Interview: Roger Haddad, Founder of KXEN Automated Modeling Software

KXEN_logo_300dpi I first talked about KXEN,the automated modeling software  in this post

So I asked Roger Haddad ,its founder and CEO if he could give an email interview and Roger being the great guy he is , both remembered me from user analyst days as well worked in the holidays to give this interview. Before founding KXEN, Mr. Roger Haddad was president of Azlan France, the first network distributor in France. Under his management, sales revenues increased 850 percent in four years.In 1977, Mr. Haddad founded Metrologie International, a leading software and hardware provider, which went public on the Paris Stock Exchange in 1985. When Mr. Haddad left in 1991, Metrologie had 4,500 employees, $900M in revenue and subsidiaries in 13 European countries.Mr. Haddad holds a master’s degree in electrical metrology from George Washington University and a bachelor’s degree in electrical engineering from Ecole Supérieure d’Electricité, Paris, France.


Ajay : What would your advice be to young professionals entering the job world today ?

Roger : If you are talking about Statisticians, I would tell them to concentrate on the data and the process rather than on the statistical orthodoxy

Ajay : What interested you most in being the head of KXEN. What is the best feature you like in KXEN. – both as a company and as a product.

Roger :To Make it happen !! Data mining is at its infancy, because SAS and others made it difficult to work with !! they made for an elite of people !!

KXEN role is to open this bottleneck and give power to the users – Analysts will help to train business users and get them confident with their findings.

As a product, I am always suprised by the quality of KXEN results in a fraction of the time compared to first generation workbench and automatically !! 🙂

Ajay : What areas has KXEN been most suitable for ? Biggest sucess story so far.

Roger : Classification, regression with thousands of variables and tricky data sets !! We have hundreds of success stories 

Ajay :Could you also comment on how the slowdown and recession would affect the analytics world in terms of newer solutions , Software as a service , more acceptance of trying out the unfamiliar etc ?

Roger : I believe the recession and the slowdown will push analytics further and particularly KXEN approach , since we allow corporations to do much more with less or with the same Team. We are seeing many Analytics Group being reduced and people calling on us to deliver what need to be delivered!!

Ajay : What areas would you rather not recommend KXEN? What other softwares would you recommend in those cases ?

Roger : I would not recommend KXEN in genetics – SVM would be more apropriate

Ajay : Asia has a nascent but high potential market. What are you Asian plans and any clients /case studies here ?

Roger:  We have a presence in every countries but in India – Japan is by far our best country and we have there a fantastic Distributor -WE also have Customers in China , in Asean too – We are looking for a good Distributor in India , but this seems quite difficult.(Note from Ajay – I decided to apply straight away)

Ajay : What is the biggest challenge you have faced while introducing KXEN to a wider audience.
Roger: THe resistance to Change and the fear of classical statisticians that they will loose their job !! in fact this never happened and on  the contrary they become hero in their Corporation after adopting KXEN

  Roger Haddad 1Founder and Chief Executive Officer

Mr. Haddad is responsible for overseeing the KXEN sales team, the distribution channel management, as well as the direction of the company and the strategic growth of the organization. With more than 30 years experience as an industry expert, Mr. Haddad is a forward-looking entrepreneur with an expertise in successfully running companies with multiple channels
of distribution. Mr. Haddad has a long and successful track record in developing new companies into profitable enterprises.

All the Inventors and Pirates

A terrific site for filing patents is

It has great features and a great database as the US Patent office, and it makes sense for an average Joe without needing a patent attorney.


A terrific site for copyright infringement is

Apparently it is legal as per Swedish law, and thus makes it easier for people to download songs ,videos and movies and softwares for free. One NY student even scanned his whole chemistry book and offered it for download as a protest against high prices, which seems a bit extreme in any case.

Swedes have been trying to shut down the site for 5 years now!

Their blog is quite interesting here

And they say piracy is a problem in Asia

Poetry for Free

Dear Reader,


Here are my E Books for free. I belive some things ( if not the best) things should be free

You can preview them and order the hard copies from the right sidebar.


In Case I Dont See You Again

Corporate Poetry

OT-New Year Gift-Poetry for Free

Dear Reader,


Here are my Poetry E –Books for download for free. I believe some things ( if not the best) things should be free

You can preview them and order the hard copies from the right sidebar.


In Case I Dont See You Again

Corporate Poetry

Smart Data Collective

Here is a great online community for decision scientists especially the ones reading these posts. It is called Smart Data Collective and it is hosted at

It works like a blog aggregator for specific topics and the quality of posts is quite nice. Just like your blogroll does for you and it can also help your blog, if you write to get some added views. Basically it works as a newpaper for data topics from featured posts by data bloggers. I joined it just over the year end , and  was able to both write and read on nice topics.


Some Self Promotion by humbly yours

And also, if you didnt notice , we had a revamped website at

Improvements include a better SEO url structure, new WordPress theme (disliked by many due to blue grey color) ,enhanced blogroll with labels, RSS feeds from NYT and DM Review besides my own poetry blog, and revamping the old pages. Let me know what you think about the change.

Edith on GT : A BI solution for Advanced Data Mining


    About the Author-   Edith Ohri heads a pioneering data-mining company in Israel which is dedicated to the application of GT – a new DM solution for unsupervised and complex data. Her background is Industrial & Management Engineering, MSc. She had started researching the issue of data mining in the early 80’s, and has continued with it ever since. She created a new model (GT) which enables larger and more complex data analysis. In 2002 she started in SMU Singapore the development of GT software. She is involved in several areas of implementation, such as: BI, Quality Control, Bio-med and Research. She manages a DM forums with Israel Engineering Association and a DM forum with the Data Warehouse site (Israel). She is a member and active participant in a number of DM forums, give presentations, and write articles.

December 31, 2008

GT data mining of NYSE companies – example

This is an example of data mining with GT, based on web free data from

The purpose is to demonstrate the ability to create a coherent explanation to complex, partial, incomplete, non-representative and unsupervised data. In this case the data also is restricted to a single point of time and exclude information regarding shares, and therefore is particularly difficult for analytics.

Given: two sets of 1000 records each about companies in the New York Stock Exchange year 2000 (just before the dotcom bubble burst). The records include 22 attribute describing the company field, its state of investments, assets, liabilities, expenses, R&D, sales, profits, dividends and other major elements from the Public Report Statement, except information regarding shares.

The method:

1. Define clusters based on just half of the data, find their characteristics and drivers, and conclude about the phenomena which they may represent.

2. Validate the results by projecting them on the other half of data. Once the stability of conclusions is re-affirmed, the following last part of the analysis.

3. Interpretation takes place. Usually it is done in collaboration with the client, in the example it shows basically just in outlines to give a sense of it.

General observation

The "heart" of the analytics is in the automatic clustering – here the pattern splits to two, and between them an exceptions subgroup:

1. Financially intense industries, such as Banking, Financial Services, Energy and Real Estates; an exception subgroup some of which financial companies have an extremely high sales profit margin – see discussion in Fig.4.

2. The rest of industries – Business Services, Transportation, Communication, Technology at large, Raw Materials, and Health Care. See Cluster map Fig.1.


Fig. 1 Cluster map: strong relations among record clusters are marked by Red Purple, no-relations are marked in Light Green. The map shows polarized patterns, the financial (in the low top) and the rest. Next to the Financial pattern there is a small exception sub-group, titled in Red. Note that the Technology pattern is much diverse

Conclusions and explanations

After clustering of the data, the pattern and characteristics become easier to spot, and their typical behavior is more noticeable. Following is the description of clusters that were found with GT, and an interpretation of their typical behavior.

1. False profitability – a warning sign

GT finds that some Technology companies "behave" like financial companies, instead of their own industry’s behavior. It may be explained by the ease of raising money in 2000 "heated" Stock Exchange, and the practical option that was opened to companies to use the excessive funds for financial activities. In such a case, the reported high profits of companies may be a symptom of a dangerously inflated market rather than sign of sound companies, and while the graphs which show profitability encourage investors to continue in that practice, they are racing toward a dead end – the DotCom crisis.

2. Investments in loosing companies – high risk

In the Technology cluster, there are companies that have substantial losses yet manage to attract massive investments. Their characteristics are: low levels of long term liabilities and long term assets, and a high level of preferred stocks. An unlikely negative relation (instead of a positive one) is found to exist in these companies between Total Assets and Net Income. See Fig.2.


Fig. 2 Technology companies: special behavior. In the red part there is an irrational phenomenon, where losing companies seem to attract investments

3. Conglomerates with "Banks" traits – need to be looked into

GT defines at the margins of the Financial and Technology clusters, a number of conglomerates, all of which have an exceptional "behavior". Although there are mainly industrial companies, their patterns resemble the Energy and the Financial ones.

Remark: knowing the characteristics of the exception behavior enables the analyst to "comb" the entire database and find by the use of a straight query, additional companies that might demonstrate similar irregularity, for close up study.

Fig. 3 In Statistics the special pattern of Technology does not show; it is un-distinguishable from the general pattern of behavior

GT Second edition note

The upgraded new version of GT shades more light over the 2000 phenomenon and reveals among the rest an interesting exceptional behavior of a few financial organizations, which apparently found a different way to make money… Their profit seems to enjoy a much larger net value than of other companies. See the chart below. The organizations are: HSBC Holdings PLC, Chase Manhattan Corp., and Societe Generale Group. This fact may be part of the kind of practices that have led 8 years later in 2008 to the "credit crunch".


Fig. 4 Exceptional high sales profits ratio is observed in a sub-group of financial organizations – companies such as HSBC Holdings, Chase Manhattan, and Societe General

Final words

GT produces a fresh view of complex unsupervised data. It can track down even minute and rare phenomena (3 out of 1000 companies), an give early signals to financial managers and analysts about the things to come, their patterns, spread, drivers and key indicators. The study of this example belongs to a series of applications in which GT has consistently turns out ordinary data to new revelations on "what makes it tick".


© Edith Ohri

Procedureware Ltd. POB 16558 Tel-Aviv 61165

Tel: 972-3-5232164