Dan Stephenson
Director of Engineering, Data & Automation Barton Malow
Dan builds high-impact software and data systems that transform how construction and industrial companies operate at Barton Malow using in-house AI, generative design, and lean, ROI-driven development strategy.
Seminars
Tuesday 21st April 2026
Panel Discussion: Transforming Analytics as a Strategic Asset in Construction
9:00 am
- Examining how to scale and evolve from simple reporting tools towards analytics that drive more effective decision making
- Addressing the challenge of funding analytics in an industry in tight margins and limited overhead, and how creative cost models can drive sustainable innovation
- Revealing how defining clear responsibilities across data, technical, and innovation teams, integrates analytics into your firm’s DNA
- Ensuring your analytics practices are aligned with the overall strategic objectives of your firm
Monday 20th April 2026
Creating Robust Conversational Analytics Tools to Increase End User Accessibility
3:30 pm
As analytics teams strive to foster data-driven decision making across every layer of the enterprise, improving the accessibility of the data is paramount. LLMs have sparked a revolution in conversational analytics using AI agents to refine the user interface and integrate analytics seamlessly into daily workflows.
Yet, challenges remain in ensuring these data queries return accurate, unbiased insights.
Level up your practical analytics workflows by:
- Learning how to build AI agents and generative tools based on your data lake to allow stakeholders to query data directly without needing deep knowledge of data structuring or dashboards, accelerating decision making and analytics adoption with field users
- Evaluating how generative AI can create new outputs beyond text-based answers as part of workflows
- Understanding which parts of your firm are best suited to the implementation of these tools to reduce the risk of incorrect decisions
- Creating backstops to ensure LLM-driven insights are reliable and actionable within these defined workflows
- Adopting strategies for making conversational analytics as effective as possible, including prompt engineering and contextual queries for better results to educate end users on how specificity and context directly influence the value of AI outputs
CASE STUDY
