How are you approaching the journey to increase data maturity within your organization? Could you share the key steps or frameworks you've implemented to scale data capabilities, improve data governance, and foster a data-driven culture across departments? What have been the most significant challenges you've faced, particularly in aligning data strategy with business goals, securing executive buy-in, or overcoming technical limitations? How do you ensure continuous progress in areas like data literacy, quality, and analytics innovation?

We established an Enterprise Data Strategy as a standard which includes both Architecture and Data Governance, identified 18 Enterprise Data Domains with clear executive ownership, then codified our standards into an Enterprise Policy to ensure it endures over time.  This effort over the past 4 years has unleashed tremendous, sustained value through operationalizing Analytics that previously could only be achieved through single point of time projects.

My company is pretty young in Data & Analytics maturity, a very B2B, engineering environment, and now doing more B2B2X business models..   
Our journey started with more Data Privacy driven strategic needs, ensuring compliances across PDPA and GDPR across the operating countries. 
Then it's Data Security and as part of improvement of CyberSecurity posture. 

Now, we're entering the Data & Analytics space by: 
1. Establishing a Data Strategy and Roadmap  
2. Establishing an Open Data & AI platform, based on Azure Data Fabric and Databricks 
3.  Refreshing AI strategy and use cases (happening now as we speak) 
4. Establishing Data Governance Framework and Guidelines (in-progress) 

In parallel, we've been pretty bullish in experimenting and exploring AI use cases, whether copilot or beyond. 

No silver bullet, but the main advice is the start from Data & AI use cases aligning with business strategies and developing business cases/justifications alongside explorations and experiments. 

Sit down with you Data team and do a Garter D&AI maturity benchmarking. This can help you hone in on areas needing developing or simply confirm your own understanding of your capability. It can be a useful artefact when requesting greater investment for D&AI

Start with Data Literacy and focusing in on the real impact of poor data quality and how it affects revenue, customer service and operational efficiency. Start gathering stories from your colleagues and stakeholders about where poor data management practices have impacted the business. Nothing gets focus faster than issues affecting lost revenue.