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Develop a comprehensive data warehouse project plan to merge five disparate operational data sources into a unified warehouse and data marts. The plan should cover technical design aspects necessary for future implementation, addressing the integration of three internal and two external data sources. Explain the purpose of the data warehouse, its necessity driven by the company's data volume and fragmentation issues, and outline the system's functions such as data gathering, conformation to standards, and restructuring for reporting and data mining.

The project plan should include requirements gathering, architectural framework, ETL system design, and testing methodologies to ensure successful deployment. Present a detailed problem statement including an introduction of the company and factors necessitating a data warehouse. Construct a requirements document that elaborates on business user needs and technical specifications.

Utilize tools like Excel, Visio, MS Project, or equivalents such as Open Project, Dia, or OpenOffice to produce diagrams and schemas. The diagrams should not exceed five pages and must be accompanied by written explanations. These should include:

  • Business and technical metadata necessary for the data warehouse
  • A data warehouse schema
  • Fact and dimensional tables
  • The data flow diagram illustrating input and output processes

Recommend and justify a Business Intelligence (BI) solution, including a probable dashboard tailored for expert users. The final submission should be 12-15 pages, formatted according to Strayer Writing Standards, double-spaced, Times New Roman size 12, with one-inch margins. Include a cover page (not counted in page length) with the assignment title, student name, professor name, course, and date. All diagrams or charts must be embedded within the document. Support your plan with at least five credible resources, excluding Wikipedia and similar sites. Follow any additional instructions provided by the instructor.

Sample Paper For Above instruction

Introduction and Company Background

ABC Corporation, a medium-sized retail organization, has experienced rapid growth over the past decade, resulting in data fragmentation across multiple operational systems. These systems include sales, inventory management, customer relationship management (CRM), supply chain logistics, and external data sources such as third-party market analysis reports and social media feeds. The proliferation of data from these diverse sources has created challenges in obtaining timely and consistent insights, hampering strategic decision-making. The company recognizes the urgent need for a unified data warehouse to centralize data, improve data quality, and enable efficient reporting and data mining capabilities.

Problem Statement

The company faces significant obstacles due to disparate data sources which have led to inconsistent data quality, redundant data storage, and delayed reporting cycles. The lack of an integrated data platform impairs the organization's ability to perform comprehensive analysis, such as sales trend forecasting, inventory optimization, and customer behavior analysis. The absence of a centralized system causes data silos, complicates data validation, and hampers strategic agility. Therefore, developing a robust data warehouse that consolidates internal and external data sources into a unified repository is essential for improving business intelligence capabilities and supporting data-driven decision-making.

Requirements Document

Business Requirements:

  • Consolidate data from internal systems (sales, inventory, CRM) and external sources (market analysis, social media)
  • Enable real-time and batch data processing
  • Support reporting tools and advanced data mining techniques
  • Ensure data quality and consistency across sources
  • Facilitate user-friendly access for business analysts and executives

Technical Requirements:

  • Design scalable data warehouse architecture to accommodate growing data volume
  • Implement Extract, Transform, Load (ETL) processes with error handling and logging
  • Develop metadata management for data lineage and governance
  • Ensure secure data access compliant with organizational policies
  • Support schema evolution and data versioning

Architectural Framework and Metadata

The data warehouse architecture will utilize a three-tiered architecture consisting of staging, warehouse, and data mart layers. Metadata will include business definitions, data source descriptions, transformation rules, and data lineage. Business metadata will define key metrics and KPIs, while technical metadata will document data transformations, storage formats, and table schemas.

Data Warehouse Schema

The schema will follow a star schema model. The central fact table, SalesFact, will store measures such as total sales, units sold, and profit margins, linked to dimension tables such as ProductDim, CustomerDim, TimeDim, LocationDim, and ExternalDataDim. These dimensions will include descriptive attributes for filtering and analysis.

Fact and Dimensional Tables

  • Fact Table: SalesFact (sales_id, product_id, customer_id, date_id, location_id, sales_amount, units_sold, profit)
  • Dimension Tables:
    • ProductDim: product_id, product_name, category, supplier
    • CustomerDim: customer_id, name, segment, demographic_info
    • TimeDim: date_id, date, week, month, quarter, year
    • LocationDim: location_id, store_name, region, country
    • ExternalDataDim: data_id, source_name, data_type, data_value, timestamp

Data Flow and Integration

The data flow begins with extraction from internal operational systems and external data feeds, followed by transformation processes that conform data to organizational standards, such as standardized formats and coding schemes. Data validation occurs at each stage to ensure accuracy. Transformed data is loaded into the warehouse, where it is structured for accessibility. End-users access data through reporting tools and dashboards, which provide real-time analytics and insights.

Business Intelligence Solution and Dashboard Design

The recommended BI solution includes platforms such as Tableau or Power BI, which are capable of connecting directly to the data warehouse for real-time analytics. The dashboard will feature key metrics like sales performance, inventory levels, customer insights, and external market trends. Visualizations include line graphs, pie charts, heat maps, and KPI indicators, providing dynamic and interactive exploration capabilities for expert users to facilitate informed decision-making.

Conclusion

This project plan aims to guide the systematic development of a data warehouse that effectively integrates multiple data sources to support strategic BI initiatives. By adopting a well-structured architecture, comprehensive metadata management, and robust ETL processes, the organization will attain improved data consistency, faster reporting, and enhanced analytical capabilities, ultimately leading to better business outcomes.

References

  • Inmon, W. H. (2005). Building the Data Warehouse. John Wiley & Sons.
  • Golfarelli, M., & Rizzi, S. (2009). Data warehouse design based on XML data. Data & Knowledge Engineering, 68(11), 1223-1242.
  • Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit. Wiley.
  • Watson, H. J., & Wixom, B. H. (2007). The Current State of Data Warehousing. Communications of the ACM, 50(9), 96-102.

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