High Business Impact Habits of Top Leaders in Data, Analytics, and AI

Introduction

• Presenter: Rita Salam
• Focus: Habits of top leaders responsible for data, analytics, and AI.
• Key Inquiry: How to become a data-centric organization?
  • Aim to convince leaders to be value-centric rather than just data-centric.

Analysis of High Performing Organizations

• Hypothesis: Companies treating data, analytics, and AI as strategically important are top financial performers.
• Methodology:
  • Analyzed S&P 1200 companies from 2014 to mid-2023.
  • Evaluated financial performance using six key financial metrics.
  • Compared performance with mentions of data analytics and AI in earnings calls.
• Findings:
  • High-performing organizations mention data, analytics, and AI 50% more than low-performing ones.

Key Habits of High Performing Data and Analytics Leaders

1. Strategic Alignment:

   • View data, analytics, AI as strategic to the business.
   • By 2026, 25% of organizations will have a top-earning product based on these technologies.

2. Value-Centric Strategy:

   • Infuse business strategy with data, analytics, and AI.
   • Develop a value-centric story around strategies and operating models.
   • Connect initiatives with stakeholder priorities.

3. Urban Shopping Case Study:

   • Retail chain optimizing revenue through marketing analytics.
   • Mapped customer journey with analytics impact.
   • Identified KPIs to measure stakeholder impact.

4. Prioritizing Talent and Change Management:

   • Focus on talent, skills, and change management.
   • Need data and AI literacy across the organization.
   • Implement communities of practice for training and governance.
   • Prepare for emerging roles due to AI advancements.

5. Intuit and Guangfa Security Cases:

   • Intuit delivers insights rather than just data.
   • Guangfa Security has a best-in-class data literacy program.

6. Driving Business Innovation:

   • Leverage emerging trends.
   • Use fast teams and set audacious goals.
   • Importance of quick proof of concepts.
   • Example: Page Group's fast cycle innovation lab.

7. Building Data Products, Not Projects:

   • Focus on creating reusable data products.
   • Understand customer needs and deliver agilely.
   • ZF Group's approach to data product creation.

8. Composable Data Foundation:

   • Build a cloud ecosystem for data and analytics.
   • Importance of AI-ready data.
   • Integrated workflows from data to applications.

9. Governance and Risk Management:

   • Treat governance as a value driver.
   • Governance as an "enterprise trust initiative."
   • Axon’s AI Ethics Board as a model example.

Conclusion

• Shift from being data-centric to value-centric.
• Consider the type of leader you will be: both a business driver and enabler.

Final Thoughts

• Importance of aligning data, analytics, and AI with organizational goals.
• Continuous learning and adaptation in emerging tech trends.
• Building systems that are agile and responsive to change.