“Humanizing Data Strategy: Leading Data with the Head and the Heart” by Tiankai Feng focuses on a people-centered approach to data strategy. The book introduces the Five Cs Framework, which highlights five key areas of focus: Competence, Collaboration, Communication, Creativity, and Conscience. Feng defines data strategy as: “a long-term plan that defines the people, processes, and technologies to create, process, and use data to intentionally drive value in a meaningful, secure, and transparent way.”
While people, processes, and technologies are all critical to data strategies, this book emphasizes the people aspect, which is addressed through the 5C framework. The framework seeks to ensure that human elements – such as skills, collaboration, and ethics – are fully integrated into how data strategies are developed and executed, highlighting the importance of fostering a data culture built on trust, collaboration, and creativity.
Competence
This sub-chapter emphasizes the need for continuous skill development and competence building within organizations. Feng advocates for aligning data skills with business needs and encourages fostering a learning culture. Investing in academy concepts is essential for accelerating speed and innovation in areas critical to business success, and these competencies should be developed internally.
Both business professionals and data professionals need to expand their skill sets: business professionals must enhance their data literacy, while data professionals should deepen their understanding of business operations. This alignment is crucial to effectively bridge the gap between technology and business needs. It is necessary to approach data with a strong business acumen, ensuring that technological solutions are always informed by the strategic goals of the organization.
Collaboration
Collaboration is about breaking down silos between departments to ensure cross-functional teams work together seamlessly. Feng emphasizes that employee performance should be measured, in part, by how well they collaborate around data. Different roles, such as data producers, data processors, and data consumers, must work closely together to maximize the value of data. Communities of practice can also play a significant role in enhancing collaboration.
A recurring debate in data strategy is the balance between service vs. self-service or, more broadly, centralization vs. decentralization. As with many things, the optimal approach often lies somewhere in the middle, with a focus on co-creation. Frameworks like Data Mesh and Data Fabric can help organizations navigate this balance by identifying the best ways to manage and share data efficiently while fostering collaboration across different teams.
Communication
This sub-chapter delves into the importance of tailoring communication strategies for different stakeholders. He outlines a toolkit that includes factors such as audience targeting, frequency of communication, and choosing the right communication channels. Feng also warns against “toxic positivity” – overlooking real issues in favor of an overly optimistic view – and stresses the need for balanced, honest communication.
Creativity
Creativity manifests in different forms, including:
- Spontaneous vs. solution-oriented creativity
- Incremental vs. disruptive creativity
Employees should be encouraged to experiment and think outside the box. It is essential to create an environment where no one fears failure, as this fosters innovation and out-of-the-box thinking. Creativity needs to be nurtured and developed like a muscle – from cultivating the right mindset, to regular practice, until it becomes a habit. By doing so, organizations can continually drive innovation and adapt to changing challenges with creative solutions.
Conscience
This chapter explores the ethical considerations in data strategy. Feng advocates for embedding key ethical principles – such as fairness, privacy, and diversity – into every aspect of data work. He emphasizes that data governance should prioritize these values to ensure that data-driven decisions benefit not only the organization but also broader society. Additionally, Feng encourages organizations to approach data usage with sustainability in mind, considering the long-term impacts of their data practices on both the environment and future generations.
My Take
Many insights in the book, such as “Think big, start small,” may not be entirely new, but throughout Humanizing Data Strategy, Tiankai Feng seamlessly integrates his personal experiences as both a human being and a data management professional. This blend of personal anecdotes and professional insights makes complex data strategies more accessible and engaging for readers.
In the data field, people are often overlooked, with the focus placed heavily on technologies or tools. As someone who gives lectures on data management at DHBW, I often pose the question to my students: “What do you think is the biggest challenge in data?” While mature technologies and tools are essential, most failures in data strategy stem from people and organizational issues. Common challenges include silos, resistance to change, a lack of leadership, and an ignorance of risks – just a few examples of human-related obstacles that can derail success.
Talking about tools and technologies is the norm, as these topics are concrete and easier to address. However, discussing human and organizational issues is much more challenging, but also far more necessary for achieving meaningful data outcomes.
“Humanizing Data Strategy: Leading Data with the Head and the Heart” by Tiankai Feng is an excellent resource for anyone involved in data management, offering valuable tools for both seasoned professionals and newcomers alike. It provides a fresh perspective on data strategy, emphasizing the often-neglected human element, making it a significant contribution to the field. The 5 Cs framework – Competence, Collaboration, Communication, Creativity, and Conscience – serves as a valuable guide for focusing on the people side of data strategy.
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