Key Components of an Enterprise Data Platform:

• Data Ingestion: Mechanisms to collect and import data from various sources (e.g., databases, APIs, IoT devices).

• Data Storage: Scalable storage solutions (like data lakes and warehouses) that accommodate structured and unstructured data.

• Data Processing: Tools and frameworks for processing data, including ETL (Extract, Transform, Load) and data transformation.

• Data Analytics: Capabilities for analyzing data, including business intelligence tools and machine learning frameworks.

• Data Governance: Policies and practices to manage data quality, security, and compliance.

AI ready: The data platform is ready to add the AI element as plugin.

1. Modularity: The platform should be built with modular components that can be independently developed, deployed, and scaled.
2. Interoperability: Ensure that different components of the platform can communicate seamlessly, supporting various data formats and protocols.
3. Scalability: Design the platform to scale horizontally with increasing data volume and user demands without compromising performance.
4. Security: Implement strong security practices, including data encryption, access controls, and monitoring, to protect sensitive information.
5. Data Quality: Establish mechanisms for continuous data quality assessment and improvement, ensuring reliability and trustworthiness.

Definition:
Data service encompass the processes and practices for managing and maintaining data throughout its lifecycle.

Key Features:

• Data Ingestion Pipelines: Automated workflows for ingesting data from various sources.

• ETL Processes: Structured processes for extracting, transforming, and loading data into storage systems.

• Monitoring and Alerting: Tools to continuously monitor data flows and alert on anomalies or failures.

• Data Quality Checks: Regular assessments to ensure data integrity and accuracy.
• Data Governance Framework: Policies for data stewardship, compliance, and security management.

Project Objectives

The objectives of this initiative were to centralize financial data from over 10 business units, facilitate frequent data ingestion, and provide nearly real-time analytics. Additionally, the project aimed to design a user-friendly data model that empowers power users to create ad-hoc reports through self-service capabilities in Power BI. This approach sought to enhance visibility into financial performance, streamline reporting processes, and support strategic decision-making.

The transformation began with migrating financial data to the Azure Data Platform, utilizing Azure Databricks for seamless integration of data from on-premises SQL databases and Azure Dataverse. This ensured effective aggregation and reliable access to financial data.

For nearly real-time data transformation, the organization leveraged Azure Databricks, enabling efficient processing of incoming data streams. This collaborative tool allowed for quick adjustments to data models as business needs evolved. To maintain data integrity, Unity Catalog was utilized for comprehensive data governance, ensuring effective management of financial data while enhancing security and compliance.

Power BI was implemented to create a comprehensive dashboard with over 200 pages of reports. With a well-designed data model, the dashboard allows finance teams to monitor daily performance metrics with an average loading time of just 5 seconds, ensuring immediate access to insights.

This integrated approach established a streamlined process for daily financial reporting, enabling timely performance monitoring across all business units and allowing teams to focus on analysis and strategy.