the means of exchanging product information (product data syndication) between trading partners is essential in almost any PIM solution.
At Product Data Lake we offer the remedy to this challenge and in combination with any PIM solution or other application where in-house product information is managed.
The Gartner Magic Quadrant for Master Data Management (MDM) Solutions 2018 was published last month.
Some of the numbers in the market that were revealed in the report was the number and distribution of MDM licenses from the included vendors. These covered their top-three master data domains and estimated license counts as well as the number of customers managing multiple domains:
One should of course be aware of the data quality issues related to comparing these numbers, as they in some degree are estimates based on different perceptions at the included vendors. So, let me just highlight these observations:
The overall number of MDM licenses and unique MDM customers (at the included vendors) is not high. Under 10,000 organizations world-wide is running such a solution. The potential new market out there for the salesforce at the MDM vendors is huge.
If you find an existing MDM solution user organization, they probably have a solution from SAP or Informatica – or maybe IBM. To be complete, Oracle has been dropped from the MDM quadrant, they practically do not promote their MDM solutions anymore, but there are still existing solutions operating out there.
The reign of Customer MDM is over. Product MDM is selling and multidomain is becoming the norm. Several MDM vendors are making their way into the quadrant from a Product Information Management (PIM) base as reported in the post The Road from PIM to Multidomain MDM.
PS: If you, as an end customer organization or a MDM and PIM vendor, want to work with me on the consequences for MDM solutions, here are some Popular Offerings for you.
The title of this blog post is also the title of a presentation I will do at the 2019 Data Governance and Information Quality Conference in San Diego, US in June.
There is a little difference between how we can exercise data governance and information quality management when we are handling data about products versus handling the most common data domain being party data (customer, vendor/supplier, employee and other roles).
The title of this blog post is also the title of a webinar I will be presenting on the 28th February 2019. The webinar is hosted by the visionary Multidomain MDM and PIM solution provider Riversand.
Customer experience (CX) and Master Data Management (MDM) must go hand in hand. Both themes involve multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. Master data is the glue that brings the data you hold about your customers together as well as the glue that combines the data you share about your product offering together.
To be successful within customer experience in the digital era you need classic master data outcomes as a 360-degree view of customers as well as complete and consistent product information. In other words, you need to maintain Golden Records in Multidomain MDM.
You also need to combine your customer data and your product data to get to the right level of personalization. Knowing about your customer, what he/she wants, and their buying behaviour is one side personalization. The other side is being able to match these data with relevant products that is described to a level that can provide reasonable logic against the behavioural data.
Furthermore, you need to be able to make sense of internal and external big data sources and relate those to your prospective and existing customers and the products they have an interest in. This quest stretches the boundaries of traditional MDM towards being a more generic data platform.
When working with data management – and not at least listening to and reading stuff about data management – there is in my experience too little work with the actual data going around out there.
I know this from my own work. Most often presentations, studies and other decision support in the data management realm is based on random anecdotes about the data rather than looking at the data. And don’t get me wrong. I know that data must be seen as information in context, that the processes around data is crucial, that the people working with data is key to achieving better data quality and much more cleverness not about the data as is.
But time and again I always realize that you get the best understanding about the data when getting your hands dirty with working with the data from various organizations. For me that have been when doing a deduplication of party master data, when calibrating a data matching engine for party master data against third party reference data, when grouping and linking product information held by trading partners, when relating other master data to location reference data and all these activities we do in order to raise data quality and get a grip on Master Data Management (MDM) and Product Information Management (PIM).
Well, perhaps it is just me and because I never liked real dirt and gardening.
The difference between doing Business-to-Consumer (B2C) or Business-to-Business (B2B) reflects itself in many IT enabled disciplines.
When it comes to Product Information Management (PIM) this is true as well. As PIM has become essential with the rise of eCommerce, some of the differences are inherited from the eCommerce discipline. There is a discussion on this in a post on the Shopify blog by Ross Simmonds. The post is called B2B vs B2C Ecommerce: What’s The Difference?
Some significant observations to go into the PIM realm is that for B2B, compared to B2C:
The audience is (on average) narrower
The price is (on average) higher
The decision process is (on average) more thoughtful
To sum up the differences I would say that some of the technology you need, for example PIM solutions, is basically the same but the data to go into these solutions must be more elaborate and stringent for B2B. This means that for B2B, compared to B2C, you (on average) need:
More complete and more consistent attributes (specifications, features, properties) for each product and these should be more tailored to each product group.
More complete and consistent product relations (accessories, replacements, spare parts) for each product.
More complete and consistent digital assets (images, line drawings, certificates) for each product.
Even though that Master Data Management (MDM) has been around as a discipline for about 15 years now, there is still a lot of road to be covered for many organizations and for the discipline as a whole.
Some of the topics I find to be the most promising visit points on this journey are cloud deployment of MDM solutions, inclusion of Artificial Intelligence (AI) in MDM and multienterprise MDM.
Cloud deployment of MDM has increased slowly but steadily over the recent years. Quite naturally the implementation of MDM in the cloud will follow the general adoption of cloud solutions deployed in each organization as master data is the glue between the data held in each application. Doing MDM in the cloud or not is, as with most things in life, not a simple question with a yes or no answer, as there are different deployment styles as examined in the post MDM, Cloud, SaaS, PaaS, IaaS and DaaS.
Inclusion of Artificial Intelligence (AI) and Machine Learning (ML) in the MDM discipline will, in my eyes, be one of the hottest topics in the years to come. MDM is not the easiest IT enabled discipline in which AI and ML can ne applied. Handling master data has many manual processes today because it is highly interactive, and the needed day-to-day decisions requires much knowledge input. But we will get there step by step and we must start now as told in the post It is time to apply AI to MDM and PIM.
Multienterprise MDM is emerging as a necessity following the rise of digitalization. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners will be a part of digitalization and thus, we will have a need for working on the same foundation around master data. This theme was pondered in the post Share or be left out of business.
Ultima Thule is a name for a distant place beyond the known world and the nickname of the most distant object in the solar system closely observed by a man-made object today the 1st January 2019. Before the flyby scientists were unsure if it was two objects, a peanut formed object or another shape. The images probing what it is will be downloaded during the next couple of months.
A while ago the trend of having the possibility to deploy a Master Data Management (MDM) solution in the cloud was covered in the post The Rise of Cloud MDM.
The latest Gartner MDM Magic Quadrant report has some numbers on that trend as mentioned in the post Who Will Make the Next Disruption on the MDM Market? Cloud based deployment has increased from 19% in 2017 year to 24 % in 2018 among Gartner’s respondents. While the organizations included here are the larger ones, I will guestimate that the cloud portion of MDM implementations are higher among midsize and smaller organizations.
As mentioned in the Gartner report there are however some confusion about what a cloud MDM solution really is. Does it come as SaaS (Software as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service)? In this spectrum the vendor will provide most things in a SaaS solution, lesser stuff as PaaS and only the ability for the software to be hosted somewhere out there as IaaS.
One “as a Service” component in relation to master data you could expect in SaaS, but not necessarily in IaaS, is DaaS (Data as a Service) as for example out-of-the-box address verification and business directory integration services. A common address verification service is the one from Loqate, while Informatica though have their own solution based on their AddressDoctor acquisition. The most common business directory provider is Dun & Bradstreet.
Else the difference follows the general difference between SaaS, PaaS and IaaS which is about what the organization has do themselves (or through system integrators) around software updates, configuration, maintenance, monitoring and more.
On the brink to 2019 my guess is that we will see more MDM in the cloud next year as well as a movement from IaaS over PaaS to SaaS. This will include more DaaS covering more master data domains not at least in the product data space – a reason of being for the Product Data Lake service I am involved with.
There are many market reports covering the Master Data Management (MDM) and Product Information Management (PIM) market. Below you can find 4 of these coming from who is usually considered as the more reliable analyst houses around: