Humanizing Data Strategy: The Real Reason Data Projects Succeed or Fail

Metadata

• Author: count.co
• Full Title: Humanizing Data Strategy: The Real Reason Data Projects Succeed or Fail
• URL: https://count.co/blog/tiankai-feng-webinar-summary?utm_campaign=119411910-06-25%20-%20Creating%20a%20Clarity%20Defining%20Metric%20Tree&utm_medium=email&_hsenc=p2ANqtz-8O3UUw9A2CS-T41w1BZUO38x8PyVnSRGUVhTm17AOrfCi3Bc0vQsjIHl557b68ObFamADgqijF8BhZd8zcu6lRIU4jHw&_hsmi=113896170&utm_content=113882043&utm_source=hs_email

Highlights

• The 5 Cs of a humanized data strategy Tiankai shared a practical framework he calls the “5 Cs”—five essential elements for grounding data strategy in real human impact: • Clarity: Everyone should understand what you’re doing and why. • Curiosity: Encourage exploration, questions, and a hunger to learn. • Connection: Build relationships and cross-functional alignment. • Courage: Make bold calls, even with imperfect data. • Compassion: Treat people like people, not just users or data points. (View Highlight)
• Humans, not data, drive impact Data alone doesn’t create change—people do. Tiankai stresses the importance of focusing on human behaviors, motivations, and interactions rather than just technology.

  “What we’re doing is not only technical transformation—it’s human transformation. It’s about mindset, it’s about behaviors, it’s about how people interact.” — Tiankai Feng Tiankai emphasized that without thoughtful engagement from people across the business, even the most sophisticated data models remain unused. It’s the emotional and behavioral buy-in that unlocks true impact. (View Highlight)

• The secret to alignment is empathy Misalignment between data teams and business stakeholders is common and costly. Empathy, however, can bridge this gap.

  “You can’t build empathy by guessing—you have to ask. And when you ask, people open up. You learn what they actually care about, not just what you think they do.” — Tiankai Feng He shared how empathy interviews often reveal the unspoken anxieties that block progress. By tuning into these emotional undercurrents, data leaders can co-create solutions that people actually want to use. 🚀 Pro tip: Conduct empathy interviews before kicking off data projects to uncover hidden barriers and motivations. (View Highlight)

• Follow the pain to find real problems The most meaningful data work starts where the real business pain lives. Tiankai emphasized that if you want to make a difference quickly, you need to locate and engage with the areas that are hurting the most.

  “Follow your pain was a really wonderful thought experiment… that means you need to follow where the pain is.” — Tiankai Feng Pain is not just a problem—it’s an invitation. If people are frustrated, blocked, or overwhelmed, there’s opportunity for data to create real, visible relief. It’s the difference between solving hypotheticals and making lives easier. 💡 Key takeaway: Don’t just chase interesting data—go where the pain is. That’s where impact lives. (View Highlight)

• Data storytelling isn’t optional The most meaningful data work starts where the real business pain lives. Tiankai emphasized that if you want to make a difference quickly, you need to locate and engage with the areas that are hurting the most.

  “Follow your pain was a really wonderful thought experiment… that means you need to follow where the pain is.” — Tiankai Feng Pain is not just a problem—it’s an invitation. If people are frustrated, blocked, or overwhelmed, there’s opportunity for data to create real, visible relief. It’s the difference between solving hypotheticals and making lives easier. 💡 Key takeaway: Don’t just chase interesting data—go where the pain is. That’s where impact lives. (View Highlight)

• Create psychological safety for data success Projects fail when teams fear admitting mistakes or expressing uncertainty. Psychological safety is critical for successful data initiatives.

  “People are afraid to say, ‘I don’t understand this,’ or ‘I think we’re wrong.’ That fear kills progress. We have to build spaces where it’s safe to speak up.” — Tiankai Feng He emphasized that when team members feel safe to challenge ideas or share concerns, it leads to better decisions and stronger outcomes. Without this, teams default to surface-level agreement and underperform. 🙌 The result: Teams become more innovative, collaborative, and resilient in tackling complex data challenges. (View Highlight)

• Small wins build big momentum Ambitious data projects often collapse under their own complexity. Instead, aim for incremental improvements that build momentum and confidence.

  “Quick wins matter because they prove you’re not just doing theory—you’re solving real problems. That builds trust.” — Tiankai Feng Tiankai explained that quick wins are trust builders. They help teams see early value and make it easier to secure buy-in for broader transformation. 💡 Key takeaway: Quick wins reinforce the value of data projects, securing support for more ambitious initiatives down the line. (View Highlight)