siraj bigdataweek london chicago 2014

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