How report preparation issues are a data quality problem

In our sixth installment, designed to complement The trailblazer’s guide to marketing, sales, and RevOps excellence, we turn our attention to the topic of reporting. Accurate reporting is fundamental for making informed business decisions and maintaining trust in your data. This blog, along with the ebook, provides a comprehensive roadmap to optimize your RevOps data quality strategy, ensuring your reports are dependable, precise, and actionable. 

In previous posts we tackled how enhancing data quality can resolve ideal customer profile (ICP) issues, improve segmentation, data governance issues and routing accuracy. We also highlighted the importance of managing duplicate accounts and records for clean data.

The challenge of accurate reporting for RevOps data quality

So, maybe accurate reporting shouldn’t be this hard.

Probably everyone in Ops has a story about sitting in a meeting where someone pipes up with, “Why are there two rows with the same information?” It looks something like this:

Suddenly, instead of looking at the data, everyone is looking at all the problems with the table. Trust in what’s being shared has been eroded.

Manual data correction

Most of the time, in order to fix these issues before they’re presented, operations folks, data analysts, or others spend time manually correcting the information in a spreadsheet so they can create a table or graph that doesn’t have these pesky problems.

But no one wants to spend their days manually checking data before a report is created. And, honestly, reports should be available on demand, without a bunch of manual work by anyone.

Automated data cleaning

If you have automated processes that look at keywords and classify the results, you can easily clean up the data before it even gets to a report. For example, you could have something that looks like this:

As you come across different variations, you can add them to the table and ultimately have an exhaustive list of all the variations and the standard value you prefer to use.

Standardizing data entries in RevOps data quality

Another example is a report that includes United States, US, and U.S.A. in three different rows. You already know someone’s going to call that out, and rightfully so. Or you could clean up the data first:

You have the option here to pick the standard value you choose, whether that’s the full country name, ISO-2, or ISO-3 formats.

The case of automation in RevOps data quality

Yes, you can definitely clean up your data manually every time you need to produce a report, but is that really what you want to do with your time and career? It’s certainly not scalable and it doesn’t fix the problem at its source permanently.

Create automations, avoid this manual labor, and have clean reports that are easy to produce on demand, just because of the improvement in data quality.

Ready to learn more about improving your data quality? Download The GTM guide to data quality to ensure your data is prepped for reporting and more!

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