Sisu Data with Peter Bailis

A high volume of data can contain a high volume of useful information. That fact is well understood by the software world. Unfortunately, it is not a simple process to surface useful information from this high volume of data. A human analyst needs to understand the business, formulate a question, and determine what metrics could reveal the answer to such a question.

Sisu is a system for automatically surfacing insights from large data sets within companies. A user of Sisu can select a database column that they are interested in learning more about, and Sisu will automatically analyze the records in the database to look for trends and relationships between that column and the other columns. 

For example, if I have a database of user purchases, including how much money those users spent on each purchase, I can ask Sisu to analyze the purchase price column, and find what kinds of attributes correlate with a high purchase price. Perhaps there will be correlations such as age and city that I can use to understand my customers better. Sisu can automatically surface these correlations and display them to me to help me make business decisions.

Peter Bailis is the CEO of Sisu Data and an assistant professor at Stanford. Peter joins the show to give his perspective on the development of Sisu, which came out of his research on data-intensive systems, including MacroBase, an analytic monitoring engine that prioritizes human attention.

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Transcript

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