My workflow steps
Problem-first process
Start with a problem, not a data set. Every workflow I use begins with a real-world question—then I build a process around the signals that actually matter for that context. No chasing shiny objects.
Peer-reviewed sources
Every source I use goes through peer review. Whether it’s an open dataset or proprietary feed, I test it with colleagues, check for bias, and run scenario analysis before making a call.
Documented workflows
Open methods are the default. Every step, from data cleaning to model building, is documented so you can retrace the logic and spot any flaws.
Got a workflow to share, or a process you want to stress-test? Drop me a message—always happy to debate new approaches.
Building trust
Transparency counts
Nothing beats transparency for building trust. Every method I use is published and peer-reviewed—no hiding behind proprietary models or black-box algorithms.
Never stop learning
Continuous learning is built in. I follow the latest research, adapt to new sources, and regularly test assumptions against real results.
How I build trust
When I first heard about alternative data, I figured it was just another buzzword—something for big budgets only. But after digging in, I saw the value: it’s not about the data itself, but how you use it. Every new signal is a question—will it actually help spot something conventional analysis misses? So I started cross-referencing: job posts with earnings calls, sentiment data with actual company actions. I learned to distrust “black box” models. Instead, I walk through each decision, keeping everything as open and reproducible as possible. The aim isn’t to replace what works, but to fill the blind spots and push the boundaries of what’s possible. If you’re skeptical, good. I’m always testing, always questioning, and I’m happy to share every step.
Gallery of process
How workflows and transparency shape the process
Workflow mapping
Sketching out workflows on a whiteboard, I map how each signal interacts with others. Visualizing the connections makes it easier to spot gaps or redundancies before diving into the data.
Code review
During code review sessions, colleagues challenge every assumption. We dig into each function, check for hidden bias, and look for places where logic might break down.
Documentation logs
I keep logs and documentation in a shared system. This means everyone can see how the analysis was built and replicate it or spot issues fast.
Feedback in action
After publishing a new method, I invite feedback. It’s all about finding blind spots and building something stronger as a community.
Document everything
When I say everything is documented, I mean it. Every step, decision, and test is written down, so you can check my work or use it as a blueprint.
Transparency doesn’t just mean sharing results—it’s about showing the work behind every call.
I track where data comes from, who collects it, and what methods are used, so bias is always checked.
Mistakes happen. Keeping detailed logs makes it easy to spot errors, fix them fast, and learn for next time.
If you want to reproduce an analysis or adapt a workflow, all the documentation is there—no guesswork required.
Peer review isn’t just for journals. I invite outside scrutiny and publish methods whenever possible.
All this keeps me honest, and helps anyone who wants to build their own process, too.
Trust-building features
Solid processes build trust—documented, checked, and open for feedback.
See every step documented
Documented workflows let you check every step of the process. This helps catch errors and supports continuous improvement through peer review.
Analysis checked by peers
Peer review means methods are checked by others. This strengthens analysis and helps keep bias in check—citing CFA Institute as a core reference.
Adapt as you learn
Adaptable frameworks evolve with new data. You’re never stuck with outdated methods, and every step can be challenged and improved.
Inside my research workflow
Tired of marketing fluff? So was I. That’s why every claim here is traceable to a peer-reviewed method, industry study, or open-source tool—nothing behind a curtain, just open books and real evidence.
Documented steps
Open workflows and independent checks
IndependentForget following the herd. I’ve built independent, open workflows to keep confirmation bias at bay. That means leaning on reproducible steps, peer review, and open-source tools wherever possible. If I say a data point matters, I’ll show you how I got there—no shortcuts, just clear reasoning from start to finish.