Every analyst is a finance analyst

The world for analysts is changing. Unlike ever before, every interaction with a customer is at their fingertips.

Only a decade ago, marketing took place primarily at events, on television, on radio, or in print advertising. Sales took place over steak dinners and cocktails. Product was hosted behind a customer’s firewall. Today these interactions are digital - occurring within blogs, email, webinars, social media, and in cloud hosted software.

With the advent of Redshift and ETL technologies, all of this data is being loaded, transformed, enriched and pulled nearly instantaneously. Accessibility has now enabled companies to understand their businesses across every function in a way unparalleled historically.

These tools and technologies make it easier and more important to answer questions that sit at the intersection of functions. As a result, analyst work is colliding (including for analysts within Biz Ops, Sales Ops, Marketing Ops). For example, product analysts are increasingly asked to quantify how engagement or new feature launches have impacted the funnel, pipeline, or retention. Marketing and sales analysts are increasingly asked to justify the outputs of their functions in terms of unit economics. Finance analysts today are expected to opine on marketing funnels, sales capacity plans, product metrics, engineering infrastructure, etc.

As a result, companies are grappling with how to best structure analysts: finance & analytics teams are being combined, sales and marketing operations are consolidated into revenue operations, bizops teams are exploding in popularity, and almost every company has lived the constant struggle of centralizing and decentralizing analysts. Ask leaders of these analytically oriented teams and they’ll tell you that they’re finding it significantly more difficult to define roles and responsibilities for analysts across departments.

And as much as the work of analysts across every department collides, they do so in service of one purpose: to quantify tradeoffs. And within a business, the fundamental way of quantifying any trade off is return on investment. Whether over the short term or long term, every business exists to yield a return on invested resources. Every decision implicitly has a different return, and the best analysis aims to bring those differences to the forefront. It’s no longer enough to quantify impact solely within your own domain (be it marketing, sales, etc.).

This has two major implications for analysts:

1. Every analyst is a finance analyst. At minimum, every analyst needs to understand and deeply internalize the fundamentals of how their business model works. In SaaS, that means deeply understanding the equation(s) leading to and the sensitivities behind both ARR and unit economics. A great primer is David Skok’s seminal piece on measuring SaaS.
   
2. Every analyst needs to learn to access a broad, horizontal set of data. More explicitly, every analyst should learn SQL because every technology business (past, present, and future) is built on relational databases. It’s the only way to access and deeply understand the breadth of data necessary to answer the questions asked of analysts today. SQL is now table stakes, and the secret - it’s actually not hard! It only takes a few weeks to learn.

As the work of analysts continues to overlap, analysts are becoming positionless. Reporting structure and functional domain expertise matter less than skills and abilities. If you’re a sport’s enthusiast like me - there’s a parallel to the NBA in its shift towards positionless basketball. What used to be a rigid set of positions and unique skills from point guard to center, instead has turned into an emphasis on two abilities: 3 point shooting and floor spacing. This has had meaningful implications on how championship teams are constructed and on the skills players choose to develop (never before did we see centers shooting 3s!). A similar shift is happening in the world of analysis, and the skill sets required to keep up are financial analysis and self sufficiency in accessing data.