Book Review: Humanizing Data Strategy by Tiankai Feng
As much as I love sharing my own blueprints for Digital Transformation, I believe there's wisdom in learning from others who create exceptional approaches. This week, instead of focusing on my frameworks, I want to introduce you to a blueprint that resonated with me. Sometimes, the best way to build your transformation toolkit is to study what others have mastered.
When we talk about Digital Transformation, we often jump straight to discussing technology. But the most successful transformations I've seen always start with people. That's why Tiankai Feng's "Humanizing Data Strategy" immediately caught my attention—it focuses on building strategies people actually embrace.
While the topic is data strategy, I quickly realized the principles apply to virtually any technological transformation. Whether you're implementing AI, automation, or new ERP systems, humans always play the central role in determining success or failure. The human-centered approach Tiankai advocates transcends the data domain and offers a blueprint for any digital initiative.
Let me walk you through why this book matters for your transformation journey and the key insights that could transform your approach.
Why This Book Matters
If you're into Digital Transformation, you've probably heard the statistics: most initiatives fail to deliver on their promises. Companies invest in powerful data tools, create dashboards, implement new processes... and then watch as adoption lags, insights go unused, and the promised ROI fails to materialize.
The core issue? Most strategies focus exclusively on technology and processes while treating people as an afterthought.
Tiankai's book flips this approach on its head. Instead of starting with technology, he advocates beginning with empathy, creativity, and collaboration. This matches my experience—technical excellence means nothing if your team doesn't understand, trust, or use the tools you've provided.
A humanized data strategy empowers people through empathy, creativity, and collaboration. Technology should support and not replace humanity.
Key Insights That Will Change Your Approach
1. The Integration of People, Process, and Technology
Tiankai emphasizes that successful data strategies require synergy between people, processes, and technology—with people as the driving force. This mirrors what I've observed in manufacturing environments: when operators understand why data matters to their daily work, adoption skyrockets.
Takeaway: Before implementing new data systems, map how they will help your people solve their problems and make better decisions.
2. Fostering Intrinsic Motivation Through Learning
The book highlights how building data literacy requires environments that foster curiosity and motivation. I've seen this firsthand: Companies that view training as an integral part of their transformation journey consistently outperform those implementing learning programs just to check compliance boxes.
Organizations that integrate continuous learning into their culture create self-sustaining momentum, where employees actively seek opportunities to apply new data skills. By contrast, companies that treat training as a one-off event rarely see meaningful adoption or innovation.
Takeaway: Create learning environments where curiosity is rewarded, and knowledge sharing becomes the norm.
3. Moving From Transactional to Trust-Based Collaboration
Another idea was the shift from treating data initiatives as transactional service requests to fostering genuine collaboration. Tiankai calls for environments where business and data professionals develop authentic interests in each other's domains. This isn't about a business department submitting a ticket and waiting for a dashboard—it's about building cross-functional teams with shared ownership of both problems and solutions.
Takeaway: Design cross-functional data initiatives that challenge the "internal customer" mindset and create partnerships based on shared goals and mutual respect.
4. Tailored Communication for Different Audiences
Communication strategies should be tailored to different stakeholders using frameworks like WHY-WHAT-HOW. This aligns perfectly with what I discussed in Blueprint #3 about securing stakeholder buy-in—different roles need different messages.
Takeaway: Map your stakeholders and customize your data strategy messaging for executives (focusing on business outcomes), managers (focusing on process improvements), and frontline workers (focusing on daily benefits).
5. Start Small, Learn Fast
Rather than trying to "boil the ocean" with ambitious data strategies, Tiankai recommends beginning with small, manageable initiatives. This iterative approach reduces risk and builds trust through visible wins.
Takeaway: Identify processes with high pain points but limited complexity for your first data initiatives, and prove value quickly before scaling.
So, how can you apply these insights? Here are three concrete actions to take:
1. Develop a Data Literacy Academy
Create a modular, gamified learning program that helps employees across functions understand data concepts relevant to their roles. This isn't about turning everyone into data scientists—it's about enabling people to use data confidently in their daily decisions.
2. Map Your Data Roles and Responsibilities
Clearly define who produces, processes, and consumes data under a transparent operating model. This clarity reduces friction and accelerates decision-making. A simple RACI matrix for data initiatives can transform how your organization approaches data ownership and governance.
3. Implement Persona-Based Communication
Develop communication strategies tailored to different stakeholders. Create personas representing various roles in your organization and adapt how you communicate data initiatives to each group.
Remember that the executive who wants ROI metrics needs a very different message than the machine operator who needs to know how data collection impacts their daily work.
My Personal Take
What I appreciate most about Tiankai's approach is how it validates something I've observed repeatedly: technical excellence is necessary but insufficient for transformation success. The human element—trust, motivation, understanding—is what separates successful data initiatives from expensive failures.
This echoes what I've shared previously about process improvement. Just as you can't expect business departments to spontaneously generate improvement ideas without guidance, you can't expect data adoption without deliberately designing for human needs and motivations.
Breaking down departmental silos transforms data initiatives from isolated projects to integrated business capabilities. When teams shift from requesting and delivering data services to co-creating solutions, the quality of outcomes improves dramatically while implementation barriers diminish.
The goal of humanizing your data strategy is always about doing better. And we can always do better, especially when it's about working with and for people.
Your Next Steps
If you're leading a data initiative or broader Digital Transformation, I recommend taking these actions:
Assess your current approach: How people-centered is your data strategy? Are you focusing primarily on technology and processes while neglecting the human elements?
Start small: Identify one high-impact area where better data usage could solve a real business problem and approach it using Tiankai's framework.
Build learning into the process: Create opportunities for your team to develop data literacy in the context of solving real business problems.
Read the book: If you liked this review, Tiankai's book offers a wealth of additional frameworks and practical advice that could transform your approach.
Connect with Tiankai: Feel free to connect with him on LinkedIn if you have questions or want to explore these concepts further. He's a nice guy who values conversations about his work.
Remember, Digital Transformation isn't about technology—it's about enabling people to work better through improved processes, insightful data, and appropriate technology.
Thank you for reading! I'm considering making book reviews a regular feature of this newsletter, so I'd love your feedback. Was this review helpful? Would you like to see more book breakdowns relevant to Digital Transformation?
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Until next week,
Mark