Techcom Sales Analysis Insights

Data Quality Issues

The report appears to contain several data inconsistencies and formatting problems that make analysis challenging:

1. Inconsistent units: Mixing of “K” (thousands), “M” (millions), and raw numbers without clear standardization
2. Duplicate sections: Multiple “Sales Quantity by Market” tables with conflicting data
3. Missing data: No data for 2019, and some tables have incomplete or unclear headers
4. Formatting errors: Apparent copy-paste issues in the “Total Amount” rows with repeating values

Observable Trends

Despite the data quality issues, some patterns emerge:

1. Revenue Growth:
   • Total revenue reported as $12.83M,butindividualentriesshowmuchhighervalues(e.g.,$51.004M)
   • Potential significant growth from 2017 ($10,000K) to later years (values in millions)
2. Customer Analysis:
   • Top customers show values in the $50M+ range (Electricalars Store, Electricaltylist, etc.)
   • “Production” appears frequently in customer data, possibly indicating a key industry segment
3. Seasonality:
   • The monthly breakdown shows consistent values across months ($50.334M repeating), suggesting either data errors or extremely stable sales

Recommendations

1. Data Cleaning:
   • Standardize all monetary values to one unit (preferably millions)
   • Resolve duplicate tables and conflicting entries
   • Investigate the repeating $50.334M values for potential data entry errors
2. Additional Analysis Needed:
   • Verify the accuracy of the $12.83M total revenue against the detailed numbers
   • Examine why 2019 data is missing
   • Investigate the “Production” category in customer data
3. Visualization Improvements:
   • The current table format makes trend analysis difficult
   • Recommend adding time series charts for revenue trends
   • Create clear customer segmentation visualizations

The most critical next step would be to validate the data quality before drawing firm conclusions, as the current inconsistencies make reliable analysis difficult. The presence of duplicate tables and repeating values suggests potential errors in the underlying data or export process.