This is the initial installment of a weekly post on notable articles on the data-driven trend. Topics covered include self-service analytics, data literacy, democratization of data and analytics, and how these subjects foster insights, innovation, and operational excellence.

SaaS Metrics

Software as a service (SaaS) has changed the business game in many ways. Businesses succeed by becoming monopolies for a narrow vertical. The path to success requires understanding and quantifying a number of financial metrics, including revenue, LTV, and churn. Here’s what to read:

• InsightSquared provides a good primer of SaaS Finance KPIs. This is a good 5 minute starting point if you are new to this space.
• Creating financial models can be daunting, but usually they start from humble origins. I wrote an article on modeling MRR using Panoptez, a self-service analytics environment that runs inside Slack.
• Once you’ve knocked out these basic metrics, you might consider drilling into second-order metrics that will give you greater visibility into your financials. One example is quantifying SaaS pipeline metrics, which Nic Poulos of Bowery Capital says is a leading indicator of performance.
• Another path is to check the assumptions in your model. Jeff Bussgang is provocative by saying Your LTV Math Is Wrong, and he’s right on point. This is a must-read in developing your financial models.

Product Management

Here are two viewpoints of product management. The first discusses why product management is needed, and the second on the traits of a great PM. Look for overlap in traits.

• Over on Medium, Brandon Chu writes that PMs get sad when you ask them why they exist. The TL;DR is that PMs exist because businesses are more interconnected driven by speed and scale. Another way of saying this is that PMs are needed because decision-making demands around product development has become continuous. This is driven by 1) the trend towards continuous iteration and integration of software, and 2) increasing business process interdependence. This in turn, is driven by increasing data transparency within an organization.
• At Bubba VC, Bubba Muraka writes about three skills of a great PM. The thinking on this piece is quite sound. It’s also notable that being a “visionary” is not on the list, which implies that great PMs are pragmatic and data-driven as opposed to idealistic.

Tools and Technology

Some useful tools I’ve come across this past week.

• Check out Prof. Jenny Bryan’s reprex package for preparing reproducible examples in Markdown (in R)
• Ando Saabas discusses using prediction intervals with random forests (in Python)
• Robert Chang wrote Doing Data Science at Twitter over the summer. I recently came across this again, and it is still relevant. It’s a great read for anyone wanting to understand what a Data Scientist actually does (disclaimer: N=1).

That wraps up this week’s digest. Have any suggestions for other great data-driven content? Add your recommended reads in the comments.