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.