May 15, 2025
To ensure your organization is effectively leveraging modern data management practices and is poised to capitalize on advancements like Generative AI, it’s crucial to foster a dialogue with your technology and data leaders. Here are key questions the CEO and COO should consider asking the CIO and CDO:
Understanding Our Current Data Capabilities & Strategy:
- Data Foundation & Accessibility:
- How would you characterize our current ability to store, access, and process the vast amounts of data our business generates? Are we capturing all potentially valuable data, or are we still limited by legacy systems or cost concerns?
- To what extent have we adopted scalable cloud storage and modern data lake principles? What is our roadmap for further modernization if gaps exist?
- How quickly can our business teams access the data they need for analysis and decision-making? What are the current bottlenecks, and what is the plan to address them?
- Speed and Agility of Analytics:
- Are we able to perform complex analytics on our data in a timely manner? How does our analytical speed compare to industry benchmarks or our key competitors?
- What is our strategy for leveraging high-speed analytical engines and columnar data formats to accelerate insights? Where have we seen the biggest wins, and where are the opportunities for improvement?
- How elastic is our analytical infrastructure? Can we scale our processing power up or down based on demand to manage costs effectively while ensuring performance?
- Real-Time Data Utilization:
- How much of our critical operational data is processed and analyzed in real-time versus batch? Are we making decisions based on the freshest data possible?
- What initiatives are in place to expand our real-time data streaming and processing capabilities? Which business areas would benefit most immediately from more real-time insights (e.g., customer experience, fraud detection, supply chain optimization)?
- How are we ensuring the reliability and accuracy of our real-time data streams and the analytics derived from them?
Paving the Way for AI and Predictive Analytics:
- The Lakehouse and AI Readiness:
- What is our current stance on adopting a “lakehouse” architecture? How does this approach fit into our broader data strategy, and what are the perceived benefits and challenges for our organization?
- How critical is a lakehouse architecture for our ambitions with Generative AI and advanced predictive analytics? What steps are we taking to ensure our data infrastructure can support these demanding workloads?
- What is the quality and preparedness of our data for AI applications? Do we have robust data governance, metadata management, and data lineage practices in place to ensure AI models are built on trusted, high-quality data?
- Integrating Internal and External Data for Richer Insights:
- What is our strategy for integrating our internal enterprise data with valuable external data sources (e.g., market trends, social media sentiment, economic indicators, partner data)?
- How are we ensuring the secure and compliant integration of external data? What are the key technical and governance challenges we face in this area?
- Can you provide examples of how combining internal and external data is currently, or could in the future, provide us with a significant competitive advantage or unlock new business opportunities, particularly for predictive analytics and AI?
Governance, Talent, and Future-Proofing:
- Data Governance and Security in a Modern Landscape:
- As we adopt more flexible and scalable data platforms, how are we evolving our data governance, security, and compliance frameworks to manage risk effectively?
- Who is accountable for data quality and integrity across our evolving data ecosystem?
- Skills and Talent:
- Do we have the necessary in-house talent and skills to manage and exploit these modern data technologies? What is our strategy for upskilling our current teams and attracting new talent in areas like data engineering, data science, and AI?
- Measuring ROI and Business Impact:
- How are we measuring the return on investment (ROI) from our data initiatives? Can we clearly articulate the business value being generated from our investments in modern data platforms and analytics?
- Looking ahead, what are the top 2-3 strategic business outcomes that our evolving data capabilities will enable in the next 18-24 months?
These questions are designed to spark a strategic conversation and ensure alignment between executive leadership and technology/data teams on the critical role of data in achieving business objectives.
