Medallion.Platinum

Overview:

The Platinum Layer is the most advanced tier in the data warehouse, dedicated to performing complex business transformations and machine learning (ML) exercises. This layer leverages advanced analytics to generate insights that support budget, forecasting and predictive analytics. The resulting transformed data and predictive models are then returned to the Gold Layer for delivery.

Key Characteristics:

1. Advanced Business Transformations:
   • Complex Transformations: The Platinum Layer handles intricate data transformations that go beyond the scope of the Silver and Gold layers. These transformations include aggregations, financial modeling, and scenario analysis to support strategic decision-making.
   • Business Logic Integration: This layer integrates sophisticated business logic to ensure that the data transformations align with organizational goals and strategies.
2. Machine Learning and Predictive Analytics:
   • ML Models: The Platinum Layer is where machine learning models are developed, trained, and validated. These models use historical data to make predictions about future trends, such as sales forecasts, customer behavior, and budget requirements.
   • Predictive Analytics: By applying ML algorithms, the Platinum Layer generates predictive insights that help the organization anticipate future scenarios and make data-driven decisions.
3. Integration and Data Flow:
   • Returning Views to Gold Layer: The outputs from the Platinum Layer, including transformed views and predictive insights, are returned to the Gold Layer. This ensures that these advanced analytics are readily accessible for consumption and integration into business processes.
   • Continuous Feedback Loop: The Platinum Layer operates in a continuous feedback loop, where insights and models are constantly refined based on new data and changing business conditions.
4. Technology and Tools:
   • Advanced Analytics Platforms: The Platinum Layer utilises advanced analytics platforms such as Databricks, Apache Spark, and other ML frameworks to handle large-scale data processing and model training.
   • Collaboration and Version Control: Data scientists and analysts work collaboratively, using tools like VSCode, Git, and DevOps for version control and collaborative development of ML models and transformations.

Workflow Summary:

1. Data Ingestion: Data from the Gold Layer and other sources is ingested into the Platinum Layer for advanced processing.
2. Business Transformations: Complex business transformations are applied to the data to generate actionable insights.
3. Machine Learning: ML models are developed and applied to predict future trends and support strategic planning.
4. Returning to Gold Layer: The transformed data and predictive insights are returned to the Gold Layer for delivery.
5. Continuous Improvement: The process iterates, with models and transformations refined based on new data and feedback.

Importance:

• Strategic Insights: The Platinum Layer provides deep insights and foresight, enabling the organization to plan strategically and respond proactively to future challenges and opportunities.
• Advanced Analytics Capabilities: By incorporating ML and advanced business transformations, the Platinum Layer enhances the organization’s analytics capabilities, driving innovation and competitive advantage.
• Integrated Data Delivery: By returning the results to the Gold Layer, the Platinum Layer ensures that advanced insights are seamlessly integrated into the broader data delivery framework, making them accessible for decision-makers.

RAID

Risks:

• Model Accuracy: Inaccurate or biased machine learning models can lead to incorrect forecasts and analytics.
• Data Privacy: Handling sensitive data for predictive analytics requires stringent privacy controls.
• Resource Intensive: Machine learning and business transformation processes can be resource-intensive.

Issues:

• Data Transformation: Ensuring accurate and efficient data transformations for business analytics.
• Model Maintenance: Regularly updating and maintaining machine learning models to ensure their relevance and accuracy.
• Feedback Loops: Implementing effective feedback loops to continuously improve model performance and business transformations.

Dependencies:

• Machine Learning Models: Dependence on accurate and validated machine learning models.
• Data Scientists and Analysts: Collaboration with skilled data scientists and analysts.
• Advanced Analytics Tools: Availability of advanced analytics tools and platforms.

Assumptions:

• Model Accuracy: Assumption that machine learning models provide accurate and reliable predictions.
• Data Privacy Compliance: Assumption that all data processing complies with privacy regulations.
• Resource Availability: Assumption that adequate resources are available for running complex analytics tasks.

Opportunities:

• Predictive Analytics: Utilizing advanced machine learning models for predictive analytics and forecasting.
• Business Transformation: Leveraging data insights for strategic business transformations and decision-making.
• Collaboration with Data Scientists: Enhancing collaboration with data scientists to develop innovative solutions.

Mitigations:

• Model Validation: Implementing rigorous validation and testing of machine learning models to ensure accuracy.
• Data Privacy Compliance: Ensuring compliance with data privacy regulations and best practices.
• Resource Management: Optimising resource usage for machine learning processes to avoid excessive costs.