Columbia Business School
Data Disruption Event

 

Disruptive Digital Transformation:

Wielding your data and analytics competency for digital transformation success

Data and analytics are the key accelerants of an organization’s digitization and transformation efforts. A digital transformation can help re-engineer, automate and infuse intelligence to add differentiated capabilities to organizations that not only reduce cost but add value. Data Science, AI, and Machine Learning sustain high levels of innovation and added value.  You will leave this session with a better understanding of the following:

 

• Major data and analytics trends in 2022.

• Best areas of the organization where a digital-first approach can provide the biggest impact.

• Quantitative intuition – making decisions in a data-driven world.

• The opportunities and challenges with AI in marketing, identity verification, and fraud prevention.

• Low-code platforms and their potential for enabling effective use of machine learning.

Questions, Please contact alan@cglead.org or 858-705-1482

 SPEAKERS

  • '18SPS, Executive Vice President and General Manager, Identify, Fraud, and DataLabs at Experian, Board Member, Board of Overseers, Columbia University School of Professional Studies (San Diego-based)

    Eric Haller is the Executive Vice President and Group Head of Experian DataLabs. He pitched the idea of the datalabs to Experian’s executive team and received funding for 8 people to start the first lab in 2011. Since then he’s led the development and expansion of Experian DataLabs. Experian DataLabs is the research and development organization within Experian. It was established in 2011 to drive the creation of new products and services from breakthrough experimentation across Experian’s businesses in 37 countries. Labs are located in London, Sao Paulo, Singapore and San Diego. Over 65% of these team members have their PhD in hard science areas. The labs have been featured in Harvard Business Review, Wall Street Journal, Bloomberg, Fox Business News, Fortune, Forbes, Inc., Cheddar TV and two published books covering innovation and artificial intelligence. They specialize in leveraging artificial intelligence and a broad array of data assets to solve a variety of challenges in risk, operations and marketing. Prior to Experian, Eric worked in several start-ups including co-founding a local company in San Diego that focused on detecting identity fraud. This business was later renamed ID Analytics and sold in 2012 to LifeLock for $185 million. Eric also held roles as Chief Marketing Officer for HNC Software (sold to FICO in 2002 for $800mm) and executive roles with MasterCard and Visa. Eric has his M.S. in Technology Management from Columbia University and B.S. in Finance from San Diego State University. He and one of the DataLabs are based in San Diego.

  • Arthur J. Samberg Professor of Business, Columbia Business School and sought-after advisor to global corporations on data-based decisions and extracting useful information from rich and thin data.

    Professor Netzer's expertise centers on one of the major business challenges of the data-rich environment: developing quantitative methods that leverage data to gain a deeper understanding of customer behavior and guide firms' decisions. He focuses primarily on building statistical and econometric models to measure consumer preferences and understand how customer choices change over time, and across contexts. Most notably, he has developed a framework for managing firms' customer bases through dynamic segmentation. More recently, his research focuses on leveraging text-mining techniques for business applications. Professor Netzer has won the Columbia Business School Dean’s Award for Teaching Excellence, and the Columbia University GSAC Faculty Mentoring Award to commemorate excellence in the mentoring of Ph.D. students. Professor Netzer frequently consult to Fortune 500 companies and entrepreneurial organization on strategy, data-driven decision making, marketing research and extracting useful information from rich and thin data.