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RevOps Strategy

The Blueprint for Data Ops: Part 2

Now that you’ve had a little time to think about our first dive into Data Ops — including how to think about data differently than before — it’s time to delve deeper. Let’s go beyond defining data and think about how Data Ops can help the different parts of the business use data more efficiently.

How can Data Ops help?

The purpose of a Data Ops team — and an operational team in general — is to make your business run more smoothly and efficiently. When data or process issues are found, a Data Ops team might suggest changes (e.g. start doing something, stop doing something, or change an existing process) to execute and measure with the goal of helping the business move forward.

Think of Data Ops as a consultant to identify the changes needed to fix the issue in every part of your tech stack, not just one of them. And, for each team and/or system in the business, the improvement needed will be different.

Where that change happens will also be different. Especially with modern frankenstacks… I mean tech stacks, it’s critical that each team determines and maintains a source of truth. This isn’t necessarily a single source of truth for the entire business — more often than not, that simply isn’t possible — but it is the undisputed source of information (or system of record) for that team.

Is it sales vs. customer success vs. marketing?

It’s easy to play different teams at a company against one another when it comes to answering the question of “who owns what data?” Each team has a valid claim to portions of the customer lifecycle:

Sales wants to know all about why a lead or prospect came to the company and how to solve their pain point. To store that information, a sales team’s system of record is often a customer relationship management (CRM) system such as Salesforce or HubSpot. Using a CRM as their system of record gives the rest of the company a place to review information about ongoing sales business and a method of interconnection to other teams’ systems and data.

A customer success (CS) team usually will want to store different information about customers (and prospects) than the sales team, and so they will have their own source of truth. CS teams focus instead on the interactions they have with those customers; many store data on the number of support tickets, entitlements on a customer contract, or what products and services the customer has bought and used.

A marketing team might store still more information about a person’s entire lifecycle, especially prior to them becoming a customer. Data on leads and prospects, as well as the campaigns or events that brought them to the company, all need to be stored and linked for marketing to determine their efficacy. For the marketing team, attributing initial interest from a lead to the end product of a successful sales-activated lead or a sale is key.

No, it’s sales and customer success and marketing

For a company to succeed, these three teams must not be at odds and instead need to work together and share data.

For Data Ops, part of their responsibility — and perhaps one of the biggest benefits of a Data Ops team — is to facilitate the teams working together. In the perfect scenario, each has equal access to information about the customer, suffers no friction when trying to identify a person, and has a direct line to quantify the cost of revenue.

But have you ever worked in a business that had all of those things figured out? Maybe. But probably you are familiar with some of the pitfalls that occur when the data machine is not quite so finely tuned.

Moving forward

To create the line that connects all the teams that touch customer data on a regular basis, and most importantly, ensure the teams are sharing good, clean data, your business needs to implement a Data Ops team. (Which, if you’re this far in, seems like you’re well on your way to doing so.)

So, now that you know the types of data that are a problem, the teams that need all this data, and how a single source of truth won’t fix your problem, what’s next? Check back for Part 3 where we’ll discuss the how. In the meantime, take a minute to sign up for our Data Superheroes newsletter to hear from other pros in the world of ops.

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