Since creating Insights @ Canopy Labs, we’ve been reminded by customers and potential recruits about the old articles we posted on the original Canopy Labs blog. To facilitate easy reading, below are the data-oriented articles that received the most attention over the past half year:

  • Analytics has a user experience problem Most analytics platforms are developed to solve as many data-related problems as possible, and are not catered to specific uses or solutions. This makes them difficult to use by non-statisticians, and hinders successful applications of analytic techniques. We outline three ways to overcome this challenge and make analytics more user friendly.
  • “Is accuracy important?” and other questions in enterprise analytics Using analytics in the enterprise brings with it unique challenges not present in more academic settings. Specifically, analytics becomes a strategic decision — a balancing of pros and cons, costs, and risks. Much of this is less dependent on model performance than internal corporate constraints and budgets. Data scientists take note.
  • Five data types often ignored by customer anlaytics teams If your company is using analytics to improve any aspects of its sales operations, success can mean the difference between using or not using a specific variable or data set within a model. We outline five types of data that are often ignored by companies running customer analytics projects, yet are likely to yield valuable insights and drive significant model performance.

Have other data-oriented questions? Let us know!