Steven Haffner

Focus Area 9: Data Culture & Business Value Realization Part of the 2025 Data Strategy Scorecard series. The goal: Create a culture where data is a strategic asset, measured for impact, and championed across the business. What I look for: 9.1 Executive sponsorship & data champions Leaders visibly support data strategy and amplify wins. 9.2 Data literacy programs by role Tailored training for execs, analysts, engineers, and ops. 9.3 OKRs tied to data value Strategic goals connected to measurable data outcomes. 9.4 Celebration of data wins Teams showcase success stories that moved the needle. 9.5 Internal communities of practice Slack channels, brown bags, and meetups to share learnings. 9.6 Storytelling with data Insights are communicated clearly and tied to real decisions. 9.7 Business impact measurement Initiatives include ROI metrics, not just delivery checkmarks. 9.8 Cross-functional collaboration on strategy Business and data teams co-create roadmaps and priorities. 9.9 Recognition for data contributions Stewards, engineers, and analysts are celebrated for impact. 9.10 Cultural norms that encourage data-driven thinking Data is baked into planning, retros, and everyday decisions. You can’t “install” a data culture. You earn it through momentum, measurement, and meaningful collaboration. What’s been your most effective lever for building a data-driven culture?

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When Your Team Becomes the SME One of the hardest parts of analytics projects isn’t always the data. It’s figuring out who really knows the full story behind it. On a recent project, our team tried to track down the subject matter experts (SMEs) for key data elements. We talked with Finance, Marketing, Sales, Operations, and IT. Every group gave us useful input. They each had definitions, context, and history that made sense from their point of view. But nobody had the complete answer. As we pulled all those pieces together, compared notes, and wrote things down, something became clear. It dawned on us slowly: the SME we were looking for had become our team. What We Learned 1. Expertise is spread out. No one person has it all. 2. Our job is to connect the dots. By combining different perspectives, we create a clearer picture. 3. Clarity comes from sharing. Writing everything down helps everyone work from the same playbook.

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I think the best analytics teams don’t just answer questions, they shape better ones. Their performance isn’t measured by dashboards delivered, but by decisions improved and trust earned.

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Teams say they’re data-driven but most decisions are still made by instinct or habit. The real gotcha: data that doesn’t prompt action isn’t insights. Use the data to clarify risk, surface opportunities, and guide your next move. Thanks for listening!

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What Makes an Analytics Team Truly High-Performing? A strong analytics team is not defined by tools or technical depth alone. It’s defined by its ability to: • Translate complexity into clarity • Partner across business and technology • Align work to strategy, not just requests • Deliver insight that drives measurable action Leadership in analytics means building capability, not just delivering output. The most effective teams combine technical strength with communication, accountability, and shared purpose.

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Digital agility isn’t born from tools. It’s built through discipline. The most adaptive organizations share a pattern. They start small, learn fast, and scale only what works. They unify systems before expanding them, upskill teams before replacing them, and measure progress before declaring victory. Digital transformation isn’t a project. it’s a living system, anchored in training, transparency, and measurable outcomes. Those who lead with integration and accountability, not hype, sustain change long after the first rollout ends. Follow Rizwan Tufail for frameworks on digital leadership, transformation strategy, and enterprise adaptability.

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When enterprise AI struggles to scale, the root cause is rarely technology. It is how the organization is structured. Centralized governance protects the organization. Centralized AI development slows learning. The fastest progress happens when teams closest to the problem are trusted to build within clear boundaries. Speed and safety are not opposites when the AI guardrails are designed right.

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