Archive for the ‘Projects’ Category

I was contracted by the FICO product marketing department to review their long term use of artificial intelligence (AI) for fraud, cybersecurity and compliance (AML) solutions. All three of these areas utilize machine learning and AI for anomaly detection. The flagship of this portfolio is the Falcon Fraud Platform, and is used by ~ 10,000 financial institutions to risk score approximately 9,000 payment card transactions/sec globally. I had a blast digging into all that FICO was doing with AI, and I came up with a summary document “5 Keys to Successfully Applying Machine Learning and AI in Enterprise Fraud Detection.” Here is a list of the 5 elements I focused on for the project:

  • The role of supervised and unsupervised models in fraud detection (with a focus on behavior anomaly analytics)
  • The importance of large data sets in model development and training
  • What are predicting features and why is domain expertise necessary in their development
  • The benefits of Specialized vs. Generic models in enterprise fraud (i.e., importance of expert features)
  • The role of adaptive analytics and/or self-learning AI in enterprise fraud

You can download the report here: 5_Keys_Successfully_Applying_Machine_Learning_ AI

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I’m very pleased to announce the availability of a new special technology report that I was contracted to research, develop and write – “insideHPC Special Report: Riding the Wave of Machine Learning and Deep Learning” sponsored by Dell EMC and NVIDIA. insideHPC is the sister publication (specializing in high performance computing) to insideBIGDATA where I serve as Managing Editor.

The report focuses on how many companies are moving decisively to develop capabilities based on AI, machine learning and deep learning. In time-honored business fashion, the motivation is a combination of fear and hope. Competitive pressures are spurring companies on, and there is a sense of urgency among many enterprise thought leaders about not falling behind.

You can download a copy of the report HERE.

insidebigdata_guide_dl_aiI’m very pleased to announce the availability of a new technology guide that I was contracted to research, develop and write – “insideBIGDATA Guide to Artificial Intelligence & Deep Learning” sponsored by NVIDIA.

This guide to artificial intelligence explains the difference between AI, machine learning and deep learning, and examines the intersection of AI and HPC. The guide includes a special section highlighting the results of a new insideBIGDATA audience survey to get readers thoughts about how they see AI, machine learning and deep learning for their own companies. The guide provides some of the survey results including numeric results, data visualization, and interpretation of the results.

You can download a copy of the guide HERE.

Opera-Solutions-LogoI recently was awarded an extended contract with Opera Solutions to continue as an industry analyst and contributor for their blog SignalCentral: The Hottest Spot for Big Data Science. I will be writing on a broad range of topics including big data, data science, machine learning, AI, and deep learning. Opera Solutions is a global provider of advanced analytics software solutions that address the persistent problem of scaling Big Data analytics. The company has over 175 data scientists on staff. My original announcement was on March 31, 2014. I’m very pleased to continue my work with this leading consulting group.

Here is an on-going list of blog post written on behalf of Opera Solutions:

7 Steps to Prepare for Data Science Adoption

AI, Machine Learning, and Deep Learning Explained

Ensuring Predictive Analytics Success with Data Preparation & Quality

AI: Why Now? An Old Technology Grows Up Fast

 

 

insidebigdata_guide_healthcare_lifesciences_dell_emc_featureI’m excited to announce the availability of a new technology guide that I was contracted to research, develop and write – “insideBIGDATA Guide to Healthcare & Life Sciences” sponsored by Dell EMC.

The healthcare and life sciences industries historically have generated vast amounts of data. These large volumes of data hold the promise of supporting a wide range of medical and healthcare tasks, including clinical analytics and decision  support, patient profiling, disease surveillance, regulatory and compliance requirements, scientific research, and many  others. Data in healthcare and life sciences is expected to grow exponentially in the coming years and will be beyond the  capability of the traditional methods of data management and data analytics. This new guide details this upward trajectory for enterprise thought leaders.

You can download a copy of the guide HERE.

lafashion_paperOver the past year, I’ve been working on a new research project that culminated with a whitepaper in support of the Los Angeles fashion industry – “Los Angeles Fashion Industry: A Data Science Perspective.” The work was done in collaboration with LA’s premiere fashion mart, The New Mart. The paper is in response to numerous stories found in the local apparel industry trade press that held the sentiment that LA fashion had seen better days and was on a downward slide. We decided to use privately procured and publicly available data sets to find a single truth surrounding this matter. The whitepaper’s position is quite the opposite – LA fashion is a Los Angeles mainstay and continues on an upward trajectory. See for yourself by downloading the paper HERE.

 

Analytics Expert at Wikistrat

Posted: March 30, 2016 in Projects

I recently was appointed to the Analytic Community Experts group for crowd sourced consultancy Wikistrat. Based in Washington DC, Wikistrat operates a global network of over 2,000 subject-matter experts working collaboratively via an online platform to help decision-makers identify solutions to complex strategic challenges. Here, I will offer my insights in data science, machine learning and big data to assist in building the wisdom that is essential for dealing with an increasingly complex world.

Wikistrat