Brief Interview with James G Kobielus

Here is a brief one question interview with James Kobielus, Senior Analyst, Forrester.

Ajay-Describe the five most important events in Predictive Analytics you saw in 2010 and the top three trends in 2011 as per you.

Jim-

Five most important developments in 2010:

  • Continued emergence of enterprise-grade Hadoop solutions as the core of the future cloud-based platforms for advanced analytics
  • Development of the market for analytic solution appliances that incorporate several key features for advanced analytics: massively parallel EDW appliance, in-database analytics and data management function processing, embedded statistical libraries, prebuilt logical domain models, and integrated modeling and mining tools
  • Integration of advanced analytics into core BI platforms with user-friendly, visual, wizard-driven, tools for quick, exploratory predictive modeling, forecasting, and what-if analysis by nontechnical business users
  • Convergence of predictive analytics, data mining, content analytics, and CEP in integrated tools geared  to real-time social media analytics
  • Emergence of CRM and other line-of-business applications that support continuously optimized “next-best action” business processes through embedding of predictive models, orchestration engines, business rules engines, and CEP agility

Three top trends I see in the coming year, above and beyond deepening and adoption of the above-bulleted developments:

  • All-in-memory, massively parallel analytic architectures will begin to gain a foothold in complex EDW environments in support of real-time elastic analytics
  • Further crystallization of a market for general-purpose “recommendation engines” that, operating inline to EDWs, CEP environments, and BPM platforms, enable “next-best action” approaches to emerge from today’s application siloes
  • Incorporation of social network analysis functionality into a wider range of front-office business processes to enable fine-tuned behavioral-based customer segmentation to drive CRM optimization

About –http://www.forrester.com/rb/analyst/james_kobielus

James G. Kobielus
Senior Analyst, Forrester Research

RESEARCH FOCUS

James serves Business Process & Applications professionals. He is a leading expert on data warehousing, predictive analytics, data mining, and complex event processing. In addition to his core coverage areas, James contributes to Forrester’s research in business intelligence, data integration, data quality, and master data management.

PREVIOUS WORK EXPERIENCE

James has a long history in IT research and consulting and has worked for both vendors and research firms. Most recently, he was at Current Analysis, an IT research firm, where he was a principal analyst covering topics ranging from data warehousing to data integration and the Semantic Web. Prior to that position, James was a senior technical systems analyst at Exostar (a hosted supply chain management and eBusiness hub for the aerospace and defense industry). In this capacity, James was responsible for identifying and specifying product/service requirements for federated identity, PKI, and other products. He also worked as an analyst for the Burton Group and was previously employed by LCC International, DynCorp, ADEENA, International Center for Information Technologies, and the North American Telecommunications Association. He is both well versed and experienced in product and market assessments. James is a widely published business/technology author and has spoken at many industry events

KXEN EMEA User Conference 2010-Success in Business Analytics

KXEN User Conference-Prelim Agenda is out

Source-

http://www.kxen.com/index.php?option=com_content&task=view&id=647&Itemid=1109

THURSDAY, OCTOBER 28, 2010
09:30-10:00 AM Registration & Breakfast

10:00-10:45 AM Welcome & Opening Remarks,
by John Ball, CEO KXEN
10:45-11:30 AM Keynote Session by James Kobielus,
Senior Analyst at Forrester Research, Inc. and author
of “The Forrester WaveTM: Predictive Analytics & Data Mining Solutions, Q1 2010” report 

11:30-12:05 AM Customer Case Study:
The European Commission (Government)
12:05-12:50 PM General Session:
Teradata Advanced Analytics
12:50-02:00 PM Lunch Break & Exhibition
02:00-02:35 PM Customer Case Study: 
Virgin Media
(Communications)
02:35-03:05 PM General Session:
Sponsor Presentation
03:05-03:40 PM
Coffee Break & Exhibition

03:40-04:40 PM General Session:
The Factory Approach to Compete on Analytics
04:40-05:25 PM Customer Case Study: 
Insurance
05:30-06:30 PM Cocktail & Exhibition
07:30-00:00 PM Gala Dinner
FRIDAY, OCTOBER 29, 2010
08:30-09:00 AM
Registration & Breakfast

09:00-10:00 AM Keynote Presentation:
The CTO Talk
10:00-10:30 AM Customer Case Study: 
MonotaRO
(Japan – Retail)
10:30-10:55 AM
Coffee Break & Exhibition

10:55-11:30 AM General Session: 
Sponsor Presentation
11:30-12:05 PM Customer Case Study: 
Aviva
(Poland – Insurance)
12:05-01:00 PM Lunch Break & Exhibition
01:00-01:45 PM General Session: 
How Social Network Analysis Can Boost your Marketing Performance
01:45-02:20 PM Customer Case Study:
Financial Services
02:20-02:45 PM Closing Remarks,
by John Ball, CEO KXEN
02:45-03:00 PM
Coffee Break & Exhibition

Optional: Technical Training (Complimentary to all Attendees)
02:45-04:00 PM Hands-On Training #1: Getting Started with KXEN Analytical Data Management (ADM)
04:00-04:15 PM
Coffee Break

04:15-05:30 PM Hands-On Training #2: Getting Started with KXEN Modeling Factory (KMF)

Interview Françoise Soulie Fogelman, KXEN

This week KXEN launched its social network analysis tool thus gaining a unique edge in being the first to launch social network tools for analytics. Having worked with KXEN as an analyst for scoring model- I am aware of the remarkable innovations they bring to their premium products. In an exclusive interview ,KXENs Vice President for  Strategic Business Development, Franoise Soulie Fogelman agreed to share some light on this remarkable new development in statistical software development.

 

Ajay Franoise, how does the Social Network Analysis module helps model building for marketing professionals.

Franoise- KXEN Social network Analysis module (KSN) helps build models which take into account interactions between customers. This is done in 3 stages :

  • The data describing interactions is used to build a social network structure (actually, usually various social network structures are built in one pass through the data). You can explore your network to understand better the behavior of a given customer and what is happening around him.
  • From each social network structure, a set of attributes is automatically built by KSN for each node: it could be number of neighbors, average value of a given customer attribute among neighbors Actually, you can have statistics on anything you have loaded into the system as customer node decoration. Usually, youll generate at this stage a few tens of social attributes per social network structure.
  • You then join these social attributes to the existing customer attributes. After that, you build your model as usual.

Ajay  But how does the KSN module work and which mathematical technique is it based on (or is it just addition of extra variables). Are there any proprietary patents that KXEN have filed in this field (both automated modeling as well as social network analysis).

Franoise- The KSN module uses (for extracting social attributes) graph theory. KXEN has not filed a patent in relation with KSN.

Ajay  There are many modeling software but very few which involve social network analysis though many companies have expressed interest in this. What are the present rivals to KSN module specifically in software and who do you think the future rivals will be?

Franoise- There are many software tools, but when it comes to the ability to handle very large graphs, not very many are left. We consider that our only real competitor today is SAS who has an offer for Social network Analysis, but this product is specifically targeted for fraud in bank and insurance. There are also companies positioned in Telco, usually offering a consulting service, built around an internal product. We think our solution is unique in its ability to handle very large volumes (were talking here more than 40 M nodes and 300 M links) and to address all industry domains. As usual, we offer a tool which is an exploratory tool, giving the customer the ability to produce by himself as many models as he wants.

Ajay  Who would be the typical customer or potential clients for KSN module? In which domains would this module be not so relevant? Are there any specific case studies that you can point out?

Franoise- This is a first version, so we do not really know yet who the typical customer will be and cannot point yet to case studies. However, Telco operators have expressed a very strong interest and we already have a Telco customer with whom weve worked on marketing projects. So our first case studies will most certainly come from Telco. We are working on some research projects in the retail space. We think that banks (for fraud), social sites, blogs sites and forums will be our next customers. The sector where I do not see (yet?) a potential is manufacturing industries.

Ajay  How would privacy concerns of customers be addressed with the kind of social network analysis that KSN can now offer to marketers.

Franoise- KXEN offers a tool to build models and is not concerned with the problem of collecting, storing and exploiting data: this is KXEN customers responsibility. Depending upon the country, there are various jurisdictions protecting the storage and use of data and those will naturally apply to building and analyzing Social Networks. However, in the case of Social Network Analysis the issue of ethical use will be more sensitive.

Ajay What kind of hardware solutions go best with KXEN’s software. What are the other BI vendors that your offerings best complement with.

Franoise – KXEN software in general and KSN in particular, run on any platform. When using KSN to build decent size graphs (with tens of millions of nodes and hundreds of millions of links for example), 64 bits architecture is required. A recent survey of KXEN customers show that the BI suites used by our customers are mostly MicroStrategy and Business Objects (SAP). We also like very much to mention Advizor Solutions which offers data visualization software already embedding KXEN technology.

Ajay Do you think the text mining as well as the Data Fusion approach can work for online web analytics, search engines or ad targeting?

Franoise –Of course, our data fusion approach can be very well suited for online web analytics and ad targeting (we have a number of partners that either are already using KXEN for this purpose or developing applications in these domains using KXEN technology). We would be more cautious for search engines per se.

Ajay Are there any plans for offering KXEN products as a Service (like Salesforce.com) instead of the server based approach?

Franoise – We do not have yet plans to offer KXEN products as a service yet, but, again, we have partners such as Kognitio that offers analytics platforms embedding KXEN.

 

Brief Biography-

 

Franoise Soulie Fogelman is responsible for leading KXEN business development, identifying new business opportunities for KXEN and working with Product development, Sales and Marketing to help promote KXENs offer. She is also in charge of managing KXENs University Program.

Ms Soulie Fogelman has over 30 years of experience in data mining and CRM both from an academic and a business perspective. Prior to KXEN, she directed the first French research team on Neural Networks at Paris 11 University where she was a CS Professor. She then co-founded Mimetics, a start-up that processes and sells development environment, optical character recognition (OCR) products and services using neural network technology, and became its Chief Scientific Officer. After that she started the Data Mining and CRM group at Atos Origin and, most recently, she created and managed the CRM Agency for Business & Decision, a French IS company specialized in Business Intelligence and CRM.

Ms Soulie Fogelman holds a masters degree in mathematics from Ecole Normale Superieure and a PhD in Computer Science from University of Grenoble. She was advisor to over 20 PhD on data mining, has authored more than 100 scientific papers and books and has been an invited speaker to many academic and business events.

 

   ( Ajay – So it seems like an interesting software and with the marketing avenues for social networking growing, and analytics modelers exploring the last bit of data for incremental field this is an area where we can be sure of new developments soon. I wonder what the response from other analytics vendors including open source developers would be as this does seem a promising area for statistical modelling as well as analysis. What do you think ?? Can I search all data from Twitter , Facebook ,search results on Indeed .com and Linkedin and add it to your credit profile for creating a better propensity model .. 🙂 Will the credit or marketing behavior scores of your friends affect your propensity and thus the telecom ads you see while surfing )

KXEN releases Social Network Analysis tool

KXEN, the automated model making software company added one more innovation by being one of the first major data mining and analytics vendors in releasing an analytics tool for Social Network Analysis

From the Press Release

Press release

San Francisco, Paris, London, March 24th, 2009

New Social Network Analysis Module Strengthens KXEN Automated Data Mining

Leverages connections between people to boost marketing campaign results and profitability

Sales, marketing and customer retention campaigns are set to become smarter, more effective and more profitable thanks to a new social network analysis module from data mining automation vendor KXEN. By exploiting the connections between customers of telcos, banks, retails and others, KXEN’s new KSN module has shown more than 15% lift improvement in campaign results.

KSN identifies the otherwise hidden links call records or bank transfers for instance -between friends, families, co-workers and other communities and extracts significant social metrics, pinpointing who are the best connected and who plays the most important role in any group. In this way it reveals valuable new customer intelligence that – when added to existing customer information – can strengthen significantly user organizations’ customer acquisition, retention, cross-sell and up-sell campaigns.

Using KSN, companies can increase the accuracy and precision of their campaigns by leveraging the many more customer attributes that the module reveals, allowing them to better predict when customers may be about to churn to another provider, close an account, or buy a new product. A feature unique-to-KXEN allows the analysis of multiple networks and their evolution over time, exposing specific patterns of behaviors like rotational churn, fraud and identity theft.

"Traditional marketing relies on models based solely on customer-vendor interaction and assumes customers act independently of each other," says KXEN’s founder and CEO Roger Haddad. "But the new social network technology in KSN recognizes that customers do indeed interact with each other, and exploits that knowledge to drive up the effectiveness and completeness of marketing and sales campaigns."

KSN integrates with and enriches existing data mining environments or may be deployed entirely standalone. Exploiting viral marketing thinking, it eliminates the normally tedious and labor intensive aspects of social network analysis. The module provides as many social network maps as users want, recognizing that individuals may belong to many different networks across their business, family and social lives.

KSN, shipping from today, complements the existing KXEN Text Coder module which allows organizations to include plain text data into their analytics activities. Together the two modules, along with KXEN’s core software, are behind the company’s Data Fusion approach which combines structured and unstructured data from multiple sources to generate fast accurate results, thus maximizing organizations return on analytics investments.

Please click here to learn more about the KXEN Social Network Analysis module.

About KXEN

KXEN, The Data Mining Automation Company delivers next-generation Customer Lifecycle Analytics to enterprises that depend on analytics as a competitive advantage. KXEN’s Data Mining Automation Solution drives significant improvements in customer acquisition, retention, cross-sell and risk applications. Its solution integrates predictive analytics into strategic business processes, allowing customers to drive greater value into their business. Find out more by visiting www.kxen.com.

I found the statement by the CEO quite interesting –

 

"Traditional marketing relies on models based solely on customer-vendor interaction and assumes customers act independently of each other," says KXEN’s founder and CEO Roger Haddad. "But the new social network technology in KSN recognizes that customers do indeed interact with each other, and exploits that knowledge to drive up the effectiveness and completeness of marketing and sales campaigns."

 

I wonder if other analytics vendors are creating /releasing products like these.

Using Web 2.0 for Analytics 2.0

Here is a great video tutorial on You Tube by Zementis, creator of ADAPA,the cloud scoring engine for next gen predictive analytics. You can watch it on the URL or below-

http://www.youtube.com/watch?v=8hNqxqrdXLI

 

A few weeks back, I was working with the ADAPA engine on a consulting gig, and Ron Ramos, the head of sales mentioned that though they have extensive documentation, they were planning a video tutorial as well on You Tube.

Beats a pdf everytime , doesnt it !!!

I wonder why companies continue to spend huge and I mean huge amounts on white papers and PDFs when they can have much better customer support using a bit of audio, video and even twitter support.

Surprisingly true even for companies working at the cutting edge with other technologies.And the essentially free availability of these tools.

 

I mean if companies can spend huge amounts for predictive solutions for the big big datasets , why cant they offer some solutions or apps for the web and social media- An exception is KXEN of course with a new Social Network Analysis Module here ).

Imagine a future –

( Example

  • Hello SAS , My code wont run blah blah blah

SAS Support on Twitter..okay do this

or

  • Hello SPSS, Where Can I find some stuff on Python because I got lost on the website
  • SPSS Support on Skype/Twitter- Dude , do this , click this link !

)

It is much better than endless rounds of email, aggravation and the list server method is well the users should try and test www.twitter.com for user groups )