This week I attended an event called Retail Summer School at Columbia Business School in New York.
Much of the talking was about how to get insights on your (prospective) customers by collecting data in all kinds of ways – while observing the thin line between cool and creepy.
My thinking, of course biased by my current Product Data Lake venture, is that you should also pay attention to product data. For at least two reasons:
Algorithm effectiveness: Your algorithms on what products to present based on your rich insight into your customers need will only work if you are able to automatically match the needs against very specific product attributes. And most retailers don not have that today if you look at product descriptions on their ecommerce sites.
Also, I am not impressed by the suggestions I get today. They generally fall into two buckets:
Things I absolutely do not need
Things I just bought
Self-service craving: As a customer, we strike back. We do not need to be told what to buy. But we do want to know what we are buying. This means we want to be able to see rich product information. Therefore retailers must maintain a lot of product data and related digital assets that they should fetch at a trusted source: From the manufactures. And if the manufacturer wants their products to be the ones selected by the end customers, they must be able to deliver these data seamlessly to all their distributors, retailers and marketplaces.
Within the upcoming EU General Data Protection Regulation (GDPR) the term data subject is used for the persons for whom we must protect the privacy.
These are the persons we handle as entities within party Master Data Management (MDM).
In the figure below the blue area covers the entity types and roles that are data subjects in the eyes of GDPR
While GDPR is of very high importance in business-to-consumer (B2C) and government-to-citizen (G2C) activities, GDPR is also of importance for business-to-business (B2B) and government-to-business (G2B) activities.
GDPR does not cover unborn persons which may be a fact of interest in very few industries as for example healthcare. When it comes to minors, there are special considerations within GDPR to be aware of. GDPR does not apply to deceased persons. In some industries like financial services and utility, the handling of the estate after the death of a person is essential, as well as knowing about that sad event is of importance in general as touched in the post External Events, MDM and Data Stewardship.
One tough master data challenge in the light of GDPR will be to know the status of your registered party master data entities. This also means knowing when it is a private individual, a contact at an organization or an organization or department hereof as such. From my data matching days, I know that heaps of databases do not hold that clarity as reported in the post So, how about SOHO homes.
During the last couple years social media have been floating with an image and a silly explanation about how a pack of wolves are organized on the go. Some claims are that the three in the front should be the old and sick who sets the pace so everyone are able to stay in the pack and the leader is the one at the back.
This leadership learning lesson, that I have seen liked and shared by many intelligent people, is made up and does not at all correspond to what scientists know about a pack of wolves.
This is like when you look at master data (wolves) without the right reference data and commonly understood metadata. In order to make your interpretation trustworthy you have to know: ¿Who is the alpha male (if that designation exists), who is the alpha female (if that designation exists) and who is old and sick (and what does that mean)?
PS: For those of you who like me are interested in Tour de France, I think this is like the peloton. In front are the riders breaking the wind (snow), who will eventually fall to the back of the standings, and at the back you see Chris Froome having yet a mechanical problem when the going gets tough and thereby making sure that the entire pack stays together.
With the rise of the Internet of Things (IoT) you may regard the Master Data Management (MDM) discipline as yet a bit more complicated.
The most addressed part of MDM has traditionally been achieving a 360 degree view of customers.
Also, a 360 degree view of products within your organization has been a good old chestnut to deal with. The way we have managed products has mostly been by looking at product models, meaning things made up by the same ingredients in the same way under the same brand.
When entering the IoT era MDM now needs to take care of each physical instance of a product model: Each smartphone, each intelligent refrigerator, each big data producing drilling machine.
In here Prash states: “The IoT plays an ever-increasing role in CX across a variety of industries, and MDM delivers the context it requires to deliver value.”
I agree with that – with an important amendment: In order not to over complicate every-thing, you have to implement a MDM landscape, where you are able to collaborate closely with your business partners as exemplified in the concept of Master Data Share.
When it comes to mastering product data there are these three kinds of data and supporting managing disciplines and solutions:
Master data and the supporting Master Data Management (MDM) discipline and a choice of MDM solutions for the technology part
Product information and the supporting Product Information Management (PIM) discipline and a choice of PIM solutions for the technology part
Digital assets and the supporting Digital Asset Management (DAM) discipline and a choice of DAM solutions for the technology part
What these disciplines are and how the available solutions relate was examined in the post How MDM, PIM and DAM Sticks Together. This post includes a model for that proposed by Simon Walker of Gartner (the analyst firm).
The right mix for your company depends on your business model and you will also have the choice of using a best of breed technology solution for your focus, that being MDM, PIM or DAM, as well as there are choices for a same branded solution, and in some cases also actually integrated solution, that supports MDM, PIM and DAM.
When selecting a (product) data management platform today you also must consider how this platform supports taking part in digital ecosystems, here meaning how you share product data with your trading partners in business ecosystems.
For the digital platform part supporting interacting with master data, product information and digital assets with your trading partners, who might have another focus than you, the solution is Product Data Lake.
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.