October 8, 2019• 2 minute read
Since its inception, Mode has been for data scientists and analysts. As former data scientists, my cofounders and I started Mode with the intention of building the product we wanted for ourselves. The vision expanded, but the analyst-first philosophy remained. “By analysts, for analysts” became Mode’s informal tagline.
September 17, 2019• 4 minute read
Co-founder & Chief Data Engineer
Lars Kamp is the Co-Founder & Chief Data Engineer at intermix.io where he helps customers optimize Amazon Redshift and their analytics queries for peak performance. We’ve invited intermix.io to share a guest piece detailing the new data stack.
May 2, 2019• 3 minute read
Data Scientist at Mode
A data model defines how an organization's various data sources are organized, related, and accessed. As data sources are added and complexity grows, the data model makes a big impact on the productivity and efficiency of anyone in the company that works with data.
March 27, 2019• 2 minute read
Senior Customer Success Engineer
Today, we're announcing the newly-designed Mode developer site. The new site contains extensive documentation of commonly used API endpoints, plus a few new ones. We've also added an API cookbook give you templates for common tasks. These new resources make it easier than ever before to build with Mode.
March 5, 2019• 2 minute read
As analysts and data scientists, we have to make our work compelling and persuasive to change the way our organizations think. It can be easy to assume that our well-constructed graphs and tables are plenty convincing on their own. But this ignores a fundamental fact of analytics: it's still a human making the decision behind those numbers, and that person is rarely as quantitatively inclined as we are. Persuading this audience takes a skill that isn't discussed enough in the data science community: empathy.
February 14, 2019• 2 minute read
A hands-off approach would seem reckless for questions about things like security. And yet that approach is not just the norm for analytical questions in most organizations; it's often the ideal. Many analytics teams aspire to enable as much “self-serve” as possible - in other words, to remove themselves from as many decision-making processes as they can.