the rise of big data - lean construction institute annual … panel 21 - couche… · ·...
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The Rise of Big Data
October 19, 2017
Jayme Couchene, The Boldt CompanyMark Sands, Performance Building Systems & Catalyst, LLC
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Big What?
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What is Big Data?
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• “Big Data” is high -volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. – Gartner, Inc.
• “Big Data” refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. – Wikipedia
• Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
• “Big Data” is high -volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. – Gartner, Inc.
• “Big Data” refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. – Wikipedia
• Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
• “Big Data” is high -volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. – Gartner, Inc.
• “Big Data” refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. – Wikipedia
• Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
• “Big Data” is high -volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. – Gartner, Inc.
• “Big Data” refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. – Wikipedia
• Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
• “Big Data” is high -volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. – Gartner, Inc.
• “Big Data” refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. – Wikipedia
• Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
• “Big Data” is high -volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. – Gartner, Inc.
• “Big Data” refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. – Wikipedia
• Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
• “Big Data” is high -volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. – Gartner, Inc.
• “Big Data” refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. – Wikipedia
• Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
• “Big Data” is high -volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. – Gartner, Inc.
• “Big Data” refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. – Wikipedia
• Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
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It’s All About Data
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Unfiltered Data
Filtered Data
Big Data
Variation
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Closer Look @ Big Data
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Variation
Budgeting w/o Functions and KPI’s
Procurement to Delivery
Conceptual Design to Design Development
Functional Program w/KPIs
Final Design & Cost
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Guiding Principle
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Predictions are only as good as the data supporting them!
Disciplined Data Governance:• Record Data in a Complete, Consistent, & Standard Way• Decipher Critical Data From Junk• Nucleus of Critical Data – Typically Less Than 200 Data Points
Lack of standardization makes comparison extremely challenging
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Which Data to Extract?
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Start with Critical Data standardized across many organizations
Facility Purpose Departments Functions
Medical OncologyRadiation OncologyPhysician OfficesTherapyAdministration
Linear AcceleratorCT SimulatorPET/CT ScanningHDR ProcedureExams (Specialized)
Cancer Center
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Which Data to Extract?
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Start with Critical Data standardized across many organizations
KPI’s (Attributes) Key Parameters Schedule
ParkingVertical CirculationCore & Common
Building ConfigurationSite/Building Quantities
Approval to DesignDesign to Mobilize
Mobilize to EnclosureEnclosure to Occupancy
LocationOwner TypeShell Type
QualityDurability
Energy/LEED…and so on
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Systems Approach
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Start with Critical Data standardized across many organizations
Costs Groups Systems
A – SubstructureB – ShellC – InteriorsD – ServicesE – EquipmentF – Special ConstructionG - Sitework
B – ShellB2010: Exterior WallsB2020: Exterior WindowsB2030: Exterior Doors….and so on
Hard Costs
Soft Costs
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MacLeamy
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Ability to Impact Cost Cost of Change
Start FinishTime
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Boldt’s Predictive Analytics Journey
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Need & Feasibility
Scope & Approval
2017 – Big Data with Systems Approach
Biggest Decisions
Program
Conceptual Design
SchematicDesign
Design Development
Contract Documents
Big Decisions
Bigger DecisionsProcure & Construct
2005 – Systems Approach based on Functional Requirements
Effort Curve Traditional Delivery
Effort Curve IPD Delivery
Effort Curve Big Data
Total Solution
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Extracting Boldt’s Existing Data
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Understanding Existing Data
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Most Efficiently Programmed, Designed and Produced Projects
Data analysis normalized to selected location and time
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Understanding Existing Data - Cost
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Material and Cost Outliner
Material and Cost Outliner
• 13 of 15 projects produced within 7% of predicted (market average) cost – 2 outliers• Sampling average of all projects is 2.7% less than market average
Sampling Average
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Understanding Existing Data - Program
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Program and Plan Outliners
Program and Plan Outliners
• 11 of 15 projects exceed 7% variation from of predicted (market average) program and plan• Sampling average of all projects is 4% less than market average
Sampling Average
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Deeper Dive Into the Data
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Exam Room Illustration
Gross Building Area per Exam Room ranges from:365 SF to 645 SF (~80% difference)
Resulting Cost per Exam Room ranges from:$95,000 to $196,000 (>100% difference)
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Understanding Existing Data Sampling
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Exterior Wall Illustration
Cost per Wall Area ranges from:$34/SF to $99 (~ 3 times)
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Understanding Existing Data
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What We Learn From This Data
1. High variation exists at project details (validates studies showing excessive waste)2. Low variation exists at project summary (helps conceal the excessive waste)3. Greatest variation is within facility program/design (Not material specification and building
production.)4. Most projects can be effectively steered to desired outcomes (Starting in the pre-
program stages)5. Project outcomes can be reliably predicted in the conceptual design stages.6. Variations to approved target can be readily discovered and managed.
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Applying This Data to Predict Future Results
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Conceptual Modeling
Rapid scenario prototyping and dialing in to most likely results – in conceptual modeling stages
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Deming, Lean and Back Again
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Toyota Production System
Lean Manufacturing
Lean Construction
W. E. Deming
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Big Data and Lean Construction
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According to Deming
• Process Improvement• Objective Knowledge
• Impartial Prediction and Analysis
• Big Data – Within & Outside The Organization• Standardized Systems Approach
• Disciplined Data Governance
• New Forms of Data Processing
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Big Data and Lean Construction
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Impartial Prediction and Analysis
• Target Value Delivery (TVD)
• Choosing By Advantages (CBA)
• Set-Based Planning/Design (aka Concurrent Engineering)
Success Story
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In the spirit of continuous improvement, we would like to remind you to complete this session’s survey in the Congress app! We look forward to receiving your feedback. Highest rated presenters will be recognized.
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Jayme CoucheneThe Boldt CompanyProject Development [email protected]
Mark SandsPerformance Building [email protected]