Yesterday I popped in at the combined Master Data Management Summit Europe 2016 and Data Governance Conference Europe 2016.
This event takes place Monday to Thursday, but unfortunately I only had time and money for the Tuesday this year. Therefore, my report will only be takeaways from Tuesday’s events. On a side note the difficulties in doing something pan-European must have troubled the organisers of this London event as avoiding the UK May bank holidays has ended in starting on a Monday where most of the rest of Europe had a day off due to being Pentecost Monday.
Tuesday morning’s highlight for me was Henry Peyret of Forrester shocking the audience in his Data Governance keynote by busting the myth about the good old excuse for doing nothing, being the imperative of top-level management support, is not true.
Back in 2013 I wondered if graph databases will become common in MDM. Certainly graph databases has become the talk of the town and it was good to learn from Andreas Weber how the Germany based figurine manufacturer Schleich has made a home grown PIM / Product MDM solution based on graph database technology.
Ivo-Paul Tummers of Jibes presented the MDM (and beyond) roadmap for the Dutch food company Sligro. I liked the alley of embracing multi-channel, then omnichannel with self-service at the end of the road and how connect will overtake collect during this journey. This is exactly the reason of being for the Product Data Lake venture I am working on right now.
I am looking forward to visiting London in a fortnight and have already secured tickets for the new musical called Kinky Boots.
Another option is to pop in at the Master Data Management Summit Europe 2016 and the co-located Data Governance Conference Europe 2016 and visit the kinky booths where the exhibitors will tell you about their latest inventions. Someone to see could be:
Semarchy, who has always been kind of kinky with their evolutionary MDM approach as told in the post Eating the MDM Elephant. Last autumn I visited Semarchy in Lyon and it would be good to catch up with FX, Richard and other good people from this exciting MDM vendor.
Ataccama has a kinky logo. Also on a recent engagement, we have been working with the data quality analyzer tool from Ataccama. So will be good to learn about all the other stuff as for example the big data analyzer.
Stibo Systems, where I worked some years ago, has just released their new version 8.0 of STEP Trailblazer. This version has an enhanced web user interface. While STEP has always had lots of good functionality, I think many STEP users will welcome a more kinky user interface.
Today I attended the Informatica MDM Day for EMEA here in London.
London has a lot of attractions. If you for example want to see a lot of big price tags and go to a public toilet with a very nice odeur the place to go is the famous luxury department store called Harrods.
Harrods, represented by Peter Rush, presented their Product Information Management (PIM) journey at the Informatica event. So, how does a luxury PIM implementation look like?
It starts with realising that traditional product master data in retail has mostly been about the buy-side, but today, not at least in light of the multi-channel challenge, you must add the sell-side to product master data, meaning having customer friendly product information.
After setting that scene Harrods went into selecting a PIM solution, meaning eliminating possible vendors one by one until the lucky one was chosen. In this case Heiler (now Informatica). In the last stages evaluated vendors were sent home based on criteria like roadmap, being in Texas and as the last step the price.
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.
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.
Going to MDM (Master Data Management) conferences is a great learning experience.
If we look at world-wide conferences there are two series of conferences going on every year:
The Master Data Management Summit series lead by the MDM Institute, which is Aaron Zornes
The Master Data Management summit series organized by Gartner (the analyst firm)
Both those traveling events are coming to London this spring. First up is the Gartner event the 12th and 13th March. As I have been to the Zornes show several times before, I am looking forward to be at the more expensive Gartner performance this year.
The learning actually starts when you are looking at company names on the attendee list. Some master data issues are showcased here:
There will be people from these three well-known British supermarkets:
The good folks at Kühne + Nagel (AG & Co.) KG is having a hard time putting their proper name in there:
The typical example of a reference data set is a country table. This is of course a very small data set with around 250 entities. But even that can be complicated as told in the post The Country List.
Reference data can be much bigger. Some flavors of big reference data are:
Third-party data sources
Open government data
Crowd sourced open reference data
Third-party data sources:
The use of third-part data within Master Data Management is discussed in the post Third-Party Data and MDM. These data may also have a more wide use within the enterprise not at least within business intelligence.
Examples of such data sets are business directories, where the Dun & Bradstreet World Base as probably the best known one today counts over 200 million business entities from all over the world. Another example is address and property directories.
Open government data
The above mentioned directories are often built on top of public sector data which are becoming more and more open around the world. So an alternative is digging directly into the government data.
Crowd sourced open reference data
There are plenty of initiatives around where directories similar to the commercial and government directories are collected by crowd-sourcing and shared openly.
In social networks profile data are maintained by the entities in question themselves which is a great advantage in terms of timeliness of data.