Predictive Analytics Innovation Summit, San Diego: Day 2 Highlights

Highlights from the presentations by Predictive Analytics leaders from eBay, LinkedIn and Facebook on day 2 of Predictive Analytics Innovation Summit 2015 in San Diego.

IE AnalyticsPredictive Analytics Innovation Summit was held by Innovation Enterprise in San Diego on Feb 12-13, 2015. It provided a platform for leading executives to share interesting insights into the innovations that are driving success in the world's most successful organizations. Data scientists as well as decision makers from a number of companies came together to learn practical predictive analytics, data science and business intelligence from top companies like Google, Sony, Walmart, Facebook, Twitter, etc. Industry leading experts shared case studies and examples to illustrate how they are using and improving predictive models to innovate in their organization.

Highlights from day 1

Here are highlights from day 2:

Ian Zhao, Director, Compensation Market Analytics, eBay talked about how to lead a successful predictive analytics project. Analytics is a widening field in non-marketing areas. Predictive analytics in non-marketing areas tends to have less data, more scattered data sources, fuzzier objectives, shorter time expectancy to insights and team leaders have more executive exposure. Different environments call for different approaches to problems. He shared the following success factors:
  1. Beware of different analytical methods: Problems can be resolved with various methods. Team should plan for contingencies before finding the default approach is not working.
  2. Access three types of talent: Business Consultants (for domain understanding), Data Scientists (to test hypotheses) and IT Staff (to access data).
  3. Employ lean analytics method: Identify the most important measure for business, establish a minimum viable model and modify it based on feedbacks. Frequent milestones and status updates are very important.
  4. Leave ample time for data preparation: Always start with data exploration and confirm data availability for hypotheses. Also, it is critical to be prepared for imputing data and validating summary statistics with business community.
  5. Dedicate time/resource for communication: Try to analyze and gain knowledge once reached insights. Always keep communicating with executives at a higher level summarizing the results. Storytelling is a skill that helps people understand statistics in a much better and memorable fashion.