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.
While the European data management community is fully occupied with the General Data Protection Regulation (GDPR) the United States president realDonaldTrump and his family is preparing something bigger being the Great Data Protection Wall (GDPW).
An upcoming executive order will enforce a tall and beautiful wall around each data centre. It’s true.
Hereafter, the only way to bring data pass such a wall will be by accessing data on a microwave oven (aka wiretapping).
Some other core concepts will be rules for handling alternative facts as well as how to apply the term fake news to anything, which does not fit into your scheme.
A White House spokesperson is spicing it up like this: “We will repeal and replace the current way of taking care of data with something else”.
China and Russia is expected to come up with similar GDPx initiatives. As China already has a Great Wall, their implementation will be known as GDPH (General Data Piling via Huawai devices) and Russia is routinely already involved in the US implementation.
The digital age has a lot of consequences in our life and the next big reform is the end of the time zones.
As most shops nowadays are web shops being open 24/7 and many people work around the clock from home, travel and anywhere else, we really don’t need time zones around the world anymore.
Therefore, the United Nations have decided that everyone will be on UTC from 1st January 2020.
There will only be a few exceptions:
- The US Midwest will g.. d.. it stay one their usual time zone.
- Switzerland will have their separate time zone, the so called cuckoo clock time.
- The UK prime minister has decided that there first will be a referendum about this in the UK if he wins the next three general elections.
Within data management we already have “The MDM Institute”, “The Data Governance Institute” and “The Data Warehouse Institute (TDWI)” and now we also have “The Data Matching Institute”.
The founder of The Matching Institute is Alexandra Duplicado. Aleksandra says: “The reason I founded The Institute of Data Matching is that I am sick and tired of receiving duplicate letters with different spellings of my name and address”. Alex is also pleased about, that she now have found a nice office in edit distance of her home.
Before founding The Matching of Data Institute Alexander worked at the Universal Postal Union with responsibility for extra-terrestrial partners. When talking about the future of The Match Institute Sasha remarks: “It is a matter of not being too false positive. But it is a unique concept”.
One of the first activities for The Data-Matching Institute will be organizing a conference in Brussels. Many tool vendors such as Statistical Analysis System Inc., Dataflux and SAS Instiute will sponsor the Brüssel conference. I hope to join many record linkage friends in Bruxelles says Alexandre.
The Institute of Matching of Data also plans to offer a yearly report on the capabilities of the tool vendors. Asked about when that is going to happen Aleksander says: “Without being too deterministic a probabilistic release date is the next 1st of April”.
11th of November and it’s time for the first x-mas post on this blog this year. My London gym is to blame for this early start.
Santa’s residence is disputed. As told in the post Multi-Domain MDM, Santa Style one option is Lapland.
Yesterday this yuletide challenge was included in an eMail in my inbox:
Nice. Lapland is in Northern Scandinavia. Scandinavia belongs to that half of the world where comma is used as decimal mark as shown in the post Your Point, My Comma.
So while the UK born gym members will be near fainting doing several thousands of kilometers, I will claim the prize after easy 3 kilometers and 546 meters on the cross trainer.
How do you select a Master Data Management (MDM) vendor? There is of course the RFP way of scoring vendors against a bunch of carefully specified requirements within data model, user interface, architecture and so on. But as I have seen it, maybe the multi-domain way is much more used.
The multi-domain MDM vendor selection process has three basic parameters:
- Distance between locations
- Chemistry between parties
- Price of products
Distance between locations:
Here you measure four numbers:
- N1 = Northern UTM geocode of buyers head quarter
- E1 = Eastern UTM geocode of buyers head quarter
- N2 = Northern UTM geocode of vendors head quarter or major regional office
- E2 = Eastern UTM geocode of vendors head quarter or major regional office
Then using the Pythagorean Theorem you get:
(You could make up the distance on Google Maps as well, but that doesn’t look very scientific).
Chemistry between parties:
Here you, at the meetings between the buying team and the vendor team, measure the occurrence of these sentences:
- Could you repeat that question please?
- Could you repeat that answer please?
(Observe that there may be a correlation with distance in cases where distance calls for the use of a webex for a meeting).
Price of products:
I guess everyone knows how to sum up euros/dollars/pounds/whatever.
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.