Webinar spotlight – Juggling Data Sources: Automation Hiccups & Corrections

Numbers don’t lie. But when multiple data sources aren’t properly managed, it’s not good. It can wreak havoc on your RevOps data quality as a whole.

These data issues can lead to misalignment between departments, errors in enrichment data automation, and mass confusion around whether or not your data can be trusted.

We sat down with other RevOps leaders for a candid discussion around this topic in a webinar, “Juggling Data Sources: Automation Hiccups and Corrections,” sponsored by RevOps Co-op.

Speakers

• Rhys Williams, founder and managing partner at Domestique
• J.P. Zangre, director of marketing operations at Bazaarvoice
• Cayden Bergeron, enterprise solutions engineer at Openprise

The importance of data integrity and RevOps data quality

The session was kicked off by a funny meme featuring Dr. Evil, emphasizing the collective pain point around data integrity.

Data quality can mean different things to different people. Cayden highlights our 3-layer approach to data quality here at Openprise::

1. Technical quality: This is your foundation. It includes standardization, deduplication, and other basic technical aspects. These elements form the very bare bones of your RevOps data quality layer.
2. Operational quality: Can you make timely decisions? This layer is about operational efficiency. Are you able to route leads effectively based on the reliable data foundation you’ve established? If your technical quality is sound, your operational quality will be robust as well.
3. Strategic quality: This is the top layer where you make those data-driven decisions. Executives usually focus on this layer, asking questions that rely on the integrity of the underlying data.

Poor data practices can wreak havoc on your operations down the road. Prioritizing data quality up front can prevent a lot of internal frustration and misalignment later on.

Both Rhys and J.P. highlighted that decision-makers often overlook data integrity, focusing instead on throughput and insights. Executives are interested in making data-driven decisions, not getting bogged down in the details of data enrichment projects. So, when presenting to them, it’s essential to link data integrity efforts directly to tangible business outcomes.

Stages of data integrity

Data integrity exists on a spectrum. Isolated errors don’t ruin an entire database: it’s about making decisions with “good enough” data. Think of it like a total addressable market (TAM) analysis, where the exact figure isn’t as critical as having sufficiently accurate data to guide decisions.

J.P. mentioned that emotional conversations about data often lead to actionable solutions. By promoting data literacy across the organization, RevOps teams can ensure data is used effectively and strategically.

Best practices for RevOps data quality

Cayden, Rhys, and J.P. share some of their data quality best practices to drive effective decision-making and operational efficiency across the organization.

Align definitions
Make sure everyone in the organization is on the same page with clear definitions and a shared understanding of data terms. This avoids miscommunication across different teams and departments and keeps things running smoothly.

Recurring data enrichment schedule
Set up a regular schedule for data enrichment to keep your data in good shape. Know what needs real-time updates and what can be handled periodically.

Establish a business systems council
Create a business systems council to align priorities across different tech owners. This ensures that data integrity initiatives are strategic and coordinated.

Choose the right data enrichment tools
Pick data enrichment tools based on your specific needs. Different tools are better suited for different tasks, such as geographic regions or specific industries.

Don’t get wrapped up in specific data issues
Don’t let minor data discrepancies derail your progress. Stay focused on the bigger picture and keep moving forward one step at a time.

Final thoughts

Data integrity is a journey, not a destination.

It requires ongoing monitoring, adjustments, and strategic thinking along the way. By aligning best practices with business goals, equipping your team with the right tools, and promoting a culture of data literacy, organizations can make informed, data-driven decisions.

The session wrapped up with a reminder to keep these best practices top of mind, recognizing that data integrity (RevOps data quality) is the cornerstone of any successful RevOps team.

Don’t forget to watch the replay!

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