One of the top challenges in product Master Data Management (MDM) is the sharing of master data attributes and digital assets across the ecosystem of manufacturers, distributors, retailers and end users.
There seems to be a range of solutions emerging in order to cover that land. Three kinds of approaches will be:
- Supplier engagement within Product Information Management (PIM) solutions.
- Similar solutions within wider IT offerings.
- Social PIM.
Master Data Management (MDM) platforms with strong offerings for the product domain comes with built-in functionality for engaging suppliers in the process of collecting product master data attributes and related materials as product sheets, images and other digital assets.
You may also find similar functionality within the broader software suites as for example the SAP Product Stewardship Network.
A somewhat different approach may be called Social PIM as explained in the post Time to Turn Your Product Master Data Social? Here the collection process is sort of independent of in-house systems. This may, in the long run, help with having your suppliers having to attend many different solutions and also help your customers depending on where you sit in the ecosystem.
What is your experience regarding sharing product master data?
When we talk about multi-domain Master Data Management (MDM) we often focus on the two dominant MDM domains being customer (or rather party) MDM and product (or maybe things) MDM.
The location domain is the third bigger domain within MDM. Location management can be more or less complex depending on the industry vertical we are looking at. In the utility and telco sectors location management is a big thing. Handling installations, assets and networks is typically supported by a Geographical Information System (GIS).
Master Data Management is much about supporting that different applications can have a unified view of the same core business entities. Therefore, in the utility and telco sectors a challenge is to bring the GIS application portfolio into the beat with other applications that also uses locations as explained in the post Sharing Big Location Reference Data.
The last couple of days I enjoyed taking part in the Nordic user conference for a leading GIS solution in the utility and telco sector. This solution is called Smallword.
It is good to see that at least one forward looking organization in the utility and telco sector is working with how location master data management can be shared between business functions and applications and aligned with party master data management and product master data management.
Last week I had some fun making a blog post called The True Leader in Product MDM. This post was about how product Master Data Management still in most places is executed by having heaps of MS Excel spreadsheets flowing around within the enterprise and between business partners, as I have seen it.
When it comes to customer Master Data Management MS Excel may not be so dominant. Instead we have MS CRM and the competing offerings as Salesforce.com and a lot of other similar Customer Relationship Management solutions.
CRM systems are said to deliver a Single Customer View. Usually they don’t. One of the reasons is explained in the post Leads, Accounts, Contacts and Data Quality. The way CRM systems are built, used and integrated is a certain track to create duplicates.
Some remedies out there includes periodic duplicate checks within CRM databases or creating a federated Customer Master Data Hub with entities coming from CRM systems and other databases with customer master data. This is good, but not good enough as told in the post The Good, Better and Best Way of Avoiding Duplicates.
During the last couple of years I have been working with the instant Data Quality service. This MDM service sits within or besides CRM systems and/or Master Data Hubs in order to achieve the only sustainable way of having a Single Customer View, which is an instant Single Customer View.
This weekend I’m in Copenhagen where I, opposite to when in London, enjoy a bicycle ride.
In the old days I had a small cycle computer that gave you a few key performance indicators about your ride as time of riding, distance covered, average and maximum speed. Today you can use an app on your smartphone and along the way have current figures displayed on your smartwatch.
As explained in the post American Exceptionalism in Data Management the first thing I do when installing an app is to change Fahrenheit to Celsius, date format to an useable one and in this context not at least miles to kilometers.
The cool thing is that the user interface on my smartwatch reports my usual speed in kilometer per hour as miles per hour making me 60 % faster than I used to be. So next year I will join Tour de France making Jens Voigt (aka Der Alte) look like a youngster.
A Viking tour around Roskilde and Vallø Borgring. Click for report with a wonderful mixup of date formats.
Using such an app is also a good example of why we have big data today. The app tracks a lot of data as detailed route on map with x, y and z coordinates, split speed per kilometer and other useful stuff. Analyzing these data tells me Tour de France maybe isn’t a good idea. After what I thought was 100 miles, but was 100 kilometers, my speed went from slow to grandpa.
That’s a bit like IT projects by the way. Regardless of timeframe, they slows down in progress after 80 % of plan has been covered.
Magic Quadrants from Gartner are the leading analyst report sources within many IT enabled disciplines. This is also true in the data management realm and one of quadrants here is the Gartner Magic Quadrant for Master Data Management of Product Data Solutions.
The latest version of this quadrant was out in November last year as reported in the post MDM for Product Data Quadrant: No challengers. A half visionary.
Most quotations after a quadrant release are vendors bragging about their position in the quadrant and this habit will possibly also repeat itself when the next quadrant for product MDM is out.
But I think Gartner has got it all wrong here during all the years. As I have seen it, Microsoft is the true leader and the rest of the flock are minor niche players.
The term American exceptionalism is born in the political realm but certainly also applies to other areas including data management.
As a lot of software and today cloud services are made in the USA, the rest of world has some struggle with data standards that only or in high degree applies to the United States.
Some of the common ones are:
In the United States Fahrenheit is the unit of temperature. The rest of the world (with a few exceptions) use Celsius. Fortunately many applications has the ability of switching between those two, but it certainly happens to me once in a while that I uninstall a new exciting app because it only shows temperature in Fahrenheit, and to me 30 degrees is very hot weather.
The Month-Day-Year date format is another American exceptionalism in data management. When dates are kept in databases there is no problem, as databases internally use a counter for a date. But as soon as the date slips into a text format and are used in an international sense, no one can tell if 10/9/2014 is the 10th September as it is seen outside the United States or 9th October as it is seen inside the United States. For example it took LinkedIn years before the service handled the date format accordingly to their international spread, at there are still mix-ups.
Having a state as part of a postal address is mandatory in the United States and only shared with a few other countries as Australia and Canada, though the Canadians calls the similar concept a province. The use of a mandatory state field with only US states present is especially funny when registering online for a webinar about an international data quality solution.
In order to have all my travel arrangements in one place I use a service called TripIt. When I receive eMail confirmations from airlines, hotels, train planners and so, I simply forward those to firstname.lastname@example.org, and within seconds they build or amend to an itinerary for me that is available in an app.
Today I noticed a slight flaw though. I was going by train from London, UK up to the Midlands via a large town in the UK called Reading.
The strange thing in the itinerary was that the interchanges in Reading was placed in chronology after arriving at and leaving the final destination.
A closer look at the data revealed two strange issues:
- Reading was spelled Reading, PA
- The time zone for the interchange was set to EST
Hmmm… There must be a town called Reading in Pennsylvania across the pond. Tripit must, when automatically reading the eMail, have chosen the US Reading for this ambiguous town name and thereby attached the Eastern American time zone to the interchange.
Picking the right Reading for me in the plan made the itinerary look much more sensible.