siraj bigdataweek london chicago 2014
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The Big Data Revolution is Expensive. Re-Evolving Your Data is Not.
Here’s Why
Part 2 – Engineers’ Perspective
Siraj Tahir@sirajt
• It takes a revolution to make the solution
• Too much confusion, so much frustration
• It takes a re-evolution to make the solution
• Too (avoid) much confusion, (and) so much frustration
Part 1 – Key Points
• Avoid Expensive Mistakes• Leverage Existing Data• Focus on Objectives not on Objects• Enhance & Enrich with BigData• Understand Limitations• Create Insight• Generate Actionable intelligence
Types of BigData
• Enterprise and Business Operation Data
• Customer and Consumer Data
• Civil Engineering & Citizen Services Data
(from Three kinds of Big Data by Alistair Croll, 2012)
Is BigData New Concept for Engineers?
Not entirely a new concept …
• Environmental Models– Weather & Climate, Hydrological, Hydraulic, etc
• Construction Management Models– Buildings, Roads, Bridges, Tunnels, etc
• Utility service provision models – Water, Electricity, Gas, etc
(Joseph Stretha, Iowa State University )
Construction Models
Hydrological Models
If its not new…
How have we dealt with it in the past?
So what is new?
Big Data
Variety
Volume
Complexity
Velocity
(DataStax 2013)
✓
✓
✓
? ?
Why Use it?
• Reduce Uncertainty
• Improve Operational Intelligence
• Increase Efficiency
• Ensure Compliance
• And mostly….. Because there is no other option!
Role of Domain Expertise
Data
Information
IntelligenceActionable Intelligence
Change In operations
What to captureHow to capture
How do they relate?
Storage
ModelsPredictive Models
Investment
Upgrades
Change in behaviour
Visualisations
Processing
New System State
© Siraj Tahir 2014
EngineersData ScientistsBusiness + Public
Key
Positive Outcomes
Cooperation of domain experts
Re-Evolution of Data
+
Water UtilitiesTaKaDu.com
Challenges …
Future Developments …
Dragan Savic, U of Exeter
Increased Velocity Increased ComplexityARUP ‘s Research on Infrastructure Resilience modelling using:- Infrastructure age- Geology - Weather- Historic Failures- Current State- Interaction with other
infrastructure
Transport Planning
Journey to work CO2 emissions – combined residents and employees (2001)
CASA, UCL
James Cheshire, CASA
Challenges
• Data Privacy
• Data Volume
• Rapidly changing lifestyles
Construction & BIMvisual5d.com
Development of BIM
Opportunities
Challenges
What is in store in the Future?
Future Growth Potential …
• BI Type Intelligence Visualisations
• Smart Sensors Intelligent Sensors
• Predictive Models Automation
What will it enable us to do?
• Augmented Human Decision• Help monitor Health of the Infrastructure• Improve Infrastructure Resilience• Manage & Reduce Traffic Congestion• Improve Construction Productivity• Operate Smart Grids and Utilities
Goal Smart Cities
Thank You
Siraj Tahir@sirajt
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