A CS champion’s journey from patching problems to solving them at the root
Over the past decade, I’ve worked with more than 100 companies trying to turn data into decisions. Before joining Kubit, I worked for one of the largest product analytics solutions on the market. I supported users from enterprise product teams to scrappy startups, and have seen a common thread in nearly every engagement: smart people asking good questions and not trusting the answers they get.
In meeting after meeting, I watched teams ask things like:
- “Why does this report conflict with our dashboard?”
- “Which version of this metric is the right one?”
- “Are we even looking at the full picture?”
Instead of building momentum, they stalled. Not because they didn’t care about data, but because they couldn’t trust it. The more tools they used, the harder it became to see what was actually true.
When You Can’t See the Whole Picture
There was a moment when it really clicked for me. I was helping a team untangle conflicting reports on a key growth experiment.
In my time working with my previous company, we spent more energy investigating breaks in ETL pipelines and any insights we gathered met the scrutiny. So much so that I felt like the girl with her finger in the dam, holding back the water in hopes that someone would come fix the issues. Ultimately, this, and other similar issues, caused the customer to leave the SaaS company I worked for in lieu of one that connected directly to their source of truth.
That experience wasn’t an outlier. It was the norm. Teams were forced to make critical decisions without the full context or worse, with incomplete or conflicting views of reality. And I realized: we weren’t solving the real problem.
CS Should Empower, Not Explain Away
Customer Success shouldn’t be about holding back the water or explaining people through ambiguity. It should be about enabling clarity, confidence, and action.
But too often, my role became about defending dashboards or translating data gymnastics when what I wanted was to empower teams to ask smart questions and trust the answers.
That shift in mindset led me to Kubit.
Why Kubit
What drew me to Kubit is how directly it solves the exact pain I’ve experienced for years: data discrepancies, duplicated logic, and endless explanations about why two reports don’t match. All the while, supporting complex behavioral questions that Product Managers need fast and BI can’t do.
Kubit connects straight to the data warehouse. No pipelines to maintain. No exports to chase. No transformations that obscure what’s actually happening. As someone who came up through BI, I’ve always believed that the only way to legitimize product analytics in the eyes of the business is to align it with the same source of truth used for executive dashboards and board reporting.
When product data lives in its own disconnected tool, it’s easy for teams to dismiss it. But when your product insights come from the same place as your financial metrics and operational KPIs, they carry weight, and they stand up to scrutiny.
And now, with the introduction of AI-powered features, we’re going even further:
- Natural language query creation
- Automated insight generation
- Root cause detection
- Proactive data quality alerts
These aren’t gimmicks but tools that help teams focus on decisions instead of debugging dashboards. I cannot tolerate when AI is used to oversimplify, it should be another resource in a Product Manager or Marketers toolkit to accelerate their rate of exploration and insights.
In each customer interaction, I ask them how their business is thinking about integrating AI. Nine times out of ten the answer is something like “we are mandating that employees use AI to be more efficient in their jobs” which is precisely what we are aiming to do with Kubit. The ability to ask and answer questions of your data shouldn’t be bottlenecked by a BI or Analyst team, though we need a layer of governance (tools like Kubit) that allow for business logic to drive insights.
Becoming a Data Detective
That’s the mindset behind my new series, Data Detective. It’s about surfacing the story behind the numbers giving teams confidence in their conclusions and helping them move from reactive to strategic.
We explore questions like:
- Why are conversion rates dropping for this segment?
- What’s driving adoption for a new feature?
- How can I confidently tell this story to my exec team?
The objective of this series isn’t to gloss over the “stuff we all know”. It’s about digging into the mess of analytics and surfacing some best practices for others to leverage, no matter the tool you use. Getting really good at asking the right questions, pulling the right threads, and organizing the right insights will always be touched by human intervention. These aren’t skills that come naturally to some, but are still necessary to be exceptional in your role. I want to share all the experience I have and hopefully it helps or starts some interesting conversations!
If you’ve ever thought, “I don’t know if I trust this data,” or “I wish I had the full picture,” you’re not alone. I’ve been there too.
And now, I’m building alongside a team that’s fixing it.
