How to Create a Comprehensive RevOps Data Strategy in 7 steps

Building a comprehensive RevOps data strategy is a project that involves people, processes, data, and technology wrapped up together. It seems like a daunting task, so it helps to have a roadmap.

Let these seven steps be your guide:

Step 1: Set up a data strategy governance system

Many organizations create cross-functional governance teams to look at data issues. You can do the same by bringing together delegates from marketing operations, sales operations, IT, legal, and compliance. Add some representatives from the business side, too, but try to keep the full council at seven people or fewer. You don’t want to create scheduling nightmares when you can’t find a common meeting time for so many people.

Once you’ve got everyone assembled, it’s time to lay the cards on the table. Be ready to listen—really listen—to everyone’s data issues. Find out why each team thinks data needs improvement. Only after you’ve heard the challenges, each team faces can the ops team put a plan in place to fix them. Together with the council, plan to prioritize the issues, identify quick fixes, and understand the projects in terms of effort versus impact.

Step 2: Zero in on data-reliant processes

Where do you start when tackling your data? Categorization is a typical start, but what should your categories be? There are many choices, and picking the right one for your company can make the difference as you embark on your data strategy journey. Typically, companies choose one of four paths:

By system: Assign stewardship of your data according to the systems you own.
By function: Medium-sized companies might prefer to consider data as marketing data or sales data. In that case, assign stewardship at the functional level.
At the data level: Large companies might have a master customer list, which combines contact and account information. No matter what system it fits in or what function it uses, a data steward looks after product or transactional data.
By process: Huge companies have process owners embedded in the business, so an employee or team likely owns the data that flows in and out of that process.

Step 3: Get the voice of your internal customer

A crucial step to a comprehensive data strategy is understanding what your team wants out of the data. And the best way to find out is simply to ask them. However, remember that it’s still a two-way conversation.

You should already have a good idea about what sets of data your internal customers are interested in. Prepare an interview guide and conduct stakeholder interviews that’ll keep the dialogue moving and provide constructive insights.

In these interviews, you might discover, for example, that your data vendor routinely overwrites sales reps’ manual entries. That’s when you can propose a change that will add an extra field for manual entry, leaving another field for the data vendor. Knowing what your internal customer wants helps you design a data strategy that works for everyone.

Step 4: Audit the current state

What does a complete record look like to you and your customers? Do you know which entries correspond to people actively working with you and which are inactive? Do you have their current opt-in preferences in compliance with the privacy mandates in their region, country, or state? And speaking of recency, how long has it been since they engaged with your company? Research shows that over 25% of contact databases go stale every 12 months.

Creating a matrix with this information will give you a graphical view of your data and its level of freshness or staleness. Once you’ve put aside all the old and incomplete records, you’re left with different tiers you can use for outbound outreach. Why? They’re good candidates for enrichment.

Another tactic is to do a port of entry analysis. With this strategy, you’ll look at all the different ways data comes into your ecosystem over a specific period. A port of entry matrix will enable you to analyze the quality of data at all the entrance points and get an instant road map to where good and bad data originate.

Step 5: Acquire the missing data

Okay, so now you know the truth. Some of your data is incomplete and needs work. That means you need to find data to fill in the blanks. There are a couple of ways to do that.

One option is to buy data. However, that route has become exponentially more complicated as different countries, regions, and even states have passed strict privacy restrictions and laws around the sale of personal information. The general rule of thumb is to check that the recipients agreed for a company like yours to contact them, make it easy for recipients to unsubscribe, and perform a double opt-in so recipients can confirm they want you to communicate with them. Once you’re sure your practices comply with the different regulations, you can use purchased data to augment your records and apply cleanse-and-append services to your data from the backend.

Because of the restrictions around the purchasing solution, many organizations are turning to an inbound approach. Often, the best source of data is somewhere else in the company.

Through data unification, you get a 360-degree view of all the data in multiple operating systems.

Step 6: Curate data over time

Cleaning your house one time doesn’t cut it. The same goes for data—it’s an ongoing project. And just like your home, the best way to keep something clean is to make sure it never gets dirty in the first place. A depressing realization in real life, but in RevOps, it’s part of the data strategy.

Data rarely enters your systems clean. Plan your data strategy with one of these game plans:

Surround and attack: Get to the data in real-time, as it’s coming into the marketing automation platform. You can also take care of the enrichment process and create clean records before it starts moving down the line.
Schedule cleanup batches: Some companies manage the same process but on a monthly or quarterly basis.Step 7: Measure the improvements

Naturally, you need metrics to show how your data strategy is performing. But be sure that you measure the right things. Even though you’ll be happy that the data is in better shape today than it was six months ago, company management really cares about revenue. So you’ll need to demonstrate how clean data gets better results. They’re looking for cause-and-effect metrics, like:

Do we see better conversion rates?
Are fewer leads disqualified for bad contact data?
Do we have higher volumes of leads?
Do we see bigger potential deals?

If you’re able to make a case that improved data led to more efficient activity, which in turn led to increased output, your story will be a compelling argument for your effort and budget.

Bonus step: Sing the praises of the excellent work everybody has done

Data can exist in a hidden, murky world that non-RevOps people might not understand. So go ahead, be sure the business knows how your efforts help the company and bring more value to sales and marketing. A little humblebrag never hurt anyone. And it’s not like you and your team don’t deserve it.

How to Create a Comprehensive RevOps Data Strategy in 7 Steps