Data Tech is Necessary But Not Sufficient

Introducing the SaaS analytics playbook

We’ve seen an explosion of innovation in data technologies in the last decade. From the introduction of cloud data platforms like Snowflake, Databricks, BigQuery, etc. to a number of amazing tools like DuckDB we can now process terabytes of data in seconds.

We’ve seen an explosion in the amount of data stored. Many companies have hundreds of Terabytes of data with some even reaching the Petabyte level. But has any of this helped companies be more data driven?

Despite all these advances, many organizations still struggle to get value out of their data. According to DemandSage, 97% of organizations surveyed have invested in big data technologies. Yet only 24% have actually created a data driven organization.

How come?

Because data tech is not sufficient in making companies data driven.

Just because we’re:

• Collecting more data than ever before

• Storing it in efficient formats in large cloud data platforms

• Processing it faster than we ever did before and with free tools

• Ensuring its quality and timeliness with observability platforms

• Automatically refreshing our pipelines in minutes

• etc

…it doesn’t mean we’re data driven.

The missing methodology

In order to use data effectively you need to have a way to use it to improve the business. In order to improve a business you have to know how it works, right?

A business is like a factory designed to achieve a predetermined objective (like making money) while also providing value to its customers. Just like a factory it’s made up of interlinked parts that must work in concert to achieve that objective successfully.

Unlike a factory where you can easily see the big picture – you can see products flowing from one department to another – in a modern digital business you are blind. You have no idea how your daily actions and decisions impact the overall objective.

You have lots of metrics you report on (aka KPIs) but you have no idea how those metrics interact with each other to drive the overall objective. Many of these metric definitions are not standardized so what marketing considers “total leads” and what sales considers “total leads” are two different things.

The SaaS Analytics Playbook

This methodology can be distilled into a playbook that ensures the right tools and practices are in place. Without it you get dashboard sprawl, a constant barrage of questions and requests to the data team, frustrated stakeholders, frustrated data team.

At Wave40 we have built exactly this playbook for SaaS businesses (others will eventually follow) and the coolest part is that we’re giving it away for free!

This playbook contains:

• All the metrics needed to measure the performance of key business processes

• A metrics tree that relates all the metrics to each other to visualize value flow

• A dashboard tree that connects portions of the metrics tree into a hierarchy

• Dashboard views that indicate red/green when a metric variation is common vs special

• A systematic process for reviewing performance, “drilling into the reds”, root causing, etc.

• A systematic process for discovering input metrics (aka levers) that impact KPIs.

You can get the playbook for free here:

Until next time!