Industry Track Review Criteria

I. Clarity of problem statement (Examples:)

• Is the problem to be solved well defined?

• Are the data sources/format/structure clear to the reader?

• Is the system overall expected input/output clear?

II. Methodology (Examples:)

• Is the data science/data engineering methodology sound from a foundational point of view?

• Is it clear what are the parts that come from state-of-the-art/state-of-the-practice vs. any IPR that is contributed by organization (aka technical delta);

• How you allocated resources to execute this project?

• How did you managed risk (i.e. of running this project forever vs. having the minimum accuracy/reliability for a MVP release)?

• How did you timeboxed the different tasks/stages of the project?

• How did you prioritized the validation of different hypothesis?

• Why did you choose this method instead of others?

Note: Novelty is not a pre-requisite nor an evaluation criteria in this track

III. Business Impact (Examples:)

• How this project affected your company?

• Did this project affected positively your company’s bottomline? How much?

• How did you measured this outcome? (aka causality)

• Did this project affect the way you organize your work or your teams?

• If you asked your company’s CEO to describe this project in 5 words, which words he/she would use?

IV. Lesson's learned (Examples:)

• If you needed to redo this project again, what you would change AND what you would do again?