When teams implement systems and processes, the immediate focus is usually on operational convenience. What’s the fastest way to go live? What fields do we think we need right now? How can we avoid disrupting current workflows?
It’s an understandable impulse. But too often, this short-term thinking creates long-term problems. When your business scales or your board asks questions you can’t answer, you’ll find yourself walking backwards, trying to undo system and processes decisions made months or even years ago.
Instead of starting with what’s easy today, start with what you’ll need to know tomorrow.
Reverse-Engineering Your Analytics
Think about what you want to see:
- How do deals move through your pipeline?
- Which campaigns are generating actual revenue, not just clicks?
- What is our gross revenue retention?
- Which product is performing the best?
Now ask: What has to be true in my data to answer these questions? That’s where your implementation starts.
By reverse-engineering from the KPIs, reporting and visibility you’ll eventually need, you can set up cleaner systems, capture better inputs, set up data verification, and avoid costly data rework down the road.
Humans Make Data, Therefore, Mistakes.
Even with a thoughtful setup, data quality issues are inevitable. People miss fields. Typos happen. Workflows break. That’s why it’s critical to build in data quality flags and alerts.
Set up a system that can actively tell you when something looks off:
- Opportunities with no subscriptions attached
- Accounts exist in the Invoice system but not in the CRM
- Missing values in required fields
- Subscription end date is before the start date
- Sudden spike in account revenues
- Duplicate records across key identifiers
Catching these issues early is the difference between a minor cleanup and a major forensic investigation.
Real-Time Alerts, Not Retrospective Regret
The longer you wait to fix a data issue, the harder it is to trace. Wait a week or a month, and the context is gone. People forget what happened, or the person who made the change has moved on to something else.
That’s why Discern surfaces data quality issues in real time so your team can fix the problem while it’s still fresh, not during a fire drill the day before a board meeting.
Data Quality Is a Strategic Advantage
Good data doesn’t just mean fewer headaches. It means faster decisions, better forecasts, and more confidence in the numbers you share across your company.
If you’re constantly questioning your data, you’re not strategizing and moving forward the business. With the right foundation, alerts, and hygiene processes, your data can become a source of insight, not stress.
Want to see where your data quality gaps are hiding? Discern can help you spot the cracks before they become costly. Let’s talk!
