Course Overview
AIBusiness Intelligence
Fueling Your AI With High-Quality Data
This course equips business professionals with essential knowledge to create an AI-first data strategy that provides a robust foundation for successful AI initiatives. You'll learn practical approaches to building clean data practices, breaking down data silos, and establishing governance frameworks that fuel AI transformation. Through real-world examples and actionable insights, this course demystifies data strategy for AI, making complex concepts accessible to non-technical business leaders.
Why this course matters
- Poor data is the #1 barrier preventing AI projects from reaching production, with 70% of organizations struggling with data governance challenges.
- Companies with solid data foundations are 5X more likely to make faster decisions and achieve better AI ROI within their first year.
- Most businesses have around 80% of their data sitting unused in silos, clean data practices create the foundation for scalable AI implementation across your entire company.
Who should attend
- Business leaders planning AI initiatives who need to understand data foundation requirements.
- Operations managers responsible for data quality and process improvement.
- Project managers overseeing digital transformation and AI implementation projects.
- Staff seeking to better leverage data for decision-making and AI applications.
What you’ll learn
- Data as a strategic asset - Understand how to shift organizational mindset to recognize data's intrinsic value and align people, processes, and technology to drive business goals.
- Breaking down data silos - Learn about practical methods to connect all your data sources, from structured databases to unstructured documents, creating a unified view for AI success.
- Data quality fundamentals - Learn to identify and eliminate dirty data while implementing quality assurance processes that ensure AI systems are trained on reliable information.
- Governance for AI - Master the essentials of data governance, security, and compliance frameworks that support responsible AI development and deployment.
- Building data culture - Develop strategies to foster organization-wide data literacy and create shared understanding of data value across all business units.Data as a Strategic Asset - how call analytics works, the difference between descriptive, diagnostic, predictive and prescriptive analytics and why each matters.
Practical applications
- Assess your current data readiness for AI initiatives and identify key improvement areas.
- Create a roadmap for connecting disparate data sources and building a data-driven culture.
- Implement data quality monitoring and cleansing processes that support AI model accuracy.
- Establish governance policies that balance data accessibility with security and compliance requirements.