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Revenue Operations

Unlocking Data Quality Excellence: Strategies and Principles

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What’s one thing that every RevOps team in the world struggles with? It’s data quality. No matter what you do, managing your databases is an ongoing challenge for data-driven businesses. Luckily, RevOps experts from Openprise have a strategy for you. 

Join Jasmine Chung, Director of RevOps, and Laura Marzola, Solutions Engineering Manager, at Openprise as they show RevOps Co-op how to address top data quality challenges, improve marketing and productivity, and reduce risk. Plus, snag their sample RevOps automation process AND a multi-system RevOps rationalization roadmap to help you get started!

In a recent Openprise State of RevOps survey, we see that dirty data affects RevOps’ ability to separate meaningful signals when deciding on next steps for their go-to-market plan. One reason for bad data is that companies have dozens of tech tools in their stack, all showing different data to different people. 

“A lot of marketing and sales teams are looking at one pane of glass and not the full picture. There’s no holistic view across marketing and sales.” - Jasmine Chung

Having too many systems can result in poor data and there are serious downstream impacts of having too many tools. Ever try to run a report in your CRM only to realize you have 5 different fields capturing company size, and they all have different numbers? How do you know which is correct? 

5 reasons we struggle with data quality

Poor data can significantly affect your marketing and sales funnel by delivering MQL and SQ leads that aren’t accurate. This makes it hard to know which campaigns to invest in. At some point you have to tackle your data quality, but there are roadblocks. 

“Brushing it under the rug does not make it disappear” - Jasmine Chung

Top 5 reasons for poor data today: 

  1. Improving data quality is a low priority. 
  2. Poor documentation, low enablement, and loosely agreed terminology means that teams aren’t on the same page.
  3. When no one is assigned to tackle a data quality project, there’s no accountability to deliver.
  4. Lack of tools and resources needed (or having too many competing tools) to focus on data quality. 
  5. You’ve tried but can’t figure out the best approach. 

3 ways to process your data

Revops wasn’t hired to be a cleanup crew, but it’s the state of the world. - Laura Marzola

Creating data excellence is never going to be easy, but you can tackle this problem with the right strategy. Here are 3 methods for processing your data: 

Manual data processing: 

Using spreadsheets gives you 100% control over how to prepare your data. Unfortunately, it can inflate databases, cost your company money, lengthen process life cycles, and lead to unhappy employees. You’ll still end up with multiple versions of your data.

Hybrid data processing:

This method lets you work directly inside the source of truth for each department. It’s semi-automated with a more intuitive user experience. But more complex customizations require coding, troubleshooting can cause cross-functional delays, and you can quickly reach API constraints. 

Fully automated data processing:

Openprise provides a fully automated, no-code platform for data onboarding and other RevOps processes. It’s self-service and completely customizable.  Since Openprise is cloud-based, it won’t bog down your systems of record. Keep in mind, powerful automation does have a small learning curve. 

Free guides and roadmaps

Want one more reason to watch this webinar? Jasmine and Laura share a sample RevOps automation process for list loading that helps you prepare data, automate decisions, and execute strategy. You’ll also find a multi-system RevOps rationalization roadmap to guide you in running your data excellence plan from kickoff to your one year milestone. 

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