When faced with multiple business opportunities, it's crucial to prioritize and select the most promising initiatives. To do this, data leaders should adopt a portfolio management approach. This involves:

• Prioritization and Sequencing: Identify the most impactful opportunities and sequence them based on potential value and available resources.
• Resource Management: Allocate resources effectively to ensure the successful execution of prioritized initiatives.
• Dependency Management: Identify and manage dependencies between initiatives to avoid bottlenecks and ensure smooth progress.
• Value Management: Establish clear methods for assessing and measuring the value generated by data products.

Value management consists of two steps:

1. Value Assessment: Estimate the potential value of each business opportunity before development begins.
2. Value Measurement: Track and measure the actual value generated by data products after implementation.

The measurement aspect can be challenging, but it's crucial for optimizing the portfolio and demonstrating the impact of data initiatives. It involves understanding the relationship between the output of an analytical product (e.g., model accuracy) and its impact on business outcomes (e.g., revenue).

Various approaches can be used to measure value, including expert estimates, KPIs, experiments, benchmarking, customer feedback, and econometrics. Data leaders need to be familiar with these approaches and have a clear strategy for value measurement before development begins.

Key Takeaways:

• Prioritize business opportunities based on potential value and strategic alignment.
• Understand the different types of data products and their potential contribution to unlocking value.
• Adopt a portfolio management approach to optimize resource allocation and dependency management.
• Establish a clear strategy for measuring the value generated by data products.
• Use various approaches to measure value, including expert estimates, KPIs, experiments, benchmarking, and customer feedback.

By implementing these practices, data leaders can effectively manage their data product portfolio, maximize value creation, and demonstrate the tangible impact of their initiatives on the organization's bottom line.