Interview David Katz ,Dataspora /David Katz Consulting

Here is an interview with David Katz ,founder of David Katz Consulting (http://www.davidkatzconsulting.com/) and an analyst at the noted firm http://dataspora.com/. He is a featured speaker at Predictive Analytics World  http://www.predictiveanalyticsworld.com/sanfrancisco/2011/speakers.php#katz)

Ajay-  Describe your background working with analytics . How can we make analytics and science more attractive career options for young students

David- I had an interest in math from an early age, spurred by reading lots of science fiction with mathematicians and scientists in leading roles. I was fortunate to be at Harry and David (Fruit of the Month Club) when they were in the forefront of applying multivariate statistics to the challenge of targeting catalogs and other snail-mail offerings. Later I had the opportunity to expand these techniques to the retail sphere with Williams-Sonoma, who grew their retail business with the support of their catalog mailings. Since they had several catalog titles and product lines, cross-selling presented additional analytic challenges, and with the growth of the internet there was still another channel to consider, with its own dynamics.

After helping to found Abacus Direct Marketing, I became an independent consultant, which provided a lot of variety in applying statistics and data mining in a variety of settings from health care to telecom to credit marketing and education.

Students should be exposed to the many roles that analytics plays in modern life, and to the excitement of finding meaningful and useful patterns in the vast profusion of data that is now available.

Ajay-  Describe your most challenging project in 3 decades of experience in this field.

David- Hard to choose just one, but the educational field has been particularly interesting. Partnering with Olympic Behavior Labs, we’ve developed systems to help identify students who are most at-risk for dropping out of school to help target interventions that could prevent dropout and promote success.

Ajay- What do you think are the top 5 trends in analytics for 2011.

David- Big Data, Privacy concerns, quick response to consumer needs, integration of testing and analysis into business processes, social networking data.

Ajay- Do you think techniques like RFM and LTV are adequately utilized by organization. How can they be propagated further.

David- Organizations vary amazingly in how sophisticated or unsophisticated the are in analytics. A key factor in success as a consultant is to understand where each client is on this continuum and how well that serves their needs.

Ajay- What are the various software you have worked for in this field- and name your favorite per category.

David- I started out using COBOL (that dates me!) then concentrated on SAS for many years. More recently R is my favorite because of its coverage, currency and programming model, and it’s debugging capabilities.

Ajay- Independent consulting can be a strenuous job. What do you do to unwind?

David- Cycling, yoga, meditation, hiking and guitar.

Biography-

David Katz, Senior Analyst, Dataspora, and President, David Katz Consulting.

David Katz has been in the forefront of applying statistical models and database technology to marketing problems since 1980. He holds a Master’s Degree in Mathematics from the University of California, Berkeley. He is one of the founders of Abacus Direct Marketing and was previously the Director of Database Development for Williams-Sonoma.

He is the founder and President of David Katz Consulting, specializing in sophisticated statistical services for a variety of applications, with a special focus on the Direct Marketing Industry. David Katz has an extensive background that includes experience in all aspects of direct marketing from data mining, to strategy, to test design and implementation. In addition, he consults on a variety of data mining and statistical applications from public health to collections analysis. He has partnered with consulting firms such as Ernst and Young, Prediction Impact, and most recently on this project with Dataspora.

For more on David’s Session in Predictive Analytics World, San Fransisco on (http://www.predictiveanalyticsworld.com/sanfrancisco/2011/agenda.php#day2-16a)

Room: Salon 5 & 6
4:45pm – 5:05pm

Track 2: Social Data and Telecom 
Case Study: Major North American Telecom
Social Networking Data for Churn Analysis

A North American Telecom found that it had a window into social contacts – who has been calling whom on its network. This data proved to be predictive of churn. Using SQL, and GAM in R, we explored how to use this data to improve the identification of likely churners. We will present many dimensions of the lessons learned on this engagement.

Speaker: David Katz, Senior Analyst, Dataspora, and President, David Katz Consulting

Exhibit Hours
Monday, March 14th:10:00am to 7:30pm

Tuesday, March 15th:9:45am to 4:30pm

Carole-Ann’s 2011 Predictions for Decision Management

Carole-Ann’s 2011 Predictions for Decision Management

For Ajay Ohri on DecisionStats.com

What were the top 5 events in 2010 in your field?
  1. Maturity: the Decision Management space was made up of technology vendors, big and small, that typically focused on one or two aspects of this discipline.  Over the past few years, we have seen a lot of consolidation in the industry – first with Business Intelligence (BI) then Business Process Management (BPM) and lately in Business Rules Management (BRM) and Advanced Analytics.  As a result the giant Platform vendors have helped create visibility for this discipline.  Lots of tiny clues finally bubbled up in 2010 to attest of the increasing activity around Decision Management.  For example, more products than ever were named Decision Manager; companies advertised for Decision Managers as a job title in their job section; most people understand what I do when I am introduced in a social setting!
  2. Boredom: unfortunately, as the industry matures, inevitably innovation slows down…  At the main BRMS shows we heard here and there complaints that the technology was stalling.  We heard it from vendors like Red Hat (Drools) and we heard it from bored end-users hoping for some excitement at Business Rules Forum’s vendor panel.  They sadly did not get it
  3. Scrum: I am not thinking about the methodology there!  If you have ever seen a rugby game, you can probably understand why this is the term that comes to mind when I look at the messy & confusing technology landscape.  Feet blindly try to kick the ball out while superhuman forces are moving randomly the whole pack – or so it felt when I played!  Business Users in search of Business Solutions are facing more and more technology choices that feel like comparing apples to oranges.  There is value in all of them and each one addresses a specific aspect of Decision Management but I regret that the industry did not simplify the picture in 2010.  On the contrary!  Many buzzwords were created or at least made popular last year, creating even more confusion on a muddy field.  A few examples: Social CRM, Collaborative Decision Making, Adaptive Case Management, etc.  Don’t take me wrong, I *do* like the technologies.  I sympathize with the decision maker that is trying to pick the right solution though.
  4. Information: Analytics have been used for years of course but the volume of data surrounding us has been growing to unparalleled levels.  We can blame or thank (depending on our perspective) Social Media for that.  Sites like Facebook and LinkedIn have made it possible and easy to publish relevant (as well as fluffy) information in real-time.  As we all started to get the hang of it and potentially over-publish, technology evolved to enable the storage, correlation and analysis of humongous volumes of data that we could not dream of before.  25 billion tweets were posted in 2010.  Every month, over 30 billion pieces of data are shared on Facebook alone.  This is not just about vanity and marketing though.  This data can be leveraged for the greater good.  Carlos pointed to some fascinating facts about catastrophic event response team getting organized thanks to crowd-sourced information.  We are also seeing, in the Decision management world, more and more applicability for those very technology that have been developed for the needs of Big Data – I’ll name for example Hadoop that Carlos (yet again) discussed in his talks at Rules Fest end of 2009 and 2010.
  5. Self-Organization: it may be a side effect of the Social Media movement but I must admit that I was impressed by the success of self-organizing initiatives.  Granted, this last trend has nothing to do with Decision Management per se but I think it is a great evolution worth noting.  Let me point to a couple of examples.  I usually attend traditional conferences and tradeshows in which the content can be good but is sometimes terrible.  I was pleasantly surprised by the professionalism and attendance at *un-conferences* such as P-Camp (P stands for Product – an event for Product Managers).  When you think about it, it is already difficult to get a show together when people are dedicated to the tasks.  How crazy is it to have volunteers set one up with no budget and no agenda?  Well, people simply show up to do their part and everyone has fun voting on-site for what seems the most appealing content at the time.  Crowdsourcing applied to shows: it works!  Similar experience with meetups or tweetups.  I also enjoyed attending some impromptu Twitter jam sessions on a given topic.  Social Media is certainly helping people reach out and get together in person or virtually and that is wonderful!

A segment of a social network
Image via Wikipedia

What are the top three trends you see in 2011?

  1. Performance:  I might be cheating here.   I was very bullish about predicting much progress for 2010 in the area of Performance Management in your Decision Management initiatives.  I believe that progress was made but Carlos did not give me full credit for the right prediction…  Okay, I am a little optimistic on timeline…  I admit it…  If it did not fully happen in 2010, can I predict it again in 2011?  I think that companies want to better track their business performance in order to correct the trajectory of course but also to improve their projections.  I see that it is turning into reality already here and there.  I expect it to become a trend in 2011!
  2. Insight: Big Data being available all around us with new technologies and algorithms will continue to propagate in 2011 leading to more widely spread Analytics capabilities.  The buzz at Analytics shows on Social Network Analysis (SNA) is a sign that there is interest in those kinds of things.  There is tremendous information that can be leveraged for smart decision-making.  I think there will be more of that in 2011 as initiatives launches in 2010 will mature into material results.
    5 Ways to Cultivate an Active Social Network
    Image by Intersection Consulting via Flickr
  3. Collaboration:  Social Media for the Enterprise is a discipline in the making.  Social Media was initially seen for the most part as a Marketing channel.  Over the years, companies have started experimenting with external communities and ideation capabilities with moderate success.  The few strategic initiatives started in 2010 by “old fashion” companies seem to be an indication that we are past the early adopters.  This discipline may very well materialize in 2011 as a core capability, well, or at least a new trend.  I believe that capabilities such Chatter, offered by Salesforce, will transform (slowly) how people interact in the workplace and leverage the volumes of social data captured in LinkedIn and other Social Media sites.  Collaboration is of course a topic of interest for me personally.  I even signed up for Kare Anderson’s collaboration collaboration site – yes, twice the word “collaboration”: it is really about collaborating on collaboration techniques.  Even though collaboration does not require Social Media, this medium offers perspectives not available until now.

Brief Bio-

Carole-Ann is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry. Her claim to fame is the strategy and direction of Blaze Advisor, the then-leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience. She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

Leveraging her Masters degree in Applied Mathematics / Computer Science from a “Grande Ecole” in France, she started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication – as well as conducting strategic consulting gigs around change management.

She now tweets as @CMatignon, blogs at blog.sparklinglogic.com and interacts at community.sparklinglogic.com.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication.  At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs mostly around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM).  She developed a growing interest in Optimization as well as Business Rules.  At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart.  She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business.  Her technical background kept her very much in touch with technology as she advanced.

She also became addicted to Twitter in the process.  She is active on all kinds of social media, always looking for new digital experience!

Outside of work, Carole-Ann loves spending time with her two boys.  They grow fruits in their Northern California home and cook all together in the French tradition.

profile on LinkedIn

TwitterFollow me on Twitter

Filtering to Gain Social Network Value
Image by Intersection Consulting via Flickr
Social Networks Hype Cycle
Image by fredcavazza via Flickr

An Introduction to Data Mining-online book

I was reading David Smith’s blog http://blog.revolutionanalytics.com/

where he mentioned this interview of Norman Nie, at TDWI

http://tdwi.org/Articles/2010/11/17/R-101.aspx?Page=2

where I saw this link (its great if you want to study Data Mining btw)

http://www.kdnuggets.com/education/usa-canada.html

and I c/liked the U Toronto link

http://chem-eng.utoronto.ca/~datamining/

Best of All- I really liked this online book created by Professor S. Sayad

Its succinct and beautiful and describes all of the Data Mining you want to read in one Map (actually 4 images painstakingly assembled with perfection)

The best thing is- in the original map- even the sub items are click-able for specifics like Pie Chart and Stacked Column chart are not in one simple drop down like Charts- but rather by nature of the kind of variables that lead to these charts. For doing that- you would need to go to the site itself- ( see http://chem-eng.utoronto.ca/~datamining/dmc/categorical_variables.htm

vs

http://chem-eng.utoronto.ca/~datamining/dmc/categorical_numerical.htm

Again- there is no mention of the data visualization software used to create the images but I think I can take a hint from the Software Page which says software used are-

Software

See it on your own-online book (c)Professor S. Sayad

Really good DIY tutorial

http://chem-eng.utoronto.ca/~datamining/dmc/data_mining_map.htm

Short Interview Jill Dyche

Here is brief one question interview with Jill Dyche , founder Baseline Consulting.

 

In 2010.

 

  • It was more about consciousness-raising in the executive suite—
  • getting C-level managers to understand the ongoing value proposition of BI,
  • why MDM isn’t their father’s database, and
  • how data governance can pay for itself over time.
  • Some companies succeeded with these consciousness-raising efforts. Some didn’t.

 

But three big ones in 2011 would be:

  1. Predictive analytics in the cloud. The technology is now ready, and so is the market—and that includes SMB companies.
  2. Enterprise search being baked into (commoditized) BI software tools. (The proliferation of static reports is SO 2006!)
  3. Data governance will begin paying dividends. Until now it was all about common policies for data. In 2011, it will be about ROI.

I do a “Predictions for the coming year” article every January for TDWI,

Note- Jill ‘s January TDWI article seems worth waiting for in this case.

About-

Source-http://www.baseline-consulting.com/pages/page.asp?page_id=49125

Partner and Co-Founder

Jill Dyché is a partner and co-founder of Baseline Consulting.  She is responsible for key client strategies and market analysis in the areas of data governance, business intelligence, master data management, and customer relationship management. 

Jill counsels boards of directors on the strategic importance of their information investments.

Author

Jill is the author of three books on the business value of IT. Jill’s first book, e-Data (Addison Wesley, 2000) has been published in eight languages. She is a contributor to Impossible Data Warehouse Situations: Solutions from the Experts (Addison Wesley, 2002), and her book, The CRM Handbook (Addison Wesley, 2002), is the bestseller on the topic. 

Jill’s work has been featured in major publications such as Computerworld, Information Week, CIO Magazine, the Wall Street Journal, the Chicago Tribune and Newsweek.com. Jill’s latest book, Customer Data Integration (John Wiley and Sons, 2006) was co-authored with Baseline partner Evan Levy, and shows the business breakthroughs achieved with integrated customer data.

Industry Expert

Jill is a featured speaker at industry conferences, university programs, and vendor events. She serves as a judge for several IT best practice awards. She is a member of the Society of Information Managementand Women in Technology, a faculty member of TDWI, and serves as a co-chair for the MDM Insight conference. Jill is a columnist for DM Review, and a blogger for BeyeNETWORK and Baseline Consulting.

 

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

Amazon goes HPC and GPU: Dirk E to revise his R HPC book

Looking south above Interstate 80, the Eastsho...
Image via Wikipedia

Amazon just did a cluster Christmas present for us tech geek lizards- before Google could out doogle them with end of the Betas (cough- its on NDA)

Clusters used by Academic Departments now have a great chance to reduce cost without downsizing- but only if the CIO gets the email.

While Professor Goodnight of SAS / North Carolina University is still playing time sharing versus mind sharing games with analytical birdies – his 70 mill server farm set in Feb last is about to get ready

( I heard they got public subsidies for environment- but thats historic for SAS– taking public things private -right Prof as SAS itself began as a publicly funded project. and that was in the 1960s and they didnt even have no lobbyists as well. )

In realted R news, Dirk E has been thinking of a R HPC book without paying attention to Amazon but would now have to include Amazon

(he has been thinking of writing that book for 5 years, but hey he’s got a day job, consulting gigs with revo, photo ops at Google, a blog, packages to maintain without binaries, Dirk E we await thy book with bated holes.

Whos Dirk E – well http://dirk.eddelbuettel.com/ is like the Terminator of R project (in terms of unpronounceable surnames)

Back to the cause du jeure-

 

From http://aws.amazon.com/ec2/hpc-applications/ but minus corporate buzz words.

 

Unique to Cluster Compute and Cluster GPU instances is the ability to group them into clusters of instances for use with HPC

applications. This is particularly valuable for those applications that rely on protocols like Message Passing Interface (MPI) for tightly coupled inter-node communication.

Cluster Compute and Cluster GPU instances function just like other Amazon EC2 instances but also offer the following features for optimal performance with HPC applications:

  • When run as a cluster of instances, they provide low latency, full bisection 10 Gbps bandwidth between instances. Cluster sizes up through and above 128 instances are supported.
  • Cluster Compute and Cluster GPU instances include the specific processor architecture in their definition to allow developers to tune their applications by compiling applications for that specific processor architecture in order to achieve optimal performance.

The Cluster Compute instance family currently contains a single instance type, the Cluster Compute Quadruple Extra Large with the following specifications:

23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge

The Cluster GPU instance family currently contains a single instance type, the Cluster GPU Quadruple Extra Large with the following specifications:

22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge

.

Sign Up for Amazon EC2

Scoring SAS and SPSS Models in the cloud

Outline of a cloud containing text 'The Cloud'
Image via Wikipedia

An announcement from Zementis and Predixion Software– about using cloud computing for scoring models using PMML. Note R has a PMML package as well which is used by Rattle, data mining GUI for exporting models.

Source- http://www.marketwatch.com/story/predixion-software-introduces-new-product-to-run-sas-and-spss-predictive-models-in-the-cloud-2010-10-19?reflink=MW_news_stmp

——————————————————————————————————–

ALISO VIEJO, Calif., Oct 19, 2010 (BUSINESS WIRE) — Predixion Software today introduced Predixion PMML Connexion(TM), an interface that provides Predixion Insight(TM), the company’s low-cost, self-service in the cloud predictive analytics solution, direct and seamless access to SAS, SPSS (IBM) and other predictive models for use by Predixion Insight customers. Predixion PMML Connexion enables companies to leverage their significant investments in legacy predictive analytics solutions at a fraction of the cost of conventional licensing and maintenance fees.

The announcement was made at the Predictive Analytics World conference in Washington, D.C. where Predixion also announced a strategic partnership with Zementis, Inc., a market leader in PMML-based solutions. Zementis is exhibiting in Booth #P2.

The Predictive Model Markup Language (PMML) standard allows for true interoperability, offering a mature standard for moving predictive models seamlessly between platforms. Predixion has fully integrated this PMML functionality into Predixion Insight, meaning Predixion Insight users can now effortlessly import PMML-based predictive models, enabling information workers to score the models in the cloud from anywhere and publish reports using Microsoft Excel(R) and SharePoint(R). In addition, models can also be written back into SAS, SPSS and other platforms for a truly collaborative, interoperable solution.

“Predixion’s investment in this PMML interface makes perfect business sense as the lion’s share of the models in existence today are created by the SAS and SPSS platforms, creating compelling opportunity to leverage existing investments in predictive and statistical models on a low-cost cloud predictive analytics platform that can be fed with enterprise, line of business and cloud-based data,” said Mike Ferguson, CEO of Intelligent Business Strategies, a leading analyst and consulting firm specializing in the areas of business intelligence and enterprise business integration. “In this economy, Predixion’s low-cost, self-service predictive analytics solutions might be welcome relief to IT organizations chartered with quickly adding additional applications while at the same time cutting costs and staffing.”

“We are pleased to be partnering with Zementis, truly a PMML market leader and innovator,” said Predixion CEO Simon Arkell. “To allow any SAS or SPSS customer to immediately score any of their predictive models in the cloud from within Predixion Insight, compare those models to those created by Predixion Insight, and share the results within Excel and Sharepoint is an exciting step forward for the industry. SAS and SPSS customers are fed up with the high prices they must pay for their business users just to access reports generated by highly skilled PhDs who are burdened by performing routine tasks and thus have become a massive bottleneck. That frustration is now a thing of the past because any information worker can now unlock the power of predictive analytics without relying on experts — for a fraction of the cost and from anywhere they can connect to the cloud,” Arkell said.

Dr. Michael Zeller, Zementis CEO, added, “Our mission is to significantly shorten the time-to-market for predictive models in any industry. We are excited to be contributing to Predixion’s self-service, cloud-based predictive analytics solution set.”

About Predixion Software

Predixion Software develops and markets collaborative predictive analytics solutions in the public and private cloud. Predixion enables self-service predictive analytics, allowing customers to use and analyze large amounts of data to make actionable decisions, all within the familiar environment of Excel and PowerPivot. Predixion customers are achieving immediate results across a multitude of industries including: retail, finance, healthcare, marketing, telecommunications and insurance/risk management.

Predixion Software is headquartered in Aliso Viejo, California with development offices in Redmond, Washington. The company has venture capital backing from established investors including DFJ Frontier, Miramar Venture Partners and Palomar Ventures. For more information please contact us at 949-330-6540, or visit us atwww.predixionsoftware.com.

About Zementis

Zementis, Inc. is a leading software company focused on the operational deployment and integration of predictive analytics and data mining solutions. Its ADAPA(R) decision engine successfully bridges the gap between science and engineering. ADAPA(R) was designed from the ground up to benefit from open standards and to significantly shorten the time-to-market for predictive models in any industry. For more information, please visit www.zementis.com.