As a Master Data Management (MDM) and/or Product Information Management (PIM) platform vendor you should support your current and prospective clients with means to participate in digital ecosystems.
Current offerings from MDM and PIM platforms vendors have become quite mature in supporting inhouse (enterprise wide) handling of master data and product information. Next step is supporting sharing within business ecosystems. A concept for that is introduced in Master Data Share.
“Organisations need architectural thinking beyond their organisational boundaries” and “The days of Enterprise Architecture taking a castle and moat approach are over”.
The end of the castle and moat thinking in Enterprise Architecture (and Business Information Architecture) is also closely related to the diminished importance of the brick and mortar ways of selling, being increasingly overtaken by eCommerce.
However, some figures I have noticed that cause the brick and mortar way to resist the decline by still having a castle and moat thinking is:
Retailers, distributors and manufacturers need to move on from the castle and moat thinking in Enterprise Architecture and Business Information Architecture and start interacting effectively in their business ecosystems with product information.
What is master data and what is Master Data Management (MDM) is a recurring subject on this blog as well as the question about if we need the term master data and the concept of MDM. Recently I read two interesting articles on this subject.
What’s wrong in the MDM angle? Well, it does not make any business process to work and therefore doesn’t create a direct business case. What if we removed the academic borderline between Master Data and other Business Critical data?
The shared sentiment, as I read it, between the two pieces is that you should design your “business information architecture” and the surrounding information governance so that “Data Design Equals Business Design”.
My take is that you should look one level up to get the full picture. That will be considering how your business information architecture fits into the business ecosystem where your enterprise is a part, and thereby have the same master data, shares the same critical data and then operates your own data that links to the shared critical data and business ecosystem wide master data.
Some votes in the current standing has gone to this answer:
There is no viable industry standard for our kind of products
Indeed, having a standard that all your trading partners use too, will be Utopia.
This is however not the situation for most participants in supply chains. There are many standards out there, but each applicable for a certain group of products, geography or purpose as explained in the post Five Product Classification Standards.
At Product Data Lake we embrace all these standards. If you use the same standard in the same version as your trading partner, linking and transformation is easy. If you do not, you can use Product Data Lake to link and transform from your way to the way your trading partners handles product information. Learn more at Product Data Lake Documentation and Data Governance.
Internet of Things (IoT) is a hot topic in the data management world and yours truly is also among those who sees IoT as a theme that will have a tremendous impact on data management including data quality, data governance and Master Data Management (MDM).
However, I think the flavour of IoT called Industrial Internet of Things (IIoT) or Industry 4.0 will mature, and already have matured, before the general IoT theme.
IIoT / Industry 4.0 is about how manufacturers use connected intelligent devices to improve manufacturing processes where the general IoT theme extends the reach out in the consumer world – with all the security and privacy concerns related to that.
In this article, Justin Hester of automotive part manufacturer Hirotec tells about their approach to embracing IIoT. Justin Hester states that “…we can finally harness the data coming in from all of these different sources, whether they are machines, humans, parts – but I think the real challenge is the next step – how do I execute? That’s the challenge.”
Indeed, how to execute and take (near) real-time action on data will be the scenario where Return on Investment (ROI) will show up. This means, as explained in the article, that you should make incremental implementations.
It also means, that you must be able to maintain master data that can support (near) real-time execution. As IIoT/Industry 4.0 is about connected devices in business ecosystems, my suggestion is a data architecture as described on Master Data Share.
In this post Shamanth, exemplified with mascara products, discusses how PIM (Product Information Management) as an enterprise solution helps with effective data management, cutting down new product introduction timelines, multi-channel content management, adhering to regulations and facilitating advanced data analytics.
I agree with all the goodness gained from an enterprise PIM solution for these matters. PIM is the new bacon.
However, in the end Shamanth mentions PIM vendor portals: “The Vendor portal automates the product onboarding process and significantly cuts down operating costs by allowing Vendors to upload complete and curated product data, in bulk, into the system.”
I am sorry to say that I think that using a PIM vendor (or supplier) portal is like lipstick on a pig.
The concept looks tempting by first glance. But it is a flawed concept. The problem is that it is hostile to your trading partners. Your upstream trading partner may have hundreds of downstream trading partners and if every one of these offers their vendor (supplier) portal, they will have to learn and update into hundreds of different portals.
All these portals will have a different look and feel coming from many different PIM solution providers.
The opposite concept, having suppliers providing their customer product data portals, has the same flaw, just the other way around.
The best solution is having a PIM vendor neutral hub sitting in the product information exchange zone. This is the idea behind Product Data Lake.
The use of graph technology in Master Data Management (MDM) has been a recurring topic on this blog as the question about how graph approaches fits with MDM keeps being discussed in the MDM world.
Recently Salah Kamel, the CEO at the agile MDM solution provider Semarchy, wrote a blog post called Does MDM Need Graph?
In here Salah states: “A meaningful graph query language and visualization of graph relationships is an emerging requirement and best practice for empowering business users with MDM; however, this does not require the massive redesign, development, and integration effort associated with moving to a graph database for MDM functionality”.
In his blog post Salah discusses how relationships in the multi-domain MDM world can be handled by graph approaches not necessarily needing a graph database.
At Product Data Lake, which is a business ecosystem wide product information sharing service that works very well besides Semarchy MDM inhouse solutions, we are on the same page.
Currently we are evaluating how graph approaches are best delivered on top of our document database technology (using MongoDB). The current use cases in scope are exploiting related products in business ecosystems and how to find a given product with certain capabilities in a business ecosystem as examined in the post Three Ways of Finding a Product.