This is a summary of a book “Becoming Data Literate” written by David Reed and published by Harriman House in 2021. He is a data, and analytics expert who emphasizes the importance of data in organizations to realize their vision. He suggests that organizations should embrace a data literacy framework and embark on data transformation to become data natives. To do this, organizations should start with a vision and move through five stages to become data natives. This includes inspiring stakeholders, fostering a culture where teams share data and a common language, learning novel approaches for managing data practitioners, and establishing a clear data ethics code. The DataIQ Way framework provides a framework to guide leaders in creating data-driven organizations, incorporating five dimensions: vision, business strategy, value creation, culture, and data foundations. The framework aims to bridge gaps between company culture and data culture, aligning with shared objectives, rewards, and metrics. By embracing a data literacy framework, organizations can become data literate within three years.

This involves understanding the value of data, providing employees with the necessary data, trusting the data, and fostering a culture of data across the organization. Companies can assess their current level of data literacy by assessing both consumers' understanding and alignment of data departments and producers with business goals. As an organization matures, it moves through five stages: "data user," "data driven," "data literate," "data cultured," and "data native." To ensure adoption, organizations should demonstrate the value of data transformation and secure buy-in from stakeholders. The analytics team should engage with stakeholders by collaborating, communicating, championing, and challenging the status quo when data reveals a need for change. By demonstrating the benefits of data, organizations can demonstrate the potential of data transformation and make their strategies more achievable.

Leaders play a crucial role in promoting behavior change in organizations by connecting actions to the organizational vision. They can do this by providing context, connecting actions to desired outcomes, and offering symbolic messages. Building a data culture involves sharing data and a common language for discussing it, fostering a data culture that embraces data democratization and clear data governance. Data leaders should develop a common language for communicating about data and consider introducing data literacy programs. They should also align data strategy with brand values at every consumer touchpoint. Data leaders must learn new approaches for managing data practitioners, prioritizing factors such as social cohesion, perceived supervisory support, information sharing, goal and vision clarity, and trust. They must demonstrate continuous improvement in the performance of the data department through skill development, productivity, and engagement. Data is an intangible asset with no standardized valuation metrics, making it difficult for data leaders to establish metrics to demonstrate its value.

Data practitioners often use metrics to identify their benefits to organizations, but leaders must establish and implement a clear data ethics code. Consumers are becoming cautious about sharing data with corporations, and leaders should embrace transparency around data governance. Research shows that over 20% of consumers share their data without properly reading privacy notices. Data leaders should focus on four principles: autonomy, beneficence, nonmaleficence, and fairness. They should ensure that data-driven decisions are demonstrably fair and that they do not infringe on citizens' rights. Data practitioners lack a system of sanctions for operating outside a professional code of ethics, so leaders must create mechanisms to respond to ethical breaches.