- Understanding Data Journeys: The data journey involves defining goals, refining data, enriching it, and sharing insights. It often includes deploying machine learning models and iterating through multiple phases to enhance results.
- Crucial Aspects: Focus on product thinking, data science for insights, and data engineering for reliability. Balancing these elements ensures a streamlined process and effective outcomes.
- Iterative Approach: Start with basic models or prototypes, refine through iterations, and seek feedback to improve accuracy and functionality. This approach helps minimize cycle times and accelerates user access to valuable insights.
- Early User Access: Provide business users early access to lower-level systems or dashboards to gather quick feedback and make necessary adjustments, which reduces overall development time.
- Integration of Data Science and Engineering: Bridging gaps between data science and data engineering ensures models are effectively operationalized, enhancing overall efficiency and output quality.
- Model Experimentation: Use frameworks for safe model rollouts, including feature toggles and A/B testing, to minimize risk and maintain business continuity during updates.
- Cost Management: Prioritize efforts based on return on investment. Utilize cost-saving mechanisms like spot instances for processing jobs and focus on core business aspects rather than peripheral data systems.
- Regulatory Compliance: Address regulatory requirements from the start. Consider using anonymized data and secure environments to manage personal information responsibly.
- Data Quality and Downtime: Implement data quality checks and monitoring systems to ensure data accuracy and system reliability, preventing issues from impacting downstream processes.
