Fastbridge Data Mapping
INTRODUCTION
The customer is a public school district in Minnesota that serves several cities. The district has over 30 schools and over 30,000 students. The customer is committed to providing a high-quality education for all students and is also committed to diversity and equity. The district offers a variety of programs and services to support student learning, including magnet schools, early childhood education programs, and special education services.
CUSTOMER PROBLEM
The customer faced the challenge of consolidating student assessment data from multiple sources into a single master file. Assessment data for different subjects, including Math, Reading, CBMR-English, and Early Reading English, were stored in separate files, leading to data fragmentation and difficulties in comprehensive analysis. Each subject file required specific transformations, including data cleansing, formatting, and mapping to a standardized structure.
SOLUTION
To address this challenge, we implemented an ETL-based solution to consolidate and efficiently transform the data:
- Data Extraction: The ETL process began by extracting data from the four subject-specific files. Each file contained student assessment scores, proficiency levels, and other relevant details.
- Data Transformation: Once the data was extracted, subject-specific transformations were performed on each dataset. Data cleansing, formatting, and standardization were applied to ensure consistency across subjects.
- Data Mapping and Merging: The transformed data from each subject was then mapped to a unified structure compatible with the final export file in Excel format. The ETL process expertly merged the transformed datasets into a single comprehensive dataset.
- Generating Final Excel Report: Using the transformed and merged dataset, the ETL process generated a final Excel report that offered a holistic view of student assessment data, organized by subject, student ID, and other key attributes.
- Automated Schedule: The ETL process was set on an automated schedule to run at regular intervals, ensuring that the final Excel report was updated with the latest assessment data
RESULTS
Data Accessibility and Consistency: The final Excel report offered easy access to comprehensive, organized, and standardized assessment data across subjects.
Improved Data Quality: The data transformations and cleansing steps in the ETL process improved data quality and accuracy, minimizing errors and inconsistencies.
CONCLUSION
The implementation of ETL efficiently merged and transformed student subject data, providing a unified Excel report. The automated process and enhanced data accuracy contributed to improved data management and facilitated comprehensive analysis for the customer.