Latest in connection with that TIBCO acquires data matching vendor Netrics the term best-in-class match engine has been attached to the Netrics product.
First: I have no doubt that the Netrics product is a capable match engine – I know that from discussions in the LinkedIn Data Matching group and here on this blog.
Next: I don’t think anyone knows what product is the best match engine, because I don’t think that all match engines have been benchmarked with a representative set of data.
There are of course on top the matching capabilities with different entity types to consider. Here party master data (like customer data) are covered by most products whereas capabilities with other entity types (be that considered same same or not) are far less exposed.
As match engine products are acquired and integrated in suites the core matching capabilities somehow becomes mixed up with a lot of other capabilities making it hard to compare the match engine alone.
Some independent match engines work stand alone and some may be embedded into other applications.
These may then be the classes to be best in:
- Match engines in suites
- Embedded match engines (for say SAP, MS CRM and so on)
- Stand alone match engines
Many match engines I have seen are tuned to deal with data from the country (culture) where they are born and had their first triumphs. As the US market is still far the largest for match engines the nomination of best match engine resembles when a team becomes World Champions in American Football. International/multi-cultural capabilities will become more and more important in data matching. But indeed we may define a class for each country (culture).
In the old days I have heard that one match engine was best for marketing data and another match engine was best for credit risk management. I think these days are over too. With Master Data Management you have to embrace all data purposes.
Some match engines are more successful in one industry. The biggest differentiator in match effectiveness is with B2C and/or B2B data. B2C is the easiest, B2B is more complex and embracing both is in my eyes a must for being considered best-in-class – unless we define separate classes for B2C, B2B and both.
As some matching techniques are deterministic and some are probabilistic the evaluation on the latter one will be based on data already processed in a given instance, as the matching gets better and better as the self learning element is warmed up.
So, yes, an endless religious-like discussion I reopened here.
Excellent post Henrik,
I have always found the “best-in-class” distinction to be quite silly (and not just for match engines).
I imagine the industry as a large university building with hundreds of classrooms. Every vendor is truly “best-in-class” — because they are sitting alone in their own classroom. 🙂
However, Americans (more specifically, Americans from the United States) are, in fact, World Champions in any sport where the championship only includes teams from our country. 🙂
If we look at who is left as independent data matching specialists I found these employee numbers on LinkedIn:
Uniserv GmbH, Stuttgart Area, Germany = 110
Melissa Data, Greater Los Angeles Area (US) = 105
Human Inference, Nijmegen Area, Netherlands = 100
Omikron Data Quality, Stuttgart Area, Germany = 65
Datanomic, Cambridge, United Kingdom = 58
Ataccama, Toronto, Canada Area = 40
S3 Matching Technologies, Austin, Texas (US) = 33
Datactics, United Kingdom = 11-50
Syslore Oy, Finland = 30
Infoglide Software, Austin, Texas (US) = 30
DataMentors, Inc., Tampa/St. Petersburg, Florida Area (US) = 25
Inquera, Israel = 25
Probably there are a lot more out there, not at least those less exposed in English language but who is used to match all the data in China, Japan, Russia, France, Italy and so on.
Interesting list, Henrik.
With the recent trend of vendor consolidation, I can’t help but wonder if one or more of the remaining independent data matching specialists will be acquired in the coming months.
For example, you have to figure that Oracle will buy at least one of them, especially one of the US based companies.
Jim, that is surely what is heard in the grapevine. Or maybe they will reinvent the wheel.
Thanks Henrik for sending me the list you had. I created a little speculative Who’s Who of Who’s Left in a new blog post. You can vote for who you think will be acquired and who will be the acquirer.
Interesting speculations. My purpose with the list was though only to state, that though the M&A speculations and reality may be like an American Football World Series, there is a lot of Soccer going on in the rest of world.
What then may be an interesting question is, if it matters that the big suites is only equipped with US/English data matching capabilities?
I remember that related to ERP, what happened for MicroSoft was that first they bought Great Plains (now MS Dynamics GP) but later they had to buy Copenhagen based Navision (now MS Dynamics NAV and MS Dynamics AX).
I agree that the “best-in-class” concept must be used within a particular scope (one cannot compare apples and pears 🙂 ).
If we narrow down to the following criteria:
– solution affordable for small company
– fast matching of 10-20 criteria in a database containing less than a million rows
– scalable solution
1. What solution would you suggest?
2. What would be the hardware/software requirements (licenses, web servers, …)?
Indeed I have little knowledge of this area but would like to build a web platform with a matching engine to help developing humanitarian projects in France.
Any piece of advice would be greatly appreciated (people to contact, articles, books, benchmarks, …)
Thanks for the comment Richard.
About affordable match engines available a discussion on DataQualityPro comes to my mind. Link here.
A lot of other resources and links are available on the DataQualityPro site.
Thank you very much Henrik for this resource.