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Analytics 2012 Conference

from http://www.sas.com/events/analytics/us/index.html

Analytics 2012 Conference

SAS and more than 1,000 analytics experts gather at

Caesars Palace
Caesars Palace

Analytics 2012 Conference Details

Pre-Conference Workshops – Oct 7
Conference – Oct 8-9
Post-Conference Training – Oct 10-12
Caesars Palace, Las Vegas

Keynote Speakers

The following are confirmed keynote speakers for Analytics 2012. Jim Goodnight Since he co-founded SAS in 1976, Jim Goodnight has served as the company’s Chief Executive Officer.

William Hakes Dr. William Hakes is the CEO and co-founder of Link Analytics, an analytical technology company focused on mobile, energy and government verticals.

Tim Rey Tim Rey  has written over 100 internal papers, published 21 external papers, and delivered numerous keynote presentations and technical talks at various quantitative methods forums. Recently he has co-chaired both forecasting and data mining conferences. He is currently in the process of co-writing a book, Applied Data Mining for Forecasting.

http://www.sas.com/events/analytics/us/train.html

Pre-Conference

Plan to come to Analytics 2012 a day early and participate in one of the pre-conference workshops or take a SAS Certification exam. Prices for all of the preconference workshops, except for SAS Sentiment Analysis Studio: Introduction to Building Models and the Business Analytics Consulting Workshops, are included in the conference package pricing. You will be prompted to select your pre-conference training options when you register.

Sunday Morning Workshop

SAS Sentiment Analysis Studio: Introduction to Building Models

This course provides an introduction to SAS Sentiment Analysis Studio. It is designed for system designers, developers, analytical consultants and managers who want to understand techniques and approaches for identifying sentiment in textual documents.
View outline
Sunday, Oct. 7, 8:30a.m.-12p.m. – $250

Sunday Afternoon Workshops

Business Analytics Consulting Workshops

This workshop is designed for the analyst, statistician, or executive who wants to discuss best-practice approaches to solving specific business problems, in the context of analytics. The two-hour workshop will be customized to discuss your specific analytical needs and will be designed as a one-on-one session for you, including up to five individuals within your company sharing your analytical goal. This workshop is specifically geared for an expert tasked with solving a critical business problem who needs consultation for developing the analytical approach required. The workshop can be customized to meet your needs, from a deep-dive into modeling methods to a strategic plan for analytic initiatives. In addition to the two hours at the conference location, this workshop includes some advanced consulting time over the phone, making it a valuable investment at a bargain price.
View outline
Sunday, Oct. 7; 1-3 p.m. or 3:30-5:30 p.m. – $200

Demand-Driven Forecasting: Sensing Demand Signals, Shaping and Predicting Demand

This half-day lecture teaches students how to integrate demand-driven forecasting into the consensus forecasting process and how to make the current demand forecasting process more demand-driven.
View outline
Sunday, Oct. 7; 1-5 p.m.

Forecast Value Added Analysis

Forecast Value Added (FVA) is the change in a forecasting performance metric (such as MAPE or bias) that can be attributed to a particular step or participant in the forecasting process. FVA analysis is used to identify those process activities that are failing to make the forecast any better (or might even be making it worse). This course provides step-by-step guidelines for conducting FVA analysis – to identify and eliminate the waste, inefficiency, and worst practices from your forecasting process. The result can be better forecasts, with fewer resources and less management time spent on forecasting.
View outline
Sunday, Oct. 7; 1-5 p.m.

SAS Enterprise Content Categorization: An Introduction

This course gives an introduction to methods of unstructured data analysis, document classification and document content identification. The course also uses examples as the basis for constructing parse expressions and resulting entities.
View outline
Sunday, Oct. 7; 1-5 p.m.

Introduction to Data Mining and SAS Enterprise Miner

This course serves as an introduction to data mining and SAS Enterprise Miner for Desktop software. It is designed for data analysts and qualitative experts as well as those with less of a technical background who want a general understanding of data mining.
View outline
Sunday, Oct. 7, 1-5 p.m.

Modeling Trend, Cycles, and Seasonality in Time Series Data Using PROC UCM

This half-day lecture teaches students how to model, interpret, and predict time series data using UCMs. The UCM procedure analyzes and forecasts equally spaced univariate time series data using the unobserved components models (UCM). This course is designed for business analysts who want to analyze time series data to uncover patterns such as trend, seasonal effects, and cycles using the latest techniques.
View outline
Sunday, Oct. 7, 1-5 p.m.

SAS Rapid Predictive Modeler

This seminar will provide a brief introduction to the use of SAS Enterprise Guide for graphical and data analysis. However, the focus will be on using SAS Enterprise Guide and SAS Enterprise Miner along with the Rapid Predictive Modeling component to build predictive models. Predictive modeling will be introduced using the SEMMA process developed with the introduction of SAS Enterprise Miner. Several examples will be used to illustrate the use of the Rapid Predictive Modeling component, and interpretations of the model results will be provided.
View outline
Sunday, Oct. 7, 1-5 p.m

Talk on Big Data Analytics

My talk on Big Data Analytics using mostly R went off peacefully.

Here are the slides

Talking on Big Data Analytics

I am going  being sponsored to a Government of India sponsored talk on Big Data Analytics at Bangalore on Friday the 13 th of July. If you are in Bangalore, India you may drop in for a dekko. Schedule and Abstracts (i am on page 7 out 9) .

Your tax payer money is hard at work- (hassi majak only if you are a desi. hassi to fassi.)

13 July 2012 (9.30 – 11.00 & 11.30 – 1.00)
Big Data Big Analytics
The talk will showcase using open source technologies in statistical computing for big data, namely the R programming language and its use cases in big data analysis. It will review case studies using the Amazon Cloud, custom packages in R for Big Data, tools like Revolution Analytics RevoScaleR package, as well as the newly launched SAP Hana used with R. We will also review Oracle R Enterprise. In addition we will show some case studies using BigML.com (using Clojure) , and approaches using PiCloud. In addition it will showcase some of Google APIs for Big Data Analysis.

Lastly we will talk on social media analysis ,national security use cases (i.e. cyber war) and privacy hazards of big data analytics.

Schedule

View more presentations from Ajay Ohri.
Abstracts

View more documents from Ajay Ohri.

 

Timo Elliott on 2012

Continuing the DecisionStats series on  trends for 2012, Timo Elliott , Technology Evangelist  at SAP Business Objects, looks at the predictions he made in the beginning of  2011 and follows up with the things that surprised him in 2011, and what he foresees in 2012.

You can read last year’s predictions by Mr Elliott at http://www.decisionstats.com/brief-interview-timo-elliott/

Timo- Here are my comments on the “top three analytics trends” predictions I made last year:

(1) Analytics, reinvented. New DW techniques make it possible to do sub-second, interactive analytics directly against row-level operational data. Now BI processes and interfaces need to be rethought and redesigned to make best use of this — notably by blurring the distinctions between the “design” and “consumption” phases of BI.

I spent most of 2011 talking about this theme at various conferences: how existing BI technology israpidly becoming obsolete and how the changes are akin to the move from film to digital photography. Technology that has been around for many years (in-memory, column stores, datawarehouse appliances, etc.) came together to create exciting new opportunities and even generally-skeptical industry analysts put out press releases such as “Gartner Says Data Warehousing Reaching Its Most Significant Inflection Point Since Its Inception.” Some of the smaller BI vendors had been pushing in-memory analytics for years, but the general market started paying more attention when megavendors like SAP started painting a long-term vision of in-memory becoming a core platform for applications, not just analytics. Database leader Oracle was forced to upgrade their in-memory messaging from “It’s a complete fantasy” to “we have that too”.

(2) Corporate and personal BI come together. The ability to mix corporate and personal data for quick, pragmatic analysis is a common business need. The typical solution to the problem — extracting and combining the data into a local data store (either Excel or a departmental data mart) — pleases users, but introduces duplication and extra costs and makes a mockery of information governance. 2011 will see the rise of systems that let individuals and departments load their data into personal spaces in the corporate environment, allowing pragmatic analytic flexibility without compromising security and governance.

The number of departmental “data discovery” initiatives continued to rise through 2011, but new tools do make it easier for business people to upload and manipulate their own information while using the corporate standards. 2012 will see more development of “enterprise data discovery” interfaces for casual users.

(3) The next generation of business applications. Where are the business applications designed to support what people really do all day, such as implementing this year’s strategy, launching new products, or acquiring another company? 2011 will see the first prototypes of people-focused, flexible, information-centric, and collaborative applications, bringing together the best of business intelligence, “enterprise 2.0”, and existing operational applications.

2011 saw the rise of sophisticated, user-centric mobile applications that combine data from corporate systems with GPS mapping and the ability to “take action”, such as mobile medical analytics for doctors or mobile beauty advisor applications, and collaborative BI started becoming a standard part of enterprise platforms.

And one that should happen, but probably won’t: (4) Intelligence = Information + PEOPLE. Successful analytics isn’t about technology — it’s about people, process, and culture. The biggest trend in 2011 should be organizations spending the majority of their efforts on user adoption rather than technical implementation.

Unsurprisingly, there was still high demand for presentations on why BI projects fail and how to implement BI competency centers.  The new architectures probably resulted in even more emphasis on technology than ever, while business peoples’ expectations skyrocketed, fueled by advances in the consumer world. The result was probably even more dissatisfaction in the past, but the benefits of the new architectures should start becoming clearer during 2012.

What surprised me the most:

The rapid rise of Hadoop / NoSQL. The potentials of the technology have always been impressive, but I was surprised just how quickly these technology has been used to address real-life business problems (beyond the “big web” vendors where it originated), and how quickly it is becoming part of mainstream enterprise analytic architectures (e.g. Sybase IQ 15.4 includes native MapReduce APIs, Hadoop integration and federation, etc.)

Prediction for 2012:

As I sat down to gather my thoughts about BI in 2012, I quickly came up with the same long laundry list of BI topics as everybody else: in-memory, mobile, predictive, social, collaborative decision-making, data discovery, real-time, etc. etc.  All of these things are clearly important, and where going to continue to see great improvements this year. But I think that the real “next big thing” in BI is what I’m seeing when I talk to customers: they’re using these new opportunities not only to “improve analytics” but also fundamentally rethink some of their key business processes.

Instead of analytics being something that is used to monitor and eventually improve a business process, analytics is becoming a more fundamental part of the business process itself. One example is a large telco company that has transformed the way they attract customers. Instead of laboriously creating a range of rate plans, promoting them, and analyzing the results, they now use analytics to automatically create hundreds of more complex, personalized rate plans. They then throw them out into the market, monitor in real time, and quickly cull any that aren’t successful. It’s a way of doing business that would have been inconceivable in the past, and a lot more common in the future.

 

About

 

Timo Elliott

Timo Elliott is a 20-year veteran of SAP BusinessObjects, and has spent the last quarter-century working with customers around the world on information strategy.

He works closely with SAP research and innovation centers around the world to evangelize new technology prototypes.

His popular Business Analytics blog tracks innovation in analytics and social media, including topics such as augmented corporate reality, collaborative decision-making, and social network analysis.

His PowerPoint Twitter Tools lets presenters see and react to tweets in real time, embedded directly within their slides.

A popular and engaging speaker, Elliott presents regularly to IT and business audiences at international conferences, on subjects such as why BI projects fail and what to do about it, and the intersection of BI and enterprise 2.0.

Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England

Timo can be contacted via Twitter at https://twitter.com/timoelliott

 Part 1 of this series was from James Kobielus, Forrestor at http://www.decisionstats.com/jim-kobielus-on-2012/

Google Docs Presentations gets a makeover but..

Maybe they should get design tips from Snooki!

Interview Dan Steinberg Founder Salford Systems

Here is an interview with Dan Steinberg, Founder and President of Salford Systems (http://www.salford-systems.com/ )

Ajay- Describe your journey from academia to technology entrepreneurship. What are the key milestones or turning points that you remember.

 Dan- When I was in graduate school studying econometrics at Harvard,  a number of distinguished professors at Harvard (and MIT) were actively involved in substantial real world activities.  Professors that I interacted with, or studied with, or whose software I used became involved in the creation of such companies as Sun Microsystems, Data Resources, Inc. or were heavily involved in business consulting through their own companies or other influential consultants.  Some not involved in private sector consulting took on substantial roles in government such as membership on the President’s Council of Economic Advisors. The atmosphere was one that encouraged free movement between academia and the private sector so the idea of forming a consulting and software company was quite natural and did not seem in any way inconsistent with being devoted to the advancement of science.

 Ajay- What are the latest products by Salford Systems? Any future product plans or modification to work on Big Data analytics, mobile computing and cloud computing.

 Dan- Our central set of data mining technologies are CART, MARS, TreeNet, RandomForests, and PRIM, and we have always maintained feature rich logistic regression and linear regression modules. In our latest release scheduled for January 2012 we will be including a new data mining approach to linear and logistic regression allowing for the rapid processing of massive numbers of predictors (e.g., one million columns), with powerful predictor selection and coefficient shrinkage. The new methods allow not only classic techniques such as ridge and lasso regression, but also sub-lasso model sizes. Clear tradeoff diagrams between model complexity (number of predictors) and predictive accuracy allow the modeler to select an ideal balance suitable for their requirements.

The new version of our data mining suite, Salford Predictive Modeler (SPM), also includes two important extensions to the boosted tree technology at the heart of TreeNet.  The first, Importance Sampled learning Ensembles (ISLE), is used for the compression of TreeNet tree ensembles. Starting with, say, a 1,000 tree ensemble, the ISLE compression might well reduce this down to 200 reweighted trees. Such compression will be valuable when models need to be executed in real time. The compression rate is always under the modeler’s control, meaning that if a deployed model may only contain, say, 30 trees, then the compression will deliver an optimal 30-tree weighted ensemble. Needless to say, compression of tree ensembles should be expected to be lossy and how much accuracy is lost when extreme compression is desired will vary from case to case. Prior to ISLE, practitioners have simply truncated the ensemble to the maximum allowable size.  The new methodology will substantially outperform truncation.

The second major advance is RULEFIT, a rule extraction engine that starts with a TreeNet model and decomposes it into the most interesting and predictive rules. RULEFIT is also a tree ensemble post-processor and offers the possibility of improving on the original TreeNet predictive performance. One can think of the rule extraction as an alternative way to explain and interpret an otherwise complex multi-tree model. The rules extracted are similar conceptually to the terminal nodes of a CART tree but the various rules will not refer to mutually exclusive regions of the data.

 Ajay- You have led teams that have won multiple data mining competitions. What are some of your favorite techniques or approaches to a data mining problem.

 Dan- We only enter competitions involving problems for which our technology is suitable, generally, classification and regression. In these areas, we are  partial to TreeNet because it is such a capable and robust learning machine. However, we always find great value in analyzing many aspects of a data set with CART, especially when we require a compact and easy to understand story about the data. CART is exceptionally well suited to the discovery of errors in data, often revealing errors created by the competition organizers themselves. More than once, our reports of data problems have been responsible for the competition organizer’s decision to issue a corrected version of the data and we have been the only group to discover the problem.

In general, tackling a data mining competition is no different than tackling any analytical challenge. You must start with a solid conceptual grasp of the problem and the actual objectives, and the nature and limitations of the data. Following that comes feature extraction, the selection of a modeling strategy (or strategies), and then extensive experimentation to learn what works best.

 Ajay- I know you have created your own software. But are there other software that you use or liked to use?

 Dan- For analytics we frequently test open source software to make sure that our tools will in fact deliver the superior performance we advertise. In general, if a problem clearly requires technology other than that offered by Salford, we advise clients to seek other consultants expert in that other technology.

 Ajay- Your software is installed at 3500 sites including 400 universities as per http://www.salford-systems.com/company/aboutus/index.html What is the key to managing and keeping so many customers happy?

 Dan- First, we have taken great pains to make our software reliable and we make every effort  to avoid problems related to bugs.  Our testing procedures are extensive and we have experts dedicated to stress-testing software . Second, our interface is designed to be natural, intuitive, and easy to use, so the challenges to the new user are minimized. Also, clear documentation, help files, and training videos round out how we allow the user to look after themselves. Should a client need to contact us we try to achieve 24-hour turn around on tech support issues and monitor all tech support activity to ensure timeliness, accuracy, and helpfulness of our responses. WebEx/GotoMeeting and other internet based contact permit real time interaction.

 Ajay- What do you do to relax and unwind?

 Dan- I am in the gym almost every day combining weight and cardio training. No matter how tired I am before the workout I always come out energized so locating a good gym during my extensive travels is a must. I am also actively learning Portuguese so I look to watch a Brazilian TV show or Portuguese dubbed movie when I have time; I almost never watch any form of video unless it is available in Portuguese.

 Biography-

http://www.salford-systems.com/blog/dan-steinberg.html

Dan Steinberg, President and Founder of Salford Systems, is a well-respected member of the statistics and econometrics communities. In 1992, he developed the first PC-based implementation of the original CART procedure, working in concert with Leo Breiman, Richard Olshen, Charles Stone and Jerome Friedman. In addition, he has provided consulting services on a number of biomedical and market research projects, which have sparked further innovations in the CART program and methodology.

Dr. Steinberg received his Ph.D. in Economics from Harvard University, and has given full day presentations on data mining for the American Marketing Association, the Direct Marketing Association and the American Statistical Association. After earning a PhD in Econometrics at Harvard Steinberg began his professional career as a Member of the Technical Staff at Bell Labs, Murray Hill, and then as Assistant Professor of Economics at the University of California, San Diego. A book he co-authored on Classification and Regression Trees was awarded the 1999 Nikkei Quality Control Literature Prize in Japan for excellence in statistical literature promoting the improvement of industrial quality control and management.

His consulting experience at Salford Systems has included complex modeling projects for major banks worldwide, including Citibank, Chase, American Express, Credit Suisse, and has included projects in Europe, Australia, New Zealand, Malaysia, Korea, Japan and Brazil. Steinberg led the teams that won first place awards in the KDDCup 2000, and the 2002 Duke/TeraData Churn modeling competition, and the teams that won awards in the PAKDD competitions of 2006 and 2007. He has published papers in economics, econometrics, computer science journals, and contributes actively to the ongoing research and development at Salford.

Interview Jaime Fitzgerald President Fitzgerald Analytics

Here is an interview with noted analytics expert Jaime Fitzgerald, of Fitzgerald Analytics.

Ajay-Describe your career journey from being a Harvard economist to being a text analytics thought leader.

 Jaime- I was attracted to economics because of the logic, the structured and systematic approach to understanding the world and to solving problems. In retrospect, this is the same passion for logic in problem solving that drives my business today.

About 15 years ago, I began working in consulting and initially took a traditional career path. I worked for well-known strategy consulting firms including First Manhattan Consulting Group, Novantas LLC, Braun Consulting, and for the former Japan-focused division of Deloitte Consulting, which had spun off as an independent entity. I was the only person in their New York City office for whom Japanese was not the first language.

While I enjoyed traditional consulting, I was especially passionate about the role of data, analytics, and process improvement. In traditional strategy consulting, these are important factors, but I had a vision for a “next generation” approach to strategy consulting that would be more transparent, more robust, and more focused on the role that information, analysis, and process plays in improving business results. I often explain that while my firm is “not your father’s consulting model,” we have incorporated key best practices from traditional consulting, and combined them with an approach that is more data-centric, technology-centric, and process-centric.

At the most fundamental level, I was compelled to found Fitzgerald Analytics more than six years ago by my passion for the role information plays in improving results, and ultimately improving lives. In my vision, data is an asset waiting to be transformed into results, including profit as well as other results that matter deeply to people. For example,one of the most fulfilling aspects of our work at Fitzgerald Analytics is our support of non-profits and social entrepreneurs, who we help increase their scale and their success in achieving their goals.

Ajay- How would you describe analytics as a career option to future students. What do you think are the most essential qualities an analytics career requires.

Jaime- My belief is that analytics will be a major driver of job-growth and career growth for decades. We are just beginning to unlock the full potential of analytics, and already the demand for analytic talent far exceeds the supply.

To succeed in analytics, the most important quality is logic. Many people believe that math or statistical skills are the most important quality, but in my experience, the most essential trait is what I call “ThoughtStyle” — critical thinking, logic, an ability to break down a problem into components, into sub-parts.

Ajay -What are your favorite techniques and methodologies in text analytics. How do you see social media and Big Data analytics as components of text analytics

 Jaime-We do a lot of work for our clients measuring Customer Experience, by which I mean the experience customers have when interacting with our clients. For example, we helped a major brokerage firm to measure 12 key “Moments that Matter,” including the operational aspects of customer service, customer satisfaction and sentiment, and ultimately customer behavior. Clients care about this a lot, because customer experience drives customer loyalty, which in turn drives customer behavior, customer loyalty, and customer profitability.

Text analytics plays a key role in these projects because much of our data on customer sentiment comes via unstructured text data. For example, we have access to call center transcripts and notes, to survey responses, and to social media comments.

We use a variety of methods, some of which I’m not in a position to describe in great detail. But at a high level, I would say that our favorite text analytics methodologies are “hybrid solutions” which use a two-step process to answer key questions for clients:

Step 1: convert unstructured data into key categorical variables (for example, using contextual analysis to flag users who are critical vs. neutral vs. advocates)

Step 2: linking sentiment categories to customer behavior and profitability (for example, linking customer advocacy and loyalty with customer profits as well as referral volume, to define the ROI that clients accrue for customer satisfaction improvements)

Ajay- Describe your consulting company- Fitzgerald Analytics and some of the work that you have been engaged in.

 Jaime- Our mission is to “illuminate reality” using data and to convert Data to Dollars for our clients. We have a track record of doing this well, with concrete and measurable results in the millions of dollars. As a result, 100% of our clients have engaged us for more than one project: a 100% client loyalty rate.

Our specialties–and most frequent projects–include customer profitability management projects, customer segmentation, customer experience management, balanced scorecards, and predictive analytics. We are often engaged to address high-stakes analytic questions, including issues that help to set long-term strategy. In other cases, clients hire us to help them build their internal capabilities. We have helped build several brand new analytic teams for clients, which continue to generate millions of dollars of profits with their fact-based recommendations.

Our methodology is based on Steven Covey’s principle: “begin with the end in mind,” the concept of starting with the client’s goal and working backwards from there. I often explain that our methods are what you would have gotten if Steven Covey had been a data analyst…we are applying his principles to the world of data analytics.

Ajay- Analytics requires more and more data while privacy requires the least possible data. What do you think are the guidelines that need to be built in sharing internet browsing and user activity data and do we need regulations just like we do for sharing financial data.

 Jaime- Great question. This is an essential challenge of the big data era. My perspective is that firms who depend on user data for their analysis need to take responsibility for protecting privacy by using data management best practices. Best practices to adequately “mask” or remove private data exist…the problem is that these best practices are often not applied. For example, Facebook’s practice of sharing unique user IDs with third-party application companies has generated a lot of criticism, and could have been avoided by applying data management best practices which are well known among the data management community.

If I were able to influence public policy, my recommendation would be to adopt a core set of simple but powerful data management standards that would protect consumers from perhaps 95% of the privacy risks they face today. The number one standard would be to prohibit sharing of static, personally identifiable user IDs between companies in a manner that creates “privacy risk.” Companies can track unique customers without using a static ID…they need to step up and do that.

Ajay- What are your favorite text analytics software that you like to work with.

 Jaime- Because much of our work in deeply embedded into client operations and systems, we often use the software our clients already prefer. We avoid recommending specific vendors unless our client requests it. In tandem with our clients and alliance partners, we have particular respect for Autonomy, Open Text, Clarabridge, and Attensity.

Biography-

http://www.fitzgerald-analytics.com/jaime_fitzgerald.html

The Founder and President of Fitzgerald Analytics, Jaime has developed a distinctively quantitative, fact-based, and transparent approach to solving high stakes problems and improving results.  His approach enables translation of Data to Dollars™ using methodologies clients can repeat again and again.  He is equally passionate about the “human side of the equation,” and is known for his ability to link the human and the quantitative, both of which are needed to achieve optimal results.

Experience: During more than 15 years serving clients as a management strategy consultant, Jaime has focused on customer experience and loyalty, customer profitability, technology strategy, information management, and business process improvement.  Jaime has advised market-leading banks, retailers, manufacturers, media companies, and non-profit organizations in the United States, Canada, and Singapore, combining strategic analysis with hands-on implementation of technology and operations enhancements.

Career History: Jaime began his career at First Manhattan Consulting Group, specialists in financial services, and was later a Co-Founder at Novantas, the strategy consultancy based in New York City.  Jaime was also a Manager for Braun Consulting, now part of Fair Isaac Corporation, and for Japan-based Abeam Consulting, now part of NEC.

Background: Jaime is a graduate of Harvard University with a B.A. in Economics.  He is passionate and supportive of innovative non-profit organizations, their effectiveness, and the benefits they bring to our society.

Upcoming Speaking Engagements:   Jaime is a frequent speaker on analytics, information management strategy, and data-driven profit improvement.  He recently gave keynote presentations on Analytics in Financial Services for The Data Warehousing Institute, the New York Technology Council, and the Oracle Financial Services Industry User Group. A list of Jaime’s most interesting presentations on analyticscan be found here.

He will be presenting a client case study this fall at Text Analytics World re:   “New Insights from ‘Big Legacy Data’: The Role of Text Analytics” 

Connecting with Jaime:  Jaime can be found at Linkedin,  and Twitter.  He edits the Fitzgerald Analytics Blog.

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