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Objective
To enhance data literacy across customer teams in a leading FMCG company, enabling employees at different skill levels to effectively access, analyze, and leverage data for decision-making.
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Solution
The initiative was structured into three distinct training modules, each tailored to different user skill levels:
- Report Viewers - Empowered users to run pre-built reports and apply filters through both native report interfaces and PowerBI.
- Medium Users - Enabled participants to create custom reports using existing datasets, blending in their own data to drive deeper insights.
- Pro Users - Delivered in-depth training on warehouse architecture, naming conventions, data domains, and SQL best practices, fostering self-sufficiency and advanced report development.
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Outcome
This targeted data literacy initiative resulted in significant benefits for the FMCG customer teams:
- Enhanced Data Interpretation: Participants now have the ability to delve deeper into data, translating raw numbers into actionable insights.
- Increased Self-Sufficiency: By equipping teams with the skills to create and modify their own reports, the company has reduced dependency on centralized IT support, leading to quicker decision-making.
- Operational Efficiency: The program streamlined the reporting process, ensuring that all users—from basic report viewers to advanced analysts—can access the information they need efficiently.
- Support for Digital Transformation: The initiative has been a cornerstone in the company’s broader digital transformation strategy, fostering a culture of data-driven decision-making that aligns with modern business needs.
This project exemplifies how tailored training programs can build data literacy, empower teams, and ultimately drive business growth through smarter, faster, and more insightful decision-making.
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Technology
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Power BI
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SQL
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Data Warehousing Principles
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Reporting & BI Tools
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How does it work?
1
Data Sources
- cloud databases
- on-premise database
- Excel files with "pretty" formatting
- csv files
2
Python Script
- processing Excel files with formatting
- conversion to *.csv
3
Linux Pipeline
- Data filtering
4
Staging
- Staging schema data load
5
Aggragation / MDS
- Data aggregation at the month level
- Populating Intermediate Fact Tables
- Loading MD datamarts
- Data transfer to MDS
6
MDS
- MD Enrichment byuser
- Enter MD required for calculations: courses, units. conversion reates.
- Launch dataflow continuation
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DWH Loading
- Calculation and loading of data marts from fact tables and MDS user data
- Recording the download log and the errors that occurred with the reasons
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PowerBI
- PowerBI dataset refresh