TOOLS & PATTERNS
Analytics patterns
Turning data into decisions—before the dashboards exist.
Approach
I’m often brought into analytics work before there’s alignment on the data, the questions, or even whether reporting is worth investing in. In those situations, the challenge isn’t visualization—it’s credibility. Data may exist, but it isn’t trusted, shared meaning hasn’t formed, and reporting is seen as a downstream concern rather than an operational asset.
My role in these moments is to surface reliable signals, shape shared understanding, and help organizations see what’s possible before committing to larger analytics investments. Sometimes that work results in dashboards I build directly. More often, it lays the foundation for teams and specialists to deliver with confidence.
I try to stay anchored on decision clarity and data shape: what the metrics actually mean, how they’re derived, and what context is required for the reporting layer to remain stable as tools and teams change.
Domain fluency through discovery
Much of my analytics work begins in domains where the data is unfamiliar, fragmented, or poorly documented. Rather than relying on prior industry knowledge, I focus on understanding how the system actually operates: what events occur, how state changes, where meaning is implied rather than explicit, and which signals truly matter to decisions.
Over time, that approach has required me to develop working semantic models across a wide range of domains, including:
Telco messaging and delivery pipelines, financial services bill payment and reconciliation, payments and fraud networks, demographic and census data, program and portfolio operations, and food and nutrition systems.
The constant isn’t the domain itself — it’s the discipline of translating complex, real-world processes into data structures and metrics that can be trusted, queried, and acted on. That same approach applies whether the data describes transactions, people, programs, or physical goods.
Selected patterns
Representative analytics situations where the work is less about charting and more about trust, meaning, and enablement.
Making data credible before it’s trusted
In one role, I served as the client representative for a technology provider working with Apple. The client raised concerns about quality issues that internal teams were unaware of and hesitant to investigate. Reporting was viewed as a low priority relative to delivery, and the prevailing belief was that the available data couldn’t be trusted.
I was granted access to raw event data and built a local data mart to explore it independently. Using Tableau, I mined the data for patterns and signals related to the reported issues. The resulting dashboards weren’t designed as a polished reporting layer—they were a way to make the data legible and defensible.
Those views became part of monthly account reviews, shifting the conversation from opinion to evidence. By grounding discussions in observed patterns, the team began to recognize both the value of the data and the need to invest more seriously in reporting. The dashboards didn’t just answer questions—they changed how the organization thought about analytics.
Incubating a semantic model before scaling reporting
In a strategic operations role, my area of the business depended heavily on operational signals, but those signals were fragmented across systems and inconsistently defined. Rather than starting with dashboards, I focused on constructing a shared semantic model that clarified how metrics should be interpreted and how different data sources related to one another.
I leaned heavily on Tableau Prep to shape and align the data, defining grains, relationships, and business logic explicitly. From there, I designed a suite of Tableau dashboards and managed the work of a developer who implemented them. These dashboards became the first coherent operational views for the organization and served as the catalyst for a broader investment in analytics and reporting.
The value wasn’t just in the visuals—it was in the process of agreeing on meaning before scaling delivery.
Designing analytics infrastructure for reuse
At Community Attributes, my role expanded beyond dashboards into data architecture. I drove the design of a data ingestion and access layer that programmatically loaded and transformed census and geospatial data. The goal was to make this data usable across multiple contexts—APIs, Tableau, Power BI, and other tools—without repeated reshaping or one-off pipelines.
This work required thinking carefully about data ownership, transformation boundaries, and how different consumers would interact with the same underlying assets. I served as both technical lead and data architect, ensuring that once data was ingested and shaped, it could support a wide range of analytical and visualization needs with minimal friction.
Leading analytics discovery under constraint
More recently, I’ve led unified dashboard discovery for a ServiceNow team running a portfolio of initiatives whose data lived in disparate systems. There was no shared database, no mandate to build a centralized pipeline, and no consensus on what a consolidated dashboard should look like.
In that context, the work was primarily about discovery. I led interviews, documented workflows, mapped data sources, and built a semantic model that clarified how different initiatives related to one another. I created a proof-of-concept dashboard to test assumptions and demonstrate what a unified view could look like, then handed the model and documentation to a Power BI developer for implementation.
The outcome wasn’t just a dashboard—it was a shared understanding that made delivery possible.
How I work in analytics contexts
Across these engagements, my focus is consistent: clarifying the questions before optimizing the visuals, defining shared meaning before scaling reporting, and using tools like Tableau and Power BI as lenses rather than endpoints.
In many analytics efforts, my work intentionally stops short of full implementation. Once the data shape, semantic model, and narrative are clear, I’m comfortable handing delivery to specialized developers—knowing the foundation will hold.
Analytics succeeds when shape and context are treated as first-class concerns. Everything else builds on that.
Want to talk this through?
If you’re realigning metrics, trying to get reporting back to a place of trust, or framing what to invest in next, I’m happy to help.