Memo_er

Hi all, thanks for checking out Memo_er! Here's a little about why I wrote Memo_er, a self-service data analysis tool, and what it can do. I wrote Memo_er because I was frustrated by trying to make dashboards like Tableau and Looker work for self-service data analysis*. Self-service analysis is the key to scaling the impact of data science, but the tools seemed a poor fit for the job. It hit me that the problem was that whenever I did a data analysis, I would never produce anything resembling a "dashboard". I would write a memo, explaining what assumptions I made, why I made them, what data I used, what the statistical findings meant for the business, and so on. The user of an automated analysis should get the same output as a data scientist would produce manually. Self-service data analysis should be data analysis. I also found SQL to be a poor choice for most analyses I wanted to automate, and the pricing for most dashboarding products also seemed a tad... excessive. I made Memo_er to enable data scientists to write automated memos in Python (not SQL) and any product/marketing/etc team to produce rich analysis for a reasonable, flat monthly fee — not an ever-growing per-seat charge. I hope you find it useful. Looking forward to any feedback you have! Memo_er's mission is to provide a flexible, affordable tool for self-serve data analysis that focuses on decision-making not slide-making. Happy Memo_ing! After signing up, you can use a free demo "Topic" (what Memo_er calls an automated analyses) to see how it all works. You don't pay for an account, you only pay for the servers to automate your memo (you don't have to pay for one to run the demo) and everyone in the same workspace can use the same server, i.e. no per-seat pricing. It's the same price whether you have 1 user or 10,000. * Self-service data analysis is when a data scientist/analyst sets up an analysis, but product/marketing/etc provide inputs to get to the final results of the analysis in an automated way (simple example: a team wants to look at the change in revenue over time for different user subsets. The data person sets up an automated system that can do a large class of cuts of the user base and the team just provides whatever cuts they want as needed instead of needing to go back to the data team to get each cut separately).