RevOps Strategy

The Blueprint for Data Operations: Part 1

There's a lot of talk and change in today’s world of operations. The various teams being created — Sales Ops, Marketing Ops, Customer Success Ops, and Revenue Ops, not to mention those in the IT space — means there's ample room for confusion and conflicting ideas. But the thread that ties them all together is data. So, where’s Data Operations? In this multi-part series, we'll discuss what Data Operations is and how building a Data Ops practice in your organization can solve some of the many problems you face with data each day.

What is Data Operations, anyway?

Data Operations is a hidden team in your organization, connecting business and people systems. They help Sales Operations, Marketing, and Customer teams present accurate metrics, marketing data, and customer operations to executives. You may not realize you need a Data Ops team until you find unexpected results in data and they slow down the business. And when you have a Data Ops team, you’ll find unexpected advantages, too.

But why Data Ops instead of just… Operations? After all, the people who solve ops problems probably know about the data in those systems, right? Too often, ops gets siloed and can’t get the whole picture of how data interacts in their tech stack. The complex system formed from the interaction of multiple systems and teams exists out of their reach.

Data Operations might refer to the people who perform data tasks (usually in a Sales Ops or Marketing Ops team) or maybe a dedicated analyst or team working on business intelligence analytics. It covers the following:

  • Data that is shared between systems
  • People who analyze, update, and fix the data in those systems
  • Tools they use to keep their data accurate, relevant, and timely for business
  • And the governance process they use to keep everything working

But what if the Data Operations function was elevated within every go-to-market (GTM) organization or Revenue Operations team as a critical tool to increase sales velocity, pipeline growth, and customer happiness? That’s exactly what we’ll discuss in this series.

Needed: a new way to talk about data

Before we can really delve into Data Ops, we have to speak about one of the most important factors — and heck, it’s even part of the name: Data.

No, not this Data.

There’s a lot of talk about the need for better data, but not always about what that actually means. Data is the connective tissue between the different parts of a business. When it doesn’t work or causes problems, it ends up creating hidden and, more importantly for your C-Suite, visible problems.

Yet little effort is spent to fix data on an ongoing basis. There are a lot of one-time fixes, prompted only by a specific issue that prevents the business from running as expected. What if the problem is that we’re thinking about Data Operations as a bolt-on solution instead of an integral part of business?

Here are a few examples of why you should see these issues as systemic and not just as one-off problems:

  • As a developer, I’ve built applications that depend on data.
  • As a team or business leader, I’ve used both internal and external data for customer ops, marketing ops, and sales ops tasks.
  • As a subject matter expert in data quality, I’m responsible for data that drives the business and accountable to other teams to fix problems that emerge in our data.

A basic start to fix these data problems involves quantifying the metrics and measures that control your data. You should also investigate creating dashboards and reports to highlight both the problems and successes you may not have seen previously. Building automated systems can also relieve any superhuman personal efforts and take back some of your nights and weekends.

Continually test and iterate, make different mistakes, and put techniques together to provide a “data weather report” for your organization. (If you’re doing something daily or weekly, it can probably be automated and improved and something to look into.)

Moving forward

As you look toward the future and put some of this into practice, it’s important to recognize that this series will not solve all your data problems. It will also not tell you exactly how to untangle the particular challenge you’re having with your business. Every company handles and works with its data differently, and each organization should build its own processes based on its particular business needs.

What this series is is the beginning of a blueprint for a Data Operations practice, illustrating a different way of thinking about information. Rethinking the health of data in your business is an important task. Heeding the adage of “garbage in, garbage out” is a good first step, and there’s even more beyond it.

The blueprint you’ll create will inform inputs to your business challenges that depend on data. It’ll help you frame the data challenges you see in your company that may look like non-compliance or general error. And it’ll help you to become literate in the data of your business and how it is interconnected across organizations.

By building a Data Operations practice, you will better understand how your company lives, breathes, consumes, and exhausts data and reveals key information and insights.

Check back for the next part in the series where we’ll discuss the increasing types and amount of data available today and how it creates business problems. In the meantime, why not sign up for our Data Superheroes newsletter to read more insights and tips from some titans in the industry? Sign up here.

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