8:15 am Morning Coffee & Registration

8:50 am Chair’s Opening Remarks

  • Jit Kee Chin Chief Data & Innovation Officer, Suffolk Construction

9:00 am Keynote – Case Study: Developing a Corporate Wide Data Strategy Centered Around Analytics as a Tool for Smarter Business Decisions

  • Jit Kee Chin Chief Data & Innovation Officer, Suffolk Construction

Synopsis

• Moving data analytics away from individual projects to a corporate wide strategy of leveraging analytics for business decisions and preparing the business for that now
• What does the company need to do support the effort and ensuring the business is actually using data analytics in their business planning?
• Understanding what risk factors are added to jobs to increase efficiency and make more accurate decisions on each jobs long term value
• Reflecting on failures and how the lessons learned has changed strategy

9:40 am Panel Discussion: Enabling Inter-Company Large Scale Data Sharing to Make Analytics a Reality

  • Dustin Burns Director, Information Technology, McCOWNGORDON Construction
  • Chris Martin Director – Technology Services & Information Technology Manager, Mycon General Contractors

Synopsis

• Overcoming legal, cultural and logistical issues around sharing data with subcontractors/owners to ensure project-level analytics can smoothly occur and insights can be taken for longer term strategies
• Making industry-wide data sharing a reality to everyone’s benefit to form large data sets for identifying trends, analytics and apply AI & ML tools
• Companywide practices around data governance and getting resistant departments to share data and managing that sensitive information appropriately

10:20 am Speed Networking & Refreshments

Track 1: Data Management
Data Collection

11.10 Presenter Led Audience Discussion: Strategic Data Capture - Developing Long-Term Planning & Implementation to Efficiently Obtain Data

  • Recognizing existing data entry processes and its place in long term data capture strategy
  • Understanding what data to use, capture, structure and the effort to do so
  • Organizing projects for data capture, naming conventions, standardizing processes and getting staff to log data accordingly to collect better data which support the various analytics tools
  • Recognizing the resources needed to capture data efficiently; connecting platforms, creating custom in-house applications and purchasing solutions & technologies

Sharon Noyce, Data & Senior Business Intelligence Analyst,
Kiewit

Bob Nussmeier, Data & Vice President of Business Development,
Kiewit

Track 2: Data Applications
Historical Cost Data
& Risk

11.10 Presenter Led Audience Discussion: Strategic Data Capture - Developing Long-Term Planning & Implementation to Efficiently Obtain Data

  • Leveraging historical data to decide what a healthier project looks like and normalizing that data to today’s dollars and locations
  • Using historical data to determine when and how costs are going to hit and what the health of a project will be based on in the future
  • Using historical data to analyze safety statistics, profit margins and profitability going forward and forecast that out
  • Implementing image software that stops manual input from field staff

Nicholas Smith, Data & Construction Data Analyst,
Faithful+Gould

12.00 Unburdening Staff to Ease Data Collection
Requirements & Make Sure Data is Collected for Analytics

  • Recognizing the labor shortage in data collection to get data without burdening field or hiring more people
  • Simple, cost-effective solutions for data capture from the field that take into consideration the labor force’s technological acumen
  • Implementing AI that allows passive data collection and bridge gaps in data
  • Utilizing visual data capture to cut out the need for human data capture

Allen Hayes, Director of Information Technology,
FTK Construction Services

12.00 Case Study: Leveraging Data Analytics as a Risk Mitigation Tool to Identify Potential Issues Early in a Project

  • Determining early warning signs, inflection points and KPIs to decide when a project’s not going well and create alerts
  • Utilizing BI to measure a project against the bid and ensure it is staying within the estimation
  • Utilizing predictive analytics to gain early warnings of complications and evaluating problems before they get out of hand
  • Combining estimates with project maintenance software

Ed Littleton, Senior Vice President – Risk Management,
Balfour Beatty

12:40 pm Lunch

Track 1: Data Management
Company Preparation for the
Future of Analytics

1.40 Leveraging Visual Data Capture for Analytics & Speed Up Inspections & Processes

  • Capturing and utilizing model data from a BIM model to create a digital representation and leveraging analytics on that model through ML & computer vision
  • Using drone photography and drone video cameras for engineering inspections to ensure that the asset built confirms to the design documents and to create a visual representation of the job site; making use of a point cloud
  • Using visual data capture to track the location of workers,
    productivity and identify safety issues
  • Overcoming union issues with labor data tracking; ensuring consent and ethical practice

David Banyard, Senior Construction Technology Disrupter,
WeWork

Track 2: Data Applications
AI & ML Case Studies

1.40 Utilizing Historical Data to Accurately Estimate Projects & Identify Profitable Jobs

  • Using historical costs and estimating software to accurately and quickly estimate the cost of a project
  • Taking scope change into consideration and getting owners to precisely outline scope when forming estimates to accurately predict the cost of project
  • Building models that predict the profitability of the job you are chasing to ensure you are maximizing company revenue
  • Moving from descriptive to predictive analytics to select profitable projects

Brent Pilgrim, DESTINI Applications Director,
The Beck Group

Greg O’Bryan, Preconstruction Engineer & Senior Engineer,
The Beck Group

2.20 Structuring Data Teams, Strategic Hiring, Roles & Responsibilities & Upscaling for the Future

  • Determining who in a data team should be undertaking which responsibilities; identifying the make-up of a team to achieve data analytics ambitions in the short and long term
  • Managing short-term requests and long-term data analytics ambitions to stay on track with strategy
  • Hiring skilled programmers and teaching them construction and teaching those with construction knowledge how to program; best practice in growing data teams
  • Recruiting, hiring and retaining talent to construction when other industries pay more

Mike Ernst, Vice President – Insights & Innovation,
Ryan Companies US, Inc.

Bryan Hadoff, Data Scientist and Financial Analyst,
Ryan Companies US, Inc.

2.20 Advancements in Applying Data Analytics, AI & ML to Schedules to Set Projects Up for Success

  • Leveraging data analytics and AI&ML to optimize baseline schedules and get the job done as quickly as possible
  • Uncovering how analytics can be and has been used to improve the scheduling processes itself
  • Using data analytics to discover root causes in problems in the schedule before they delay the project

Chris Heger, Chief Information Officer & Vice President,
OAC Services

Akshita Tyagi, Project Engineer (Data Analytics),
OAC Services

3:00 pm Afternoon Refreshments

3:30 pm Realizing the Potential of ML & AI & Preparing Your Data

Synopsis

• Examples of applying ML & AI to data and how to make decisions in business with AI & ML
• Using AI & ML to create predictive data testing trends
• Forming strategies on future analytics, structuring data for AI & ML and preparing your business now

4:10 pm Panel Discussion: Determining the Right Metrics & KPIs to Track for Success

  • Nicole Waits Insights Analyst, Ryan Companies
  • Brian Tighe National Director of Project Analytics, Skanska
  • Jason Bedogne Chief Information Officer, Kimbel Mechanical Systems
  • Allen Hayes Director of Information Technology, FTK Construction Services

Synopsis

• Creating the right KPI’s through correct categorization of projects for appropriate benchmarks to be created
• Identifying KPIs and creating data capture/mining strategies appropriately so unnecessary data capture does not burden staff
• Integrating various KPIs together to give more information on the potential success of projects and reveal key business insights
• Lessons Learned: KPIs that gave value & KPIs that did not

5:00 pm Chair’s Closing Remarks

  • Jit Kee Chin Chief Data & Innovation Officer, Suffolk Construction

5:10 pm End of Day One