Billing and Consumption Intelligence Dashboard

Overview

An operational reporting layer built on anonymized smart-meter and billing data that helped operations track billing performance, consumption trends, and data quality without rebuilding reports each cycle.

Business problem

Operations relied on monthly Excel exports that were slow to prepare and hard to compare across regions. Consumption trends, billing exceptions, and data-quality issues were only visible after significant manual work.

Responsibilities

• Analyzed customer, smart-meter, consumption, operational, and billing records using SQL, Python, Excel, and Power Query
• Investigated inconsistencies between meter readings, consumption data, customer accounts, and billing amounts
• Developed validation processes for missing data, duplicates, unusual usage, and billing exceptions
• Created dashboards and operational reports for billing performance, consumption trends, and exception tracking
• Automated recurring data-cleaning and reporting workflows
• Supported root-cause analysis and communicated findings to operational stakeholders

Dataset

Approximately 10,000 anonymized records covering customer accounts, smart-meter readings, consumption history, and billing exceptions across multiple regions and billing periods.

Approach

Profiled the raw data in Python and Excel to understand quality issues, standardized the schema, then designed Power BI dashboards around the three questions operations asked most often: consumption trends, region comparisons, and billing anomalies.

Solution

A Power BI workbook with a consumption overview, a region comparison view, and an anomaly view that highlights records outside expected ranges. Filters cover meter type, region, and billing period.

Results

Monthly reporting effort dropped by about 30%. Operations caught recurring billing anomalies that had been hidden in the spreadsheets, leading to corrected invoices and cleaner downstream data.

Challenges

Reconciling AI-based meter reads with legacy manual entries required a careful validation layer to avoid over-flagging edge cases.

Limitations

Anomaly thresholds were rule-based and needed periodic re-tuning as consumption patterns shifted by season and region.

Lessons learned

Investing in upstream data-quality checks paid back many times over. Every hour spent on validation saved several hours of report firefighting later.

Future improvements

Layer in an ML-based anomaly model to complement rule-based flags and add a scheduled data-quality scorecard for each region.