Explore the Agenda
8:30 am Check-In, Coffee, & Networking
9:15 am Chair’s Opening Remarks
Improving the Quality & Standardization of Data Inputs
9:30 am Trash In, Trash Out: Generating High Quality Data Across Every Region, Business Unit, & Department
- Examining how inconsistent field reporting, duplicate entries, and legacy behaviors undermine analytics, and how to educate teams on why clean, accurate data matters for them
- Exploring methods to passively collect accurate, actionable field data beyond traditional systems and developing strategies for verifying field-generated data to balance accuracy with practicality
- Leveraging AI and machine learning to convert qualitative observations into structured, measurable data for predictive and productivity-focused analytics
10:00 am Building & Maintaining a Single Source of Truth to Drive Accurate Analytics
- Examining how inconsistent nomenclature, multiple reporting tools, and fragmented databases create inaccurate or duplicated data
- Creating repeatable processes in standardized workflows, SOPs, and transformation rules to maintain and trustworthy data even between project partners
- Ensuring teams appreciate the business impact of clean data
10:30 am Morning Networking Break
Track 1 - Data Engineering & Governance
Track 2 - Analytics Insights & Visualization
Track 3 - Modernizing Your Analytics Practice
Track 1 - Data Engineering & Governance
Scaling Your Firm’s Data Governance
11:30 am Carrying Out Robust Data Modeling to Facilitate Cross-Functional Collaboration
Director of Business Analytics, Kast Construction
- Demonstrating a step-by-step process to map out each system’s data structure to visualize a harmonic data flow
- Aligning modeling practices and visualization with analytics requirements for specific departments such as finance and preconstruction
- Designing for flexibility to avoid vendor lock-in
12:00 pm Audience Discussion: Establishing Effective Data Governance Councils to Guide Your Enterprise
- Ensuring the data governance council has continuity and representation from all desired business areas
- Reviewing data governance strategy, cost-benefit analysis, data quality status, and change management status
- Maintaining engagement and participation
CASE STUDY
Track 2 - Analytics Insights & Visualization
Track 3 - Modernizing Your Analytics Practice
12:30 pm Networking Lunch Break
Track 1 - Data Engineering & Governance
Track 2 - Analytics Insights & Visualization
Track 3 - Modernizing Your Analytics Practice
Track 1 - Data Engineering & Governance
Enhancing Data Structure & Quality
1:30 pm Rationalizing Your Tech Stack to Achieve Value & Efficiency
Business Solutions Architect, Clayco Construction Company
- Overseeing business and enterprise architecture to drive tech stack rationalization
- Leveraging established, repeatable frameworks to assess the technical, cost, and business fitness of each application
- Demonstrating how to quantify the value and cost of applications to senior leadership, moving beyond ROI to demonstrate efficiency and strategic alignment
CASE STUDY
2:00 pm Developing Unified Pipelines for Data Enrichment & Cleaning
Data Analyst, Swinerton
- Centralizing fragment project and cost data into a scalable architecture, aligning disparate systems to enable consistent analytics and reporting across the enterprise
- Showcasing how the team is building automated ingestion pipelines, applying business logic for data quality and standardization, and ensuring datasets are intuitive and accessible even for non-technical users
- Explaining how this drives company-wide data literacy and supports self-service dashboard
CASE STUDY
Track 2 - Analytics Insights & Visualization
Track 3 - Modernizing Your Analytics Practice
2:30 pm Afternoon Networking Break
Achieving AI-Ready Data to Enable Advanced Analytics Jad Chalhoub
3:15 pm Examining How to Evolve Your Data Foundation to Be Fit-for-Purpose for AI
- Discussing why basic analytics can be achieved with a weak data foundation but a strong data foundation is critical before implementing AI or machine learning
- Exploring key factors, including column standardization and data structuring, that must be considered based on your firm’s AI roadmap
- Uncover the risks of rapid AI implementation, and why prioritizing foundational improvements achieves better long-term outcomes
3:45 pm Panel Discussion: Creating AI-Ready Data Ecosystems That Generate Meaningful, Trustworthy Insights
- Learning how to focus AI applications on operationally critical areas that have fit-for-purpose data, rather than implementing for novelty alone
- Uncovering how to identify and diagnose AI biases and untrustworthiness to drive accurate, objective decision making
- Reducing the bias of AI outputs through effective data governance and standards