An Organization Has Several Operational Systems Customer Rel

An Organization Has Several Operational Systems Customer Relationship

An organization has several operational systems: Customer Relationship Management (CRM) for marketing and sales, Enterprise Resource Planning (ERP), and Supply Chain Management (SCM). They also have external customer data. A wide variety of departments utilize this data: sales, marketing, procurement, human resources, R&D, and senior management. Design a high-level conceptual view of a data warehouse using Microsoft ® Visio ® that shows the following: Integration layers The data warehouse Recommended data marts Include arrows to show ETL (extract, transform, and load) locations and direction.

Paper For Above instruction

Introduction

In the contemporary organizational landscape, data has become a critical asset that informs strategic decision-making across various departments. Integration of data from multiple operational systems such as Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), and Supply Chain Management (SCM) into a unified data warehouse facilitates comprehensive analysis and enhances organizational responsiveness. This paper presents a high-level conceptual design of a data warehouse architecture tailored for an organization with diverse systems and extensive data utilization.

High-Level Conceptual Data Warehouse Architecture

The architecture begins with an integration layer that consolidates data from multiple source systems. Visualizing this architecture using Microsoft Visio involves creating a diagram that highlights the key components: integration layers, the core data warehouse, ETL processes, and data marts.

Integration Layers

The integration layer acts as the central hub where data from CRM, ERP, SCM, and external sources converge. This layer encompasses mechanisms for data extraction and initial transformation to prepare for loading into the warehouse. It ensures data consistency, quality, and standardization, accommodating the heterogeneity of source systems. The integration layer typically includes staging areas, where raw data is temporarily stored for processing.

The Data Warehouse

Positioned at the heart of the architecture, the data warehouse serves as the repository that stores integrated, cleaned, and consolidated data. From this central storage, analytical queries and reporting are performed. The warehouse supports multidimensional analysis and provides a single source of truth for organizational data. Its design ensures data integrity, historical tracking, and scalability.

Recommended Data Marts

Supplementing the central data warehouse are specialized data marts. These are subsets of the warehouse, tailored to specific departmental needs:

- Sales and Marketing Data Mart: Focused on customer behavior, sales performance, and marketing campaigns.

- Finance Data Mart: Contains financial metrics, budgeting, and accounting data.

- Procurement Data Mart: Supports procurement analytics, supplier management, and inventory control.

- Human Resources Data Mart: Encompasses employee data, payroll, and recruitment analytics.

- R&D Data Mart: Facilitates analysis of research projects, product development, and innovation metrics.

Each data mart derives data from the central warehouse through automated ETL processes and is optimized for rapid querying and reporting within its domain.

ETL Processes and Directions

Arrows in the Visio diagram illustrate the flow of data:

- From source systems (CRM, ERP, SCM, external data sources) into the staging area.

- From staging, after extraction and transformation, into the data warehouse.

- From the data warehouse to each data mart.

The ETL arrows demonstrate the directional flow of data, emphasizing the process of extracting from sources, transforming for consistency and quality, and loading into the warehouse and marts.

Conclusion

A high-level conceptual architecture of a data warehouse, as described, provides a strategic blueprint for integrating diverse organizational data sources. The integration layer ensures standardized data processing, the data warehouse offers centralized storage for analytics, and specialized data marts enable focused departmental insights. Visualizing this architecture in Microsoft Visio with clear ETL flow arrows facilitates stakeholders’ understanding and helps guide implementation.

References

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