data rich–analysis poor: big data webinar with dr. joe jacobsen part 1 sponsored by:
TRANSCRIPT
Data Rich–Analysis Poor: Big Data Webinar with Dr. Joe Jacobsen
Part 1
Sponsored by:
Chapters Chapter 1 Making the Case for and DefiningSustainability, Social Responsibility and Environmental ResponsibilityChapter 2 Conveying and Reporting a Mission and Vision of Financial, Environmental and Social ResponsibilityChapter 3 The Local – Global Three Bottom Lines: ISO
9000, 14000 and 26000Chapter 4 Social and Environmental Measures Chapter 5 Resources, Finance and Return on Responsible InvestmentChapter 6 FESUP - Financial, Environmental and
Social Unity Projects: research, statistics and continuous improvement
Chapter 7 Sustainable Commercial and Industrial Plant Operations Chapter 8 Responsible Lean Logistics Chapter 9 A Sustainable Economy-----------------------------------------------------------------------------Appendix A Basic quantitative analysis Appendix B Environmental and social responsibility survey Appendix C Heat Literacy: what every manager should know
about heat energy
Available at Amazon, Kindle, Nook, B&N and ASQ
Last Session Check Off
Source: Joseph J Jacobsen (2011)
WHY Improved with Big Data ?
• Save Operating Costs • Occupant Satisfaction• Intelligent Operations and Maintenance• Project Justification• Capital Budgeting • Depletion• Pollution• Earth’s Temperature• CO2
• Population • Migration• Sustainability• Environmentally Responsibility
The Biggest of Big Data Normal Variability
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50.000
100.000
150.000
200.000
250.000
300.000
350.000
400.000
CO2 over a million years (blended cores)
Time in thousands of years starting in 1958 and working back - annual observations
CO
2 (p
pmv)
Source: Joseph J Jacobsen (2011)
Source: Joseph J Jacobsen (2011)
Source: Joseph J Jacobsen (2011/13)
Big Data: What is it?No fine line between small, medium and big data
• Large Volume• Sample size
• High Velocity • Streaming data• High Speed Response to Streams
– Specific value turns into action– Proportion in variation across a distribution
• High Variety – Number of variables – Multiple sources - internal/external – Non-uniform time series – Incomplete – Complexity – Nonlinearity
Source: Joseph J Jacobsen (2011)
Large Volume: Sample Size
• Population vs. Sample: the craft and mechanics of modeling and differentiating changed!
• Inferential statistics turn into factual statistics – This is difficult for scientists to digest – Requires a new research paradigm– P-values become less important and sometimes useless – Coefficients become facts – R-square becomes more valuable – Sample size becomes population – t-test significance level becomes meaningless – Design Of Experiments becomes more important
Source: Joseph J Jacobsen (2011)
High Velocity
• Streaming data/data in motion• High Speed Response to Streams
– Specific value turns into action– Proportion in variation across a distribution
• The proliferation of real-time, web-based data acquisition systems combined with more sophisticated hand-held devices means you get on board or get out of the way
Source: Joseph J Jacobsen (2011)
High Variety
Number of variables • Combining multiple sources - internal/external • Non-uniform time series
• Cleaning (culling) data is an essential skill
• Incomplete data • Missing data techniques
• Complexity • Nonlinearity
– Dropdown menu items in statistical software are linear– Model building is one of the most sought after skills in multiple
industries
Source: Joseph J Jacobsen (2011)
Where do you get it?
N
S
EW
Natural Capital
Organizational Capital
Human Capital
Manufactured Capital
Renewable & Alternative Distributed
Energy
Conservation
Restoration
Communicate
Building
Educate
Train
Equipment
Mission & Vision,
Social System
Product Supply Networks
Current Resource Supplier Networks
Warehouse
Retail
Customer
Big Data Analysis Predictive Statistical Modeling,
Hypothesis Testing, Linear and Nonlinear Programming
Lean, Six Sigma ISO
Benchmarks
Reverse Logistics
Carrying Capacity
Energy Efficiency
Technology
Technology
Optimize
Energy Systems
Disassembly
Source: Joseph J Jacobsen (2011)
Intelligent Internal Systems• Btu/product • Btu/person• Btu/DD• Watts/sq/ft• Watts/product• Water/other
unit • Innovate
N
S
EW
Natural Capital
Organizational Capital
Human Capital
Manufactured Capital
Renewable & Alternative Distributed
Energy
Conservation
Restoration
Communicate
Building
Educate
Train
Equipment
Mission & Vision,
Social System
Product Supply Networks
Current Resource Supplier Networks
Warehouse
Retail
Customer
Big Data Analysis Predictive Statistical Modeling,
Hypothesis Testing, Linear and Nonlinear Programming
Lean, Six Sigma ISO
Benchmarks
Reverse Logistics
Carrying Capacity
Energy Efficiency
Technology
Technology
Optimize
Energy Systems
Disassembly
Source: Joseph J Jacobsen (2011)
Intelligent External Systems• Utility Pricing
Structures• Industry
Standards • Weather • Technological
Developments • Demographics• Politics • Suppliers • Innovate
N
S
EW
Natural Capital
Organizational Capital
Human Capital
Manufactured Capital
Renewable & Alternative Distributed
Energy
Conservation
Restoration
Communicate
Building
Educate
Train
Equipment
Mission & Vision,
Social System
Product Supply Networks
Current Resource Supplier Networks
Warehouse
Retail
Customer
Big Data Analysis Predictive Statistical Modeling,
Hypothesis Testing, Linear and Nonlinear Programming
Lean, Six Sigma ISO
Benchmarks
Reverse Logistics
Carrying Capacity
Energy Efficiency
Technology
Technology
Optimize
Energy Systems
Disassembly
15
Source: Joseph J Jacobsen (2011)
Industry Specific Micro/Internal & Macro/Aggregate
Big Data Grows in Volume, Speed and Variety
As time passes, more data is generated by multiple facilities, in multiple locations under multiple conditions
– Access control – Energy management systems– Computerized maintenance
management systems – Asset management Systems – Camera systems – Fire life safety systems– Utility systems – Smart meters – Expert systems
– Elevator escalator systems – Human resource systems– Power & distribution
management– Switch gear– Emergency/standby power– Power Quality– Intrusion Detection – Lighting Systems
Source: Joseph J Jacobsen (2011)
What do you do with it? • Measure• Descriptive distributions – actual behavior • Benchmark – industry standards and comparative facilities • Test for significant differences
– T-tests (paired and single) • Build relational models
– Correlations – Regression
• Displays of quantitative information – – figures– tables
• Make Decisions!!!
Measure, Measure, Measure• Commissioned construction projects• Number of energy and other resource audits • Number of employees with environmental training• Management attention to environmental issues• LEED certified buildings• LEED accredited professionals on staff• Clearly articulated vision of sustainability • Number of green vehicles • Percentage of “green” office space• Sustainability committee• Senior managers with environmental
responsibilities • Number of functions with environmental
responsibilities• Sustainability education opportunities• Matching funds for energy grants and incentives • Investments in cleaner technologies ($)• Number of water efficiency projects• Number of facilities registered as a LEED project
• Percentage of products undergoing life-cycle analysis• Reduce emissions (percentage reduction)• Energy conservation plans• Climate action plans • Safety training programs (hours)• Number of employees hired from high unemployment
(target) neighborhoods• Environmental accounting systems in place • ISO 14001 certification (number of facilities)• Number of suppliers who are considering ISO 26000• Number of employees who contribute to drafting regulation
for the industry• Number of employees who contribute to drafting
international standards• Number of employees who publish in the areas of
environmental responsibility • Number of employees with financial incentives linked to
environmental goals• Number of sustainable sites• The number of innovations in operation and upgrades in
sustainable technologies• Monitoring results of indoor environment• Quantity of materials & resources used in the manufacturing
process
Source: Joseph J Jacobsen (2011)
Descriptive(getting to know your internal and external data)
• Totals • Averages (means, medians, modes)• Frequency distributions • Minimums and maximums • Run charts • Histograms • Range • Scatter plots • Variance • Standard Deviation • Other non-inferential statistics and displays of quantitative
information
Source: Joseph J Jacobsen (2011)
Smart Systems
• Smart grid technology has the potential to reduce energy use by up to 50 percent. For example, a distributed generation (DG) microturbine with combined heat and power (CHP) can achieve an 88% efficiency rating when optimized (Swedish, et.al, 2004). Compare this to a 38% efficient coal fired power plant and we have a gain of 50% in efficiency.
Big Data or Big Science: Part 2
Join Eagle Technology and Dr. Joe Jacobsen for the second webinar in a series of three “Big Data or Big Science” webinars
Tuesday, May 12, 20151:00 PM CDT
Register at: https://attendee.gotowebinar.com/register/
6434301729939622657