Most companies today want to become data and AI driven. A key first step in achieving this, is to create a data and AI strategy that sets out a roadmap on how to get there.  When defining a data and AI strategy, most companies are faced with two broad challenges. The first is how to reduce risk to get data under control, govern it and build trusted data that can be consumed and used. The second is coordinating what to build and in what order across your business to maximise the use of data, analytics, and AI for competitive advantage to disrupt the market(s) you compete in. Data and AI strategy therefore needs to be defensive as well as offensive.  So how do you do this?  This workshop looks at this problem and seeks to find a way to build a data and AI strategy that enables you to create competitive advantage.

• What is a data and AI strategy and what should it contain? 
• What do you need to do to build a data and AI strategy? 
• Aligning data and AI with your business strategy and business capabilities
• Understanding a defensive versus an offensive data and AI strategy 
• Understanding and assessing your current state
• Defining your future state that utilises data and AI to maximise business value 
• Creating an implementation roadmap that delivers progressive incremental value

Data governance success hinges on having the right people in the right roles with clear accountability. Yet many organisations struggle with unclear responsibilities, reluctant participants, and data stewards who feel overwhelmed by their duties.

This interactive workshop will equip you with proven approaches and practical tools to build a thriving network of data owners and data stewards who actually want to do the job well. Through group discussions, real-world case studies, and practical exercises, participants will learn how to define clear, realistic roles, identify and engage the right people, build accountability without bureaucracy, and create a culture where data ownership is valued rather than seen as an additional burden.

Perfect for data leaders, data governance managers, and anyone responsible for building or improving data stewardship programmes in their organisation.

• How to define clear, realistic roles that people can actually fulfil 
• Strategies for identifying and engaging the right people for data ownership roles
• Practical techniques for building accountability without creating bureaucracy 
• Proven methods for creating a culture where data ownership is valued, not seen as extra work

This workshop is a comprehensive deep-dive into Amazon Bedrock AgentCore and will consist of tutorials to give you a deep understanding of AgentCore Services. All the labs are independent, so you can pick-and-choose the Services you want a deeper dive into.

Want to accelerate your journey into agentic AI?Join us for a hands-on, instructor-led deep dive into Amazon Bedrock AgentCore — where we’ll guide you through every key module, from runtime and identity to memory, gateway, and observability.

You’ll get hands-on with real labs, learn directly from AWS experts, and walk away with the practical skills to start building and running production-ready AI agents with confidence.

• Walk away equipped to build, deploy, and manage agentic AI solutions confidently.
• Explore key components like runtime, identity, memory, gateway, and observability.
• Learn directly from AWS instructors with step-by-step tutorials and practical insights.
• Engage with practical labs to gain real experience with Amazon Bedrock AgentCore services.

In most companies today analytical systems still tend to be siloed like data warehouses, cloud data lakes for data science, lakehouses and stand-alone streaming analytical systems for real-time analysis. Multiple tools are used to prepare and integrate data, reinvention of data integration pipelines in each silo with centralised data engineering struggling to understand data from a growing number of sources and unable keep pace with business demands for new data.  To address these issues, a new ‘factory oriented’ approach has emerged attempting to accelerate creation of data for use in multiple analytical workloads.  That approach is to create Data Products to incrementally create a high-quality data foundation for the AI era. This is a decentralised business domain-oriented approach to data ownership and data engineering to create reusable data products that can be created once and shared across multiple analytical systems and workloads. This workshop looks at how you use common data infrastructure software like Data Fabric software to create high-quality, compliant, reusable, data products and publish them in a data marketplace to share to shorten time to value while also ensuring that data is correctly governed and engineered in a decentralised environment.

• What are data products and why are they needed?  
• What is the best data architecture to build data products?
• Organising and standardising your environment to support democratised data product development   
• Methodologies for creating data products
• Defining and designing data products using a business glossary and data modelling  
• Sourcing, mapping and data quality profiling data for your data products using a data catalog
• Building data integration pipelines to create reusable data products using a data catalog and Generative AI
• Using data observability to monitor and improve pipelines
• Making data products available for consumption using a data marketplace
• Using data products to support multiple analytical workloads including BI, ML and AI 

Organisations invest in collecting and analysing data. But too often, the value is lost at the final stage: how insights are communicated. If your insights don’t land, minds don’t change. This workshop helps you master the “last mile” of data: how to turn analysis into compelling communication that informs, persuades, and inspires.

Drawing on 20 years of experience, Andy Cotgreave will share proven techniques from design, psychology, and AI. Through examples and hands-on exercises, you’ll learn how to adapt your charts to different contexts (presentations, dashboards, emails, etc), avoid the pitfalls that weaken messages, and use AI tools productively without losing trust.

Topics include:

• Why data communication is as rich and flexible as written language
• The L.E.V.E.R.S. model for making charts persuasive and effective
• Tailor data stories for presentations, dashboards, email and other conversations
• How to use AI productively in analytics and avoid the pitfalls
• Ethical considerations when communicating data

This focused workshop transforms GenAI curiosity into concrete business action for time-pressed leaders and practitioners. Participants begin with rapid organisational readiness assessments, using proven frameworks to identify high-impact use cases suited to their specific context.

The session’s core focuses on building practical 30-day pilot plans using a simple 4-step methodology covering scope, success metrics, resources, and risk mitigation. Real-world case studies demonstrate what works in practice, while peer feedback ensures plans are realistic and actionable.

Rather than overwhelming participants with technical details, the workshop emphasises practical governance through implementable checklists and templates. Every exercise builds toward creating concrete pilot plans participants can execute within weeks.

Participants leave with structured 7/30/90-day action plans plus comprehensive resource packs including assessment tools, pilot templates, and governance checklists. The workshop turns insights into business results, enabling meaningful GenAI implementation within their first month.

• Start Small, Think Strategic: Use rapid assessment tools to identify 2-3 high-impact, low-complexity GenAI opportunities that deliver measurable results within 30 days.
• Pilot-First Approach: Deploy a proven 4-step pilot methodology with clear success metrics, defined scope, and built-in learning mechanisms before scaling.
• Practical Governance: Implement essential risk management and compliance measures through simple checklists rather than complex frameworks.
• Action-Oriented Planning: Create concrete 7/30/90-day action plans with specific tasks, resource requirements, and accountability measures for immediate execution.

Join us for an immersive workshop showcasing Mosaic, Strategy’s cutting-edge data orchestration and analytics platform. Discover how Mosaic transforms complex data challenges into actionable intelligence—driving smarter decisions and faster results.

Designed for data leaders, innovators, and practitioners, this interactive session combines expert insights, live demonstrations, and hands-on practice. You’ll see Mosaic in action, explore best practices, and discover tangible strategies for leveraging modern data solutions.

What you’ll gain by attending:

• Practical experience using Mosaic on real business scenarios
• Insights into building and optimising data workflows, from ingestion to visualisation
• Strategies for integrating Mosaic with your existing tools and technology stack
• A roadmap for scaling advanced analytics and unlocking new value from your data
• Access to Mosaic experts for tailored advice and Q&A
• Connections with a community of forward-thinking data professionals

Discover a smarter way to work with data—and leave equipped to put Mosaic’s power to work in your organisation.

Many SMEs are caught at a crossroads. On one side, traditional ways of working and siloed decision-making; on the other, the AI-powered future, where growth and competitiveness are defined by how leaders adopt, govern, and scale AI. The reality is stark: you won’t compete against AI itself, you’ll compete against companies that use it.

For leaders, especially in regulated industries, the challenge isn’t just “how to use AI”, it’s how to use it safely, sovereignly, and successfully. Compliance pressures, questions of data ownership, and the risk of wasted investment mean many AI initiatives stall before they deliver value. Yet with the right strategy, SMEs can double their growth within 3–5 years.

Participants will explore the 3 Pillars of AI Strategy:

• Safe AI – Compliance, Ethics & Trust
• Sovereign AI – Data ownership & Independence
• Successful AI – Measurable ROI & Sustainable scaling

By the end of this workshop, leaders will understand how to:

• Scale AI in ways that are safe, sovereign and sustainable.
• Avoid the most common reason AI projects fail
• Build a culture of empowerment and innovation
• Set their businesses on a trajectory to double growth in the next 3-5years.