Data Integration And ETL Process For Business Analysis

Data Integration and ETL Process for Business Analysis

Data Integration and ETL Process for Business Analysis

This project aims to develop a comprehensive data mart that involves extracting data from multiple sources, cleaning, transforming, and loading it into a structured database for analysis. The primary focus is to practice the ETL (Extract, Transform, Load) process, ensuring data quality and integration for meaningful business insights. By working through this project, I intend to understand the nuances of data cleaning, normalization, and consolidation, which are critical in creating reliable data warehouses.

The core goal is to address a specific business problem, such as understanding customer behavior, sales trends, or operational efficiency, by aggregating relevant data. The dataset will include at least 5,000 records but not exceed 100,000 records to maintain manageability. Data will be sourced from at least three different datasets, which may include internal databases or publicly available sources, ensuring data relevancy and diversity. The data will be extracted as flat files or from relational databases, then cleaned—such as unifying identifiers, standardizing null values, and validating address components—to ensure data integrity. Keys such as primary and foreign keys will be identified and established for proper relational links between data tables.

The transformation phase will involve converting data units for consistency, creating surrogate keys, generating aggregated values, and deriving new calculated columns. Additionally, two new columns will be added to each dataset—one to record the current date and time of the ETL process, and another to indicate the data source file name. The process will also incorporate at least three data transformation techniques, such as data conversion, derived columns, lookup, and merge join, to enhance data integration and quality. The integrated dataset will be stored in SQL Server tables, facilitating efficient querying and analysis, which would be considerably more cumbersome with Excel alone. This approach ensures a scalable, repeatable process and supports complex decision-making through well-structured data.

Paper For Above instruction

In this project, I plan to create a data mart that integrates data from multiple sources to support business decision-making. The motivation stems from the need to analyze cross-source data efficiently, gaining insights such as customer segmentation, sales performance, and operational efficiencies. I anticipate that the ETL process will present challenges, particularly during data cleaning—such as handling inconsistent identifiers, null values, and address validation—and during transformation, especially in harmonizing units and creating surrogate keys. Ensuring seamless data integration from heterogeneous sources will require careful planning and multiple transformation steps.

The data sources include internal company datasets and publicly available datasets that cover customer information, sales transactions, and demographic data. I plan to process approximately 20,000 to 25,000 rows collectively, ensuring enough data for meaningful analysis without overwhelming system resources. Each dataset will be assigned a primary key based on unique identifiers—for example, customer ID, transaction ID, or ZIP code. These keys will facilitate establishing relationships among the tables, which will be crucial during the data loading process into SQL Server.

During the transformation phase, I will standardize measurement units, such as currency or weight, to enable accurate comparisons and aggregations. I will create surrogate keys where natural keys are insufficient or inconsistent across datasets. Additionally, I will derive new variables—such as total sales per customer or average purchase value—and incorporate two auxiliary columns: one recording the timestamp of the ETL operation and another indicating the source file name. These additions enhance data traceability and support auditing requirements. The final data warehouse will be designed with relational tables linked through foreign keys, enabling complex queries and robust reporting.

This project will utilize tools such as SQL Server Management Studio and SSIS within Visual Studio for data processing, ensuring a professional-level ETL execution. The integrated data will be stored in normalized tables, providing flexibility for various analytical queries. This approach offers significant advantages over alternative methods like Excel, including improved scalability, data integrity, and automation. Using SQL Server for data storage and transformation leverages the power of relational databases, making it possible to handle larger datasets efficiently, perform complex joins, and maintain data consistency across updates. In turn, this enhances decision support capabilities, allowing for detailed reports and advanced analytics that inform strategic business decisions.

References

  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley & Sons.
  • Rob, P., & Coronel, C. (2007). Database Systems: Design, Implementation, and Management. Thompson/South-Western.
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  • Byrd, T. A., & Turner, D. E. (2001). Achieving Supply Chain integration through information technology capabilities. Journal of Business Logistics, 22(2), 33-53.
  • Power BI Documentation. Microsoft. https://docs.microsoft.com/en-us/power-bi/
  • Microsoft SQL Server Documentation. https://docs.microsoft.com/en-us/sql/sql-server/
  • SAP Data Services. SAP. https://www.sap.com/products/data-services.html
  • Kimball, R. (1996). Data Warehouse Tools: Building Dimensions, Data Massages, and Fact Tables. John Wiley & Sons.
  • Agrawal, D., & Ranjan, R. (2014). Data cleaning techniques for data warehouses. International Journal of Data Warehousing and Mining, 10(2), 1-22.

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