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Chapter 1: Data Integrity

Revenue Operations aligns and optimises various revenue-generating departments within an organisation.

At the core of this strategy lies data. 

However, the volume and variety of data can pose significant data-cleansing challenges.

Let's look at that in a bit more detail 👇

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Data Integrity

Data hygiene issues can hinder decision-making, customer experiences, and operational efficiency.

That’s why maintaining data integrity is key.

This involves minimising inconsistencies and redundancies and ensuring that data remains high quality across revenue-related functions.

The benefits of data integrity in RevOps

Clean and reliable data provides demand signals which help GTM teams understand their ICP and minimise the risk of failure when expanding into new market segments. They can target the prospects who share the right fit and characteristics with their best customers.

As Sid Kumar, Senior Vice President of Revenue Operations at HubSpot said:

‘Data is really the fuel that drives a relevant and highly targeted go-to-market strategy.’

Accurate data also helps identify the early signals of a customer’s churn. This could allow organisations to be proactive rather than reactive and work to resolve customer’s pain points ahead of the curve.

Data integrity reduces errors and redundant work.

Reliable data allows for personalised and effective customer engagement strategies, improving the overall customer experience. Sales and marketing teams can precisely target the right audiences.

Accurate data is essential for predictive analytics, enabling organisations to anticipate customer needs and identify trends.

Data integrity ensures compliance with data protection regulations, reducing the risk of legal complications.

RevOps and data hygiene

RevOps teams can implement data hygiene strategies to maintain data integrity.

RevOps establish data governance policies and standards, defining data quality metrics and ownership responsibilities.

RevOps integrates data from various sources and reviews and updates customer information, eliminating duplicates and correcting errors.

Strategies to improve and maintain data integrity

  • Review collection process: Start with clean data by assessing and adjusting the data collection process. Communicate the value of data integrity to all employees.
  • Structuring your team: Achieving nearly 100% data integrity depends on your team structure and who is responsible for each data point.

Consider designating roles such as an Insights Manager, Pre-Sales, Post-Sales, and Marketing Operations to oversee specific data-related functions.

Each team member should be clear about their responsibilities, including managing data syncing from marketing efforts, tracking spending, and maintaining data integrity.

  • Tech stack: One of the primary responsibilities is ensuring that all the different tools and systems are connected.

This connectivity is essential to create a unified data ecosystem that enables smoother operations.

Leore Spira, the Director of Revenue Operations at Blink, pointed out:

"You want to make sure that you build a tech stack that is suitable, not just for the sales team, but also for other stakeholders in the process because you don’t want to start bringing too many tools and integrate it with several platforms.

You need to think outside the box, but you also need to ensure that you’re using your resources smartly and carefully because otherwise you’re damaging the data."

Conduct thorough research into tech stack best practices.

Evaluate tools for lead routing, prospecting, data orchestration, data capture, and data suppliers.

  • Check for data errors: Implement multiple checkpoints to validate data correctness and completeness throughout its lifecycle.
  • Define your data’s relevance and importance: Not all data is created equal, and more data doesn’t necessarily equate to better insights.

Instead of being overwhelmed by vast amounts of data, focus on understanding what data truly matters to your business.

Take it from Sid Kumar, who said:

 ‘It’s easy to get caught up in data for data’s sake, and more is not necessarily better in this context. 

I think being targeted and focused about what you’re using your data for and how it’s helping you connect your go-to-market strategy with your customers is essential.’

Define how you intend to use this data, whether it’s to understand your Total Addressable Market, achieve a Share of Addressable Market, or ensure product-market fit.

  • Data refinement: Data often has a shelf life, and knowing when data is 70-80% accurate is usually enough to base healthy and conscious decisions on.

As Sid Kumar explained:

"You sometimes have to accept it’s never going to be perfect. There’s no such thing as ‘perfect’ data, and as soon as it becomes perfect, normally it’s outdated and stale. 

So it’s how much data you need in order to get a conviction as a company that you’re taking a calculated set of risks and bets to go after a market."

  • Limit data access: Restrict data access to necessary users to reduce the risk of data integrity compromise.
  • Error-detection: Utilise error-detection software to identify potential data risks.

Closing thoughts

Data integrity is essential in any effective revenue operations strategy.

 By recognising its importance and implementing strategies to maintain it, organisations can drive revenue growth, and provide exceptional customer experiences.

Looking for more on Data Integrity?

Data Warehousing with Andy Mowat
Building an Insights Model with James McArthur
Data Democracy with Navin Persaud