Data and Insights
Finding the Story in the Data
Tools
SQL · Snowflake · Excel · Data Visualization
Working with large datasets at ComEd taught me that data only becomes valuable when you know what to look for. I used SQL, Snowflake, and Excel to explore operational and program data, uncover patterns, and translate complex information into insights that could be clearly communicated to business stakeholders.
My Data Process
RAW DATA →
Multiple datasets and sources
QUERY →
SQL + Snowflake
ANALYZE →
Trends · Patterns · Comparisons
VISUALIZE
Excel · Dashboards · Reporting· Powerpoint
SQL and Snowflake
Working with reliability fleet vehicle datasets at ComEd, I learned that having access to data isn't the same as understanding it. I used SQL, Snowflake, and Excel to dig into vehicle usage, maintenance patterns, and program costs, not just to pull numbers, but to figure out which patterns actually mattered and why. That distinction, knowing what's worth investigating versus what's just noise is what turns a dataset into a decision. It's a skill I now apply anytime I'm handed a large, messy dataset: start by asking what question the data is supposed to answer, then let that question guide the analysis instead of getting lost in the data itself.
Excel and Powerpoint
After analyzing the data, I used Excel and PowerPoint to organize the findings into dashboards, reports, and presentations, making the results easier to interpret. This step wasn't about cramming in as much data as possible. It was about deciding which numbers actually mattered and presenting them in a way that supported the conversation around them, rather than replacing it. That distinction matters because a dashboard full of metrics doesn't help anyone make a decision, but a few well-chosen numbers, framed clearly, do. It's a mindset I carry into any reporting work now: before building a slide or a chart, I ask what decision it needs to support, and let that shape what actually makes the cut.
Insight to Action
The most important part of the process was translating analysis into something actionable. By combining technical analysis with business context, I learned to move beyond simply identifying patterns in the data to explaining what those patterns meant for program performance and future decision-making.
Takeaway
Data became less about the numbers themselves and more about the questions I could answer with them. This experience strengthened my ability to approach large datasets with curiosity, identify what matters, and translate technical findings into actionable insights for others.