Data Operations, revisited

When I started writing about data operations In 2020 I suggested an example definition that focused on data shared between systems, the people who update that information, the tools used to transform and transport data, and the governance process they used to keep things working.

That definition was functionally correct and was missing a critical element: what to do about the information you discover from that process.

Here’s an updated thesis:

Data operations teams deliver data and insights for key business questions and enable operational changes based on the answers to those questions.

What is Data Operations today?

Data Operations in 2020 was a nascent idea for how to build an internal data practice. I’ve been refining these ideas into everyday tasks and transforming the “inside a company version” of data operations into repeatable playbooks for improving operations.

What’s the Ideal Customer Profile for this data-improved enablement of operations?

FOR operations teams WHO want a cross-functional way to activate data AND want to drive change in their organizations, Data Operations is a style of working or a discreet team to find, understand, and troubleshoot data in the organization, usually to make Go-To-Market teams run faster and more efficiently.

Data Operations is not …

• Data observability, though it uses those tools to provide insight
• Data science, though it helps to have a sense of probability and statistics
• Data engineering, but workflows, models, and continuous delivery of quality data is an important aspect of this practice

Data operations is a cross-functional idea to build products and services with data so that the whole company can run faster. It’s a force multiplier or flywheel to orient, observe, do, and adjust (OODA).

What does it look like in practice?

Data Operations is at the center of Ops Teams

The goal of an operations team is to help things run more smoothly inside of an organization. Whether that’s customer ops, sales ops, or some other kind of operations, they run regular plays that complete the essential shared support tasks in a company.

Support implies that things sometimes go awry. The ops team’s job? Fix it and get back on track while having minimal impact on the customer and internal team. That means finding the right resources to solve the problem, documenting it, and determining how to prevent it from happening again.

For these teams, Data Operations helps illuminate the problems between systems. When a workflow fails and sends an alert trigger to Slack; when a Dashboard doesn’t match expectations; and when stuff is jacked up, you need a team to solve that problem.

In the beginning, Ops teams self-service this troubleshooting. But as teams grow, it gets harder to think about the related network of possible things that could cause problems. Without a data ops mindset, making this repeatable is hard.

Data Operations is inherently cross-functional

This is a cross-functional role. That means you need to be an internal consultant to be effective in data operations, starting by following the data as it walks through systems and then identifying the failure points that happen.

Without knowing what’s supposed to happen and the normal workflow, it’s hard to build exception conditions and to alert when things break. When you take the time to learn how each team works, you’ll also see interesting threads across systems.

Some of these tools involve:

• simply writing down what’s supposed to happen, including the expected data that a system receives
• building process maps if you want to be fancy
• documenting what happens today so that when things don’t work you have a record of making a mistake and don’t need to repeat it

If these things do not help other teams do their job better, they are fancy paperweight ideas. The purpose of a data operations team is to improve operations using the data that is already flowing through those systems.

Sometimes, the addition of a single field in an alert (or a link to another system) makes the process run better. In other cases, there might be a heavier lift. But the goal is to give the team superpowers.

Data Operations is a core business process

Data is the lifeblood of your business. And Data Operations can improve the business if you know what you are asking and why.

Take a typical metric: improving something from x to y by a specific date (e.g. improve the number of daily leads from 100 to 150 over a period of several weeks).

The metric is an outcome, but there are many inputs that cause that outcome to change. What goes into improving that number? Probably a series of efforts that improve both the top line of acquisition through a mid-funnel exercise to understand whether these leads convert better or worse than other leads through to the end conversion.

Moving a metric doesn’t guarantee success, so you need to know the outcome you need and the process that will help your team to observe, orient, do, and adjust.

Data Operations is the process of building playbooks that take you there.

What’s the takeaway? If you want to make iterative changes to your business and see whether they are working, Data Operations helps you take the sometimes complicated and confusing handoffs between teams and turn them into repeatable playbooks. By understanding what data needs to end up where, you can make things better for the whole team.