Nanotools for Leaders® — a collaboration between Wharton Executive Education And Wharton Center for Leadership and Change Management — are quick, effective tools that you can learn and start using in less than 15 minutes, with the potential to have a significant impact on your success as well as the engagement and productivity of the people you lead.
Aim
Develop the leadership capacity needed to seamlessly move from business judgment to data-driven learning, and deploy this capacity to shape priorities, decisions and investments.
Nano tool
We are experiencing a major shift in the way businesses use information to compete. Machines now augment human intelligence, enabling faster experimentation and more precise decisions. This convergence of human vision and machine capabilities is redefining how organizations create value and rewarding those that successfully integrate the two into a single system of learning and growth.
Become one data-driven leader it’s knowing how to translate data into business value. It’s the executive equivalent of pairing an Hermès tie with a Uniqlo hoodie – moving fluidly between business judgment and analytical rigor, and grounding technical possibilities in a clear understanding of how people and organizations behave.
Action steps
1. Inventory your data as a strategic asset, not an afterthought.
The data may not appear on the balance sheet, but it can generate real value – or risk. Establish a routine process for inventorying your data, verifying its quality, and standardizing definitions. When everyone starts from the same facts, you avoid tedious debates over numbers and focus decision-making on the real issues.
2. Start with a statistical business mindset.
AI can be described as “statistics at scale”. View key business drivers as distributions, not fixed numbers, and ask yourself how the findings were tested before acting on them. Make statistical reasoning a standard part of strategy discussions, not a technical afterthought.
3. Lead with an assumption orientation.
Improve your instincts and static predictions with testable hypotheses. Ask: “What really drives our growth?” » – and use granular transaction-level data to prove or disprove it. Move from assumptions to validated information: In God we trust – everyone contributes data.
4. Map your data flows like a process engineer.
As Eliyahu Goldratt taught manufacturing executives in The Goal to find their “weeds,” identify bottlenecks in your data flow. Connect business processes, technical architecture and data processes into a single integrated picture. Streamlining this flow accelerates both scalability and visibility.
5. Integrate data, software and services into a single value engine.
Just as Lou Gerstner once redefined the value of IBM as software + services = business valuetoday’s formula is data + software + services = business value. Make sure these three elements work as one cohesive process and not as competing silos.
6. Foster a “Test-Experiment-Learn” fitness culture.
Just like training, AI models improve with iterative results. Encourage curiosity, testing, and learning at all levels of the organization. Encourage experimentation through statistical “what if” simulations, iterating through thousands of offers, channels, or pricing models to discover what actually drives results.
7. Turn data into story and story into strategy: balance the artist and the scientist.
Data alone does not lead; stories do. Use analytics to craft stories that inspire action and align stakeholders. When data becomes narrative, it becomes strategy, and the CEO becomes both storyteller and scientist. Data-driven leadership is not only analytical, it is also creative. Ask questions, tell stories, and use good judgment. Combine quantitative precision with humanistic skills that transform information into meaning and actionable results.
8. Understand the ecosystem and its history.
Schedule periodic briefings (internal or external) to guide your leadership team through previous waves of business innovation in the ecosystem: what drove adoption, what hindered progress, and what differentiated the winners. Use these models to better determine where AI investments will deliver value: pre-AI, pre-gen AI, post-gen AI.
9. Know when to probe for ROI.
Think like an investor (who financially reorganizes balance sheets), an operator (who impacts bottom-line drivers), and a technologist (who builds systems to unlock insights from raw data). Together, these perspectives create data mastery at the top. Remember: Not all AI initiatives work and are linked to ROI. Some items, just like electricity, should be treated as operating costs.
10. The dignity of work: take your colleagues with you on the journey.
Your employees are nervous about their jobs. You ask them to integrate their human and domain knowledge into an AI agent that could replace their job. Help them see this as an opportunity to move up the critical thinking value chain and move away from repetitive, mundane tasks.
How one organization used it
A new CEO of a private equity-backed software company discovered that sales and marketing were working in silos, without shared data. To change the growth trajectory, it launched a data-driven effort to unify and analyze the customer base. Data teams consolidated 28 million historical records from existing systems, and machine learning models were used to deduplicate accounts and reconstruct accurate customer hierarchies. Sales, marketing, operations and finance then validated the results to ensure the new data matched operational reality and official finances.
With a single, reliable data set in place, the team was finally able to see where the real opportunities lay. The analysis revealed $1.1 billion in potential cross-selling revenue over two years, including $788 million that could be leveraged immediately, plus $52 million from possible product upgrades. Most of this value was concentrated in the top 20% of customer-product pairs. A rich list of 7.7 million predictive scores, applied to 70,000 customers, then helped refine sales targeting and retention efforts, including $71 million in annual revenue at risk of churn.
By aligning decisions around shared, trusted data, the CEO and leadership team were able to realign teams and direct efforts where it mattered most, transforming fragmented information into coordinated, high-leverage growth.
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Contributor to this nano-tool
Sajjad Jaffer, WG’01, is a member of the advisory board of the Wharton AI and Analytics Initiative. He co-founded Two Six Capital, the Silicon Valley company pioneering data science for private equity, based on 25 years of doctoral research developed by Wharton professors Eric Bradlow and Peter Fader. The company’s data science platform has been applied to over $30 billion of closed global private equity deals, analyzing over $160 billion of granular receipt-level data.