- Powerful IT, and a new model for IT use
- Data critical mass: ERP, POS and web data
- Skills sufficiency, at least for the individual pieces
- Business need: a differentiated, personalized, well-informed easy use experience
He makes a distinction between reporting and analytics, and sees analytics as a way for companies to find the best customers and charging them the right price, or analyzing search logs to design a better website.
He sees the following attributes of a user-centric analytical strategy:
- Analytics are used to design and modify the customer experience
- Analytics are used to measure and maximize user engagement
- Analytics are used to determine what the user wants, and personalize to the user
- Analytics are employed across all customer channels
- Analytics are made available to the user
This includes companies such as Amazon and Netflix through their targeted recommendations, Google with customized ads and do-it-yourself analytics, eBay with behavioral targeting, Harrah’s and Marriott with loyalty programs, and Royal Bank of Canada and Capital One with targeted cross-selling across channels. Analytics can also be used for public service: New York City reduced crime through predictive analytics of where crimes were most likely to occur.
He stepped through what’s required for competition grade analytics: data (which he envisions in a data warehouse, counter to Weinberger’s arguments about the more serendipitous discovery of data), enterprise (widespread access to a centralized data store), leadership (executive commitment), targets (focusing analytic activity), and analysts (professional, semi-pro and amateurs, using all levels of analytical tools).
He finished up with the next steps for analytics, from the pursuit of new data types to search/BI combinations to better model management.