About ​Patrick Williams
& AnYong Analytics 
An Yong (安勇)
is my adopted daughter's
Chinese name, meaning
"Peace" and "Courage"

The Story

Hi. I'm Pat Williams, President of AnYong Analytics, LLC. I hope my firm can help yours by providing the highest quality, world-class analytics services for your Data Science initiatives. After retiring as a Data Scientist from SAS® Inc.'s Advanced Analytics Lab in 2018, I looked back on my 31-year career of modeling and analytics experience and thought, "Heck, maybe I have a little more." Why? Because Data Science can actually be both fun and valuable, even after 15 years at SAS® and 16 years prior to that! So I started AnYong Analytics.

I bring deep and broad experience, with extensive work in modeling & analytics, big data, related system implementation and integration, project management and modeling & analytics education:

  • Performing, managing, implementing, and teaching modeling & analytics in support of planning, marketing, merchandising, regulatory, engineering and other functions;

  • Spanning retail, financial services, government, telecommunications, leisure, health care and other industries;

  • Exposure to a wide array of organizations, modeling & analytics techniques, data sources, software, hardware platforms and project teams across the globe;

  • General focus on predictive modeling and analytics, with application to marketing automation and optimization, revenue/pricing optimization, corporate planning and regulatory, anti-fraud, exploratory analysis, data visualization and “big data”.

Now I certainly don't know everything about Data Science. But I've been around the horn a few times. Below are some of the valuable and fun things I have been able to do or be part of, experience which adds significant value to AnYong Analytics services. 

A Boost for Retailers

 

In July 2020, in just 12 weeks, I finished off the initial Python product development code for The Parker Avery Group's Markdown Analytics Intelligence solution. The Parker Avery Group is a sharp retail consulting firm in Georgia, and it was an outstanding contracting experience.

 

The optimization code itself was already written by some really smart folks there, but it was a blast integrating that code into a coherent business process, and designing an open-source database to store optimization input and output data.

 

Once the client data were sourced, I wrote ETL process code to load the database. Then I wrote the on-going production process code to extract input data for the Markdown Optimization process, grab the optimized price recommendations, and report them, along with a host of other weekly business decision metrics.

Along the way, I was able to collaborate with a great team of retail, math and Python gurus. You know, teamwork.

And the job's not finished until the documentation is done, which I also wrote for this product.

 

Surely, this product will be able to help many retailers as they emerge from this pandemic era. (See https://www.parkeravery.com/parker-avery-group-markdown-intelligence.html)

(My interest in and learning of both Python and R stemmed from my prior work at SAS®, when they began integrating open-source code into their platform.)

Analytics Consulting:
Modeling & So Much More

 

Before SAS® Education, I worked in both Global Retail Consulting and Marketing Automation Pre-Sales, which was my SAS® entry point in 2003 as a Principal Consultant. Prior to joining SAS®, I had my own firm, FulCRM Consulting, and also worked for Protagona, a marketing automation software firm out of the UK, also as a Principal Consultant.

While all these roles involved the building of client predictive models and their related analytics, the scope of work went well beyond those tasks to include:

  • Integration of modeling & analytics into business decision making;

  • Implementation of software and business processes;

  • Management of projects, including project plans, contracts, statements of work, project workshops, etc.

  • High levels of client interaction and relationship building in sales support, workshops, implementation and education.

Here are some highlights ...

In SAS® Global Retail Consulting, demonstrated expansive vision in the integration of analytic processes into platform solutions:

In SAS® Marketing Automation Pre-Sales:

  • Managed software implementation engagements and authored project documents (project plans, statements of work, etc.)

  • Installed and configured new software versions of SAS® Marketing Automation before release to pre-sales, for functionality testing, benchmarking, bug identification, data structure and model creation for demos, etc.

  • Spearheaded training of client and internal staff on newly-released SAS® Marketing Optimization, including:

    • Installation, configuration and use

    • Data integration with SAS® Marketing Automation

    • Technical and analytical “deep dives”

At FulCRM Consulting (my firm):

  • Consulted on, managed and implemented Customer Relationship Management (CRM) projects;

  • Built open source campaign databases, models and related analytics;

  • Executed two non-partisan judicial campaigns.


At Protagona:

  • Implemented Protagona Campaign Management software for large financial services and leisure clients; 

  • Trained clients on the use of the software and the development of predictive customer targeting and segmentation models within the software;

  • Exploited software’s agent tool to launch SAS® model code to score customer universe prior to campaign cell creation; 

  • Managed implementations and authored project documents (project plans, statements of work, etc.)

Other Stuff

 

Milwaukee Area Technical College, Economics Instructor (part time)

  • Instructed undergraduate students in macroeconomics and money & banking;

  • Promoted the intensive use of online chapter reviews and practice exams, which were very popular with students.

 

Executed winning non-partisan judicial campaign for Wisconsin's first African-American judge to be elected without prior appointment from the Governor.


United States Air Force and Wisconsin National Guard 1977-1984

  • Weapons Mechanic – Loaded conventional and nuclear weapons on aircraft, armed and launched aircraft, and maintained aircraft weapons systems. Was chief of weapons load crew in which all crew members were of higher rank. Received nuclear weapons security clearance.

  • Hydraulics Mechanic – Maintained aircraft hydraulics systems.

  • Honorably discharged at rank E-4 (Staff Sergeant)

Interests - Enjoy travel, walking (former runner), biking, kayaking, yoga, playing guitar, flying my drone and volunteering. Know some German and a smidge of Spanish and Mandarin Chinese.

And yes, I admit, the picture of me on this site is not quite 20 years old, but it does bear my likeness!

Smart & Sustainable

 

 

My last project before retiring from SAS® Inc.'s Advanced Analytics Lab was a smart campus sustainability project, in which I worked with an incredible SAS®/partner team, visualizing near-real-time HVAC, electric, gas, water, pollution and weather metrics from campus buildings in dashboards and detailed reports, and predicting electric power demand, using the SAS® Viya® platform. There was even a Python weather data feed!

 

Projects like this will help companies run leaner and cleaner, and I was proud to be part of it. 

 

(Hmm ... my last SAS® project involved modeling electric demand in SAS® Viya®, and my first intern project involved modeling the same thing in SAS® Version 5.)

Analytics Education: Instruction, Development & Implementation

 

Prior to the SAS® Advanced Analytics Lab, I worked SAS® Education's Global Retail Education division, developing SAS® modeling/analytics and technical courses, and teaching them to clients and staff around the globe, both in-person and via Adobe Connect® Live Web:

  • SAS® Revenue Optimization: Analytics and Modeling (Big Data Treaded Kernel Grid; taught to staff and clients in US, Canada (Toronto), UK (Marlow), India (Pune and Kolkata), and Finland (Helsinki); time series and mixed modeling of demand and demand share)

  • SAS® Visual Analytics: Fast Track and Getting Started (Big Data In-Memory Hadoop; made numerous "How To" videos for software tasks; started certification for administration courses)

  • Implementing SAS® Demand Forecasting for Retail

  • Using SAS® Demand Forecasting for Retail

  • Installing and Configuring Intelligent Clustering for SAS® Merchandise Planning

  • Technical Overview for SAS® Merchandise Planning

In addition to teaching and developing course material, I also staged all of the computing environments for all instructors/courses in my division. This involved:

  • Installing and configuring SAS® 9 platform solutions and third-party software (databases, middle ware, Java, Flash, etc.);

  • Staging single and mutli-machine grid environments, both virtual (VMware® on SAS® and Amazon® machines) and physical servers (Windows® and Linux®)

  • Sourcing and implementing custom data for customized training engagements;

  • Implementing and benchmark testing alternative architectures for best performance of training environments;

  • Significant interaction with clients, Tech Support, Professional Services and R&D to develop and deliver courses, customize data, etc.

Education

 

University of Wisconsin – Milwaukee, BA and MA, Economics 1980 – 1986

  • Course work included statistics, econometrics, mathematical economics and antitrust;

  • Master's Thesis entitled "The Efficiency Approach to Vertical Integration and Two Recent Policy Developments" (unpublished);

  • Highly-rated graduate teaching assistant in microeconomics, money and banking and antitrust;

  • Conducted very popular and intensive pre-exam review sessions, focusing on mathematical aspects of material to be tested.

Numerous other SAS® and non-SAS® courses related to analytics & modeling, implementation and system administration, including:

  • Programming (SAS®, Python, R, SQL) and modeling, (forecasting, regression, categorical modeling, clustering, neural networks, mixed modeling, machine learning, etc.);

  • Reporting and data visualization, platform and big data implementation and administration, etc.

Anti-Fraud & More

 

 

Also at the SAS® Advanced Analytics Lab, on both the SAS® Viya® and SAS® 9.4 platforms, I did lots of anti-fraud programming, data visualization and modeling in the areas of:

  • Health insurance claims (provider and prescription);

  • One state's worker's compensation and unemployment claims;

  • Another state's WIC program (Women, Infants and Children);

  • A third state's corporate tax fraud abatement program;

  • Traffic and member analysis for a mobile parking application;

  • Developed back-to-school and holiday campaign selection models for a major outer-wear brand;

 

My part in the corporate tax project involved reading into SAS® the metadata for numerous tax forms from a worksheet, then auto-generating SAS® code to create and index form-specific database tables. This was some of the most interesting ETL-related programming I had done to date, and I worked with an amazing team of SAS® and tax fraud experts.

(I admit, the campaign modeling project, done in SAS® Enterprise Miner®, was like "old home week" for me.)

Modeling & ETL:
The Heart of the Matter

 

 

My earlier career had a sharp focus on building predictive models and gathering the data underlying them. This included working for a mortgage insurer, a city government, and two utilities (telecommunications and electric).

At Mortgage Guaranty Insurance Corp. as Risk Management Manager:

  • Modeled mortgage loan portfolio risk for default, foreclosure and prepayment on internal and client bank loan portfolios;

  • Developed logistic and neural network qualitative outcome models using SAS® and Neuralware® software;

  • Sourced input data from internal databases and external electronic sources (e.g., U.S. Treasury and BLS, WEFA, DRI, etc.)

  • Wrote ETL processes to prep data for modeling and drop scores into database.

  • Deployed loan default models into Freddie Mac’s loan evaluation software.

  • Managed the development of:

    • Multi-bank loan database from which to develop client bank risk models.

    • Lender Landscape®, an interactive loan portfolio risk reporting system with SAS® back end.

  • Unix®, Windows® and IBM® mainframe TSO® environments.


At City of Milwaukee Budget Office as Urban Economist:

  • Responsible for state shared revenue budget and formula analysis, capital and debt budgets.

  • Integrated TSP® and SAS® forecasting into budget and shared revenue analysis processes.

  • ETL involved transcribing input data from paper historical budget documents.

  • Authored city’s public pricing user fee policy used as rationale for moving sewer and other funding from property tax to user fees.

  • Windows® and IBM® mainframe CMS® environments.


At Ameritech, Inc. (Regional Bell Operating Company):

  • As Campaign Analytics Manager:

    • Designed, modeled, executed and evaluated quarterly residential and business telecommunications service marketing, retention and win-back campaigns;

    • Campaign revenues ranged from $20 to $85 million;

    • Executed on large datasets for 15 million customers;

    • Worked heavily with SAS® logistic, neural network and tree models;

    • Advised marketing team on appropriateness of offers, cells, test cells, etc.;

    • More streamlined ETL processes, with internal data extracted from databases and electronic transfer of external data;

    • Windows®, Unix® and IBM® mainframe TSO® environments.

 

  • As Demand Analytics Economist:

    • Estimated demand, revenue, cost and class of service choice models for corporate planning and state and federal regulatory filings.

    • Composed testimony and performed rate impact analysis for models used in regulatory filings (state and federal);

    • Highly manual ETL processes form historical reports;

    • Team accurately forecasted local access minutes demand in the face of historic drops in long distance prices;

    • Team introduced the use of class of service choice models using logistic models;

    • Models included pooled cross-sectional time series using lagged dependent variables or polynomial distributed lags, and logistic (single and nested);

    • Used SAS® on IBM® mainframe TSO® environment and SHAZAM® on Windows®.


At We Energies as paid Graduate Summer Intern:

  • Developed SAS® Box-Jenkins demand models and related graphics for farm sector electricity demand (IBM® mainframe TSO® environment).

  • Extremely manual ETL processes, involving entering data from historical reports, accounting journals, etc.;

  • Developed Marginal cost calculations for generating, transmitting and distributing electricity (in IBM® Lotus Symphony® spreadsheet), using what was called the CGS method (see Cicchetti, Charles J., W. Gillen and P. Smolensky. The Marginal Cost and Pricing of Electricity: An Applied Approach. Ballinger Publishing Company, 1977.)