Not the glamorous part of the job. The part where a marketing campaign is supposed to launch Monday and the contact list has the same customer in it four times under three different spellings. The part where sales is chasing a lead that already closed because two systems disagree about who owns the record. I did this across Fortune 500 marketing teams, agency clients, and startups, and the pattern never changed: the data was always the bottleneck, and cleaning it was always the fastest path to results.
So I built the tool I always wished I had.
Why the experience matters
There's a popular story right now that anyone can ship software in a weekend with AI. I used AI heavily to build CleanSmart, and I'll tell you plainly: the twenty years came first. The tool executes. The judgment about what to build, and what to never do to someone's data, comes from having been burned.
Two product decisions show this clearly.
The first is confidence scoring. Most cleaning tools make a choice and move on. They merge two records, standardize a phone number, fill a blank field, and assume they got it right. I've watched automated cleanups quietly destroy good data because nobody could see what the system decided or why. So CleanSmart scores every change. High-confidence fixes happen automatically. Anything the system isn't sure about gets flagged instead of forced.
The second is review and approve. Every change is visible before it's final. You can see the original value, the suggested value, and how confident the system was, then approve it, reject it, or override it yourself. That human-in-the-loop step is the whole point. People don't fear data tools because the tools are dumb. They fear them because the tools are confident and invisible. I designed CleanSmart to be neither.
I learned both lessons the hard way, sitting through enough usability testing and post-mortems to know that people need control over decisions that affect their data. No amount of automation removes that need. It just changes where you put the human.
The background behind it
For the credentials side of things: I hold an M.S. in Information Systems from Drexel University and completed MIT's Applied Generative AI for Digital Transformation certification. I've led MarTech implementations across Salesforce, HubSpot, Adobe Experience Manager, Marketo, Pardot, and Cordial, and evaluated more than fifty vendors for regulated industries where a data mistake is a compliance problem, not just an inconvenience.
I was an Executive Director at Novartis, where I built a unified web strategy across 90 countries under GDPR, HIPAA, and pharma regulatory constraints. Before that I was SVP and GM at Razorfish and VP at BestReviews. Across those roles my work touched over 150 media properties, 1,200+ enterprise websites, and marketing databases north of 500,000 contacts. That scale is where you learn what quietly fails, not just what works in a demo.
CleanSmart is the product of all of it, built under CleanSmartLabs.
What I write about
I write about the realities of data quality work for the people who actually do it. The hidden cost of dirty data for growing businesses. Practical no-code data ops for marketers without an engineering team. Email deliverability. The integration mess of a modern MarTech stack. Less theory, more what to do Monday morning.
If you've ever stared at a contact list and known in your gut it was lying to you, we'll get along.