Many organizations are investing in their analytics, data governance, and information architecture programs - some to enhance existing data infrastructure and capabilities, others just beginning their journey. This session will describe how, based on the speaker's current experience launching the data strategy at Keystone Foods, to align and optimize the people, process, and capability investments in order to distribute the costs and risks, while maximizing opportunities for incremental value in ways that contribute to a long-term data strategy and avoid the common issues facing disconnected efforts that yield diminished returns. 

• There are already strong analysts in your organization – Empower them and listen. 
• Survey and rank the existing analyst output by criticality and consumption – Those are your initial data governance and data architecture use cases. 
• Provide a governance framework for critical data’s quality, metadata and assembly to reduce the redundant work analysts in the organization have to do themselves, allowing them to spend more time on value-add activities (while reducing the risk of inconsistent and incorrect data!).
• A pragmatic data architecture then creates more automation of (reduced data processing time and error) necessary data assembly work, improved self-service analysis capabilities (more capacity for insights), and re-usable data assets (shorter project duration and fewer data redundancies).