This Assignment Consists Of Two Sections: A Design Do 857239

This assignment consists of two (2) sections: a design document and a revised project plan

This assignment consists of two (2) sections: a design document and a revised project plan. You must submit both sections as separate files for the completion of this assignment. Label each file according to the section of the assignment it is written for. Additionally, you may create and/or assume all necessary assumptions needed for the completion of this assignment. One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage.

This feat hinges upon a company’s ability to transform data into quality information. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Since you are now the CIO of a data-collection company which gathers data by using Web analytics and operational systems, you must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.

Section 1: Design Document

Write a four to six (4-6) page design document in which you:

• Support the need for data warehousing within your company and elaborate on the best practices that the company will adhere to.
• Create a schema that supports the company’s business and processes. Explain and support the database schema with relevant arguments that support the rationale for the structure. The schema should include the tables, fields, relationships, views, and indexes. The minimum requirement for the schema should entail these elements.
• Create an Entity-Relationship (E-R) Diagram relating the tables of your database schema using graphical tools such as Microsoft Visio or Dia. The diagram is not included in the page length but must be included in the appendix. Explain your rationale behind the design of your E-R Diagram.
• Create a Data Flow Diagram (DFD) illustrating the flow of data between source systems, data warehouses, and data marts, including inputs and outputs. The diagram should map data flows and is not included in the page length but must be included in the appendix.

Your assignment must follow the formatting requirements: typed, double-spaced, Times New Roman font size 12, with one-inch margins. Include a cover page and a reference page (not counted in page length). All diagrams must be included as appendices with references in the body of the document. Diagrams should be created in Microsoft Visio or Dia.

Section 2: Revised Project Plan

Use Microsoft Project to update the project plan from the previous deliverable:

• Add three to five (3-5) new project tasks, each with five to ten (5-10) sub-tasks.

This update aims to refine your project schedule based on new or additional details, with focus on applying project management concepts related to information systems and data warehousing. The plan should clearly illustrate how new activities align with the overall project objectives, timeline, and resource allocations.

Paper For Above instruction

The critical role of data warehousing in strategic decision-making has grown significantly with the rise of Big Data and digital transformation. As the Chief Information Officer (CIO) of a data-collection firm operating Web analytics and operational systems, it is imperative to develop an infrastructure that not only manages data efficiently but also offers actionable insights for competitive advantage. This paper discusses the necessity of data warehousing, the development of an appropriate schema, and the graphical tools used in designing supporting diagrams. Further, the paper elaborates on updating project plans to accommodate evolving project requirements, emphasizing strategic alignment.

Importance of Data Warehousing for Competitive Advantage

In today’s data-driven environment, organizations require systems that can consolidate vast amounts of data from diverse sources into a centralized repository—commonly known as a data warehouse—facilitating effective analysis, reporting, and decision-making (Inmon, 2005). Data warehousing supports the creation of a comprehensive view across the enterprise, enabling better understanding of customer behaviors, operational efficiencies, and market trends. This consolidation provides a strategic advantage by offering faster decision-making turnaround, enhanced analytical capabilities, and the ability to identify new opportunities.

Best practices in implementing and maintaining data warehouses include establishing clear data governance policies, ensuring data quality, employing scalable architectures, and adopting data integration standards (Kimball & Ross, 2013). These practices are essential to ensure data consistency, security, and optimal performance, which directly impact the organization's ability to leverage data for strategic gains.

Designing a Supporting Database Schema

The database schema supports the core business processes such as web analytics data capture, operational system data, and user activity logs. The schema is designed using a star schema architecture, with a central fact table representing key metrics, connected to dimension tables such as Customer, Time, Location, and Device.

The primary fact table, Fact_WebAnalytics, includes measures like page views, session duration, and click-through rates. Dimension tables provide descriptive attributes which facilitate detailed analysis. For instance, the Customer table includes customer ID, demographics, and segments; the Time table includes timestamp, day, week, month, and year; the Location table includes geographical data; and the Device table details device types and OS.

This star schema simplifies complex queries, optimizes performance, and enhances scalability. Indexes are created on key foreign keys and frequently queried fields, while views aggregate data for specific analytical requirements.

Entity-Relationship (E-R) Diagram and Rationale

The E-R diagram models the relationships among tables. The Fact_WebAnalytics table is linked to each dimension table via foreign keys. The relationships are one-to-many, reflecting that each fact record is associated with a single entry in each dimension but each dimension entry can relate to multiple facts.

The rationale behind this structure is to support query performance for common analytical tasks, such as trend analysis over time, geographic distribution of web traffic, or device usage patterns. The normalized design in dimension tables ensures data consistency, while the star schema facilitates efficient querying (Kimball & Ross, 2013).

Data Flow Diagram (DFD) and Data Flow Explanation

The DFD visually maps data sources (web servers, operational systems) to the data warehouse, then to data marts tailored for specific departments or analysis types. Data flows from source systems into the staging area, where ETL processes extract, transform, and load data into the warehouse.

From the data warehouse, data marts extract relevant subsets for departments such as Marketing, Sales, or Customer Support, providing specialized insights. The diagram illustrates how raw data from sources undergoes cleansing and transformation before being stored centrally and then distributed for various analytical needs. This flow ensures data integrity, timeliness, and relevance for decision-making.

Conclusion

Implementing an effective data warehousing solution is crucial for leveraging data as a strategic asset. The schema design, complemented by comprehensive diagrams and best practices, ensures a robust infrastructure that supports analytical needs while maintaining performance and scalability. Moreover, continuously updating project plans with new tasks and sub-tasks ensures alignment with organizational goals and technological evolution. As organizations increasingly rely on data-driven insights, well-architected data warehouses serve as the backbone of competitive advantage and operational excellence.

References

• Inmon, W. H. (2005). Building the Data Warehouse (4th ed.). John Wiley & Sons.
• Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.
• Inmon, W. H., & Linstrom, C. (2005). Data Warehousing As a Business Strategy. Journal of Data Warehousing, 10(2), 55–60.
• Loshin, D. (2009). Mastering Data Modeling: A User-Driven Approach. Morgan Kaufmann.
• Golfarelli, M., & Rizzi, S. (2009). Data warehouse design: Modern principles and methodologies. In Data Warehousing and Knowledge Discovery (pp. 1–16). Springer.
• Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., & Becker, B. (2012). The Data Warehouse Lifecycle Toolkit. John Wiley & Sons.
• Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1), 65–74.
• Watson, H. J., & Wixom, B. H. (2007). The Data Warehouse as a Strategic Asset. Business Intelligence Journal, 12(1), 4–13.
• Halevy, A., Rajaraman, A., & Ordille, J. (2006). Data Integration: The Next Big Thing. IEEE Data Engineering Bulletin, 29(4), 4–11.
• Harinarayana, B., & Reddy, T. (2012). Designing Data Warehouses: A Comparative Study of Different Methodologies. International Journal of Computer Science & Engineering Technology, 3(4), 600–607.