Big Data and Sustainability: The next step for Circular Economy

Professor Lukumon O. OyedeleDirector of Bristol Enterprise, Research and Innovation Centre (BERIC)

University of the West of England (UWE), BristolUnited kingdom

July 2016

Big Data

Big Data Analytics (BDA)

Sustainability and Circular Economy

BDA & Circular Economy: Mutual Opportunities

Outline

Big Data

Definition

McKinsey Global Institute (2011)

Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

Various Aspects

• Large dataset (Megabyte, Gigabyte, Terabyte, Petabyte, Exabyte)

• Unstructured data (networked data but fuzzy relationships)

• Data-driven research, business & decisions

• High skills (IT, statistics, etc.)

Big data characteristics – The 5Vs

Collectively analyzing the

broadening Variety

Responding to the

increasing Velocity

Cost efficiently processing the

growing Volume

Establishing the

Veracity of big data sources

30 Billion RFID sensors and counting

1 in 3 business leaders don’t trust the information they use to make decisions

50x 35 ZB

2020

80% of the worlds data is unstructured

2010

Identifyinghidden data

Of Value

Almost every manager is concerned about the money they spent

Big data architecture

Big data jobs in the UK

Big data analytics

Big data analytics

• Big data in vacuum is useless

• Big data analytics is the process of examining large data sets (Big data) to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.

• Real game changer

Understanding How Data Powers Big Project

House

VA

LUE

TIME

High

Low

Past Future

Home Intelligence versus Data Science (Advanced Home Analytics)

Evolution Of The Home Analytic Process

HomeIntelligence

Home Intelligence

Typical Techniques and Data

Types

•Standard and ad hoc reporting, dashboards, alerts, queries, details on demand

•Structured data, traditional sources, manageable data sets

Common Questions

•What happened last quarter?•How much energy did we consume?•Where is the problem? In which situations?

Data Science (Advanced Home Analytics)

Typical Techniques and Data

Types

•Optimization, Predictive modeling, forecasting statistical analysis

•Structured/unstructured data, any types of sources, very large data sets

Common Questions

•What if…?•What’s the optimal scenario for our Homes?•What will happen next? What if these trends continue? Why is this happening?

DataScience

Big data analytics lifecycle

Type of Analytics

Prescriptive

Analytics

IoT Automation for Internet of Things

Decision making under uncertainty Determining best outcomes given variability

Ensemble Decision making Multiple predictive models & other techniques involved in

decision making

Self optimizing Continuously evolving looking to get the best outcome

Automatic

Analytics

Backend applications Batch processing, decision making, altering, etc.

Frontend applications Decision making, scoring, alerting, etc.

Real-time Real-time predictions as data is processed

Automatic updating Rebuild models is automated

Embedded analytics Advanced analytics is part of …

What-if analytics What happens if the data is changed?

Predictive

Analytics

Predictive modeling What will happens next?

Forecasting What if these trends continue?

Simulation What could happens if …?

Alerts An action is needed

Descriptive

Analytics

Statistical Analysis Advanced statistical techniques, correlation, etc.

Query/ Drill downs What exactly is the problem?

Ad-hoc reporting How many, how often, where?

Standard reporting What happened?

Analytics techniques & algorithms (1/3)

Technique Applicability Algorithms

Classification Most commonly used technique for predicting a

specific outcome such as response / no-response,

high / medium / low-value customer, likely to buy / not

buy.

Logistic Regression

Naive Bayes

Support Vector Machine

Decision Tree

Regression Technique for predicting a continuous numerical

outcome such as customer lifetime value, house

value, process yield rates

Multiple Regression

Support Vector Machine

Clustering Useful for exploring data and finding natural

groupings. Members of a cluster are more like each

other than they are like members of a different

cluster. Common examples include finding new

customer segments, and life sciences discovery

Enhanced K-Means

Orthogonal Partitioning

Clustering

Expectation

Maximization

Analytics techniques & algorithms (2/3)

Technique Applicability Algorithms

Attribute

Importance

Ranks attributes according to strength of

relationship with target attribute. Use cases

include finding factors most associated with

customers who respond to an offer, factors most

associated with healthy patients.

Minimum Description

Length

Anomaly

Detection

Identifies unusual or suspicious cases based on

deviation from the norm. Common examples

include health care fraud, expense report fraud, and

tax compliance.

One-Class Support

Vector Machine

Analytics techniques & algorithms (3/3)

Technique Applicability Algorithms

Association Finds rules associated with frequently co-

occuring items, used for market basket analysis,

cross-sell, root cause analysis. Useful for product

bundling, in-store placement, and defect analysis.

Apriori

Feature

Selection &

Extraction

Produces new attributes as linear combination of

existing attributes. Applicable for text data, latent

semantic analysis, data compression, data

decomposition and projection, and pattern

recognition.

Non-negative Matrix

Factorization

Principal Components

Analysis (PCA)

Singular Vector

Decomposition

business initiative

BUSINESS IMPERATIVE

The number of organizations who see

analytics as a competitive advantage is

growing.

2010 2013 2016

83%

IQIBM IBV/MIT Sloan Management Review Study 2016

substantially outperform

Studies show that organizations

competing on analytics outperform their

peers

1.6x Revenue

Growth2.0x EBITDA

Growth2.5x Stock Price

Appreciation

IBM IBV/MIT Sloan Management Review Study 2016

Sustainable & Circular Economy

Circular Economy

• A circular economy is an alternative to a traditional linear economy (make, use, dispose) in which we keep resources in use for as long as possible, extract the maximum value from them whilst in use, then recover and regenerate products and materials at the end of each service life.

Circular Economy

• Why a circular economy is important

1. As well as creating new opportunities for growth, a more circular economy will:

2. Reduce waste

3. drive greater resource productivity

4. deliver a more competitive UK economy.

5. position the UK to better address emerging resource security/scarcity issues in the future.

6. help reduce the environmental impacts of our production and consumption in both the UK and abroad.

A Networked Circular Economy

Not a Leakage Economy

Big Data Opportunities

Mutual Opportunities

Food Analytics• Sensors for occupancy, inside and outside

temperature, humidity, lighting levels, air speed, and air quality

Smart Facilities

• Utility smart meters and sub-meters for electricity, gas, and water

• Sensors for occupancy, inside and outside temperature, humidity, lighting levels, air speed, and air quality

• Smart equipment including building automation system gateways, solar inverters, and remotely monitored and controlled distributed generation

• External data sources such as weather forecasts, energy prices, and demand response signals

Energy Analytics

• Energy Analytics

• Which buildings or parts of the building have highest energy use per square foot?

• Are there similar buildings (square footage, space type, occupancy, HVAC type) that use less energy?

• What Energy Start rating do they have?

• Are the lights shutting off when the building is unoccupied?

• Is the economizer turning on when it should?

• Equipment Failure Prediction

• Are ducts leaking?

• Equipment Operations Optimization

Water Usage Analytics

• Benefits:

• Helps water engineers visualize water usage pattern

• Helps in identifying potential leakages

• Big Data Requirements:

• Data from water meters and dedicated sensors are sent toweb based warehouse

• Water usage could be visualized across service area or basedon specific account

• Level of usage are categorized and patterns are drawn

• Sudden and sharp changes in usage trends are used to traceleakages and breakages

• IBM has become a market leader in water usage analytics

Personalized Home Experience• Personalized Data are Saved:

• Usage preference data are saved for each user

• Indoor positioning system linked users with their preference.

• Techniques in Visible Light Communication (VLC), Bluetooth LowEnergy (BLE) and inertial device sensors are used.

• Huge data generated are automatically saved on cloud servers

• On Next Home Arrival

• Room automatically adjusts light.

• Sound and temperature are adjusted to your preferences everytime you enter.

• Kettle could anticipate and boil your water when you need it.

• Potential benefits

• This can let home owners feel very comfortable in their house

• Create competitive advantage for house builders

2

Supply Chain Collaborative BIM System for Minimising Waste in Projects

3

Supply Chain Collaborative BIM System for Minimising Waste in Projects

Aim

To develop an intelligent system using early supply chain involvement in projects.

Cost: £651,000Funding body: Innovate UKTimescale: 2014 - 2017Partners:

4

DRIM - Deconstruction and Recovery Information Modelling

5

DRIM - Deconstruction and Recovery Information Modelling

Aim

To develop an intelligence-based tool that will enable identification of reusable and recoverable materials at end-of-life of Projects.

Cost: £800,000Funding body: EPSRCTimescale: 2016 - 2019Partners:

6

Smart Cities Information Modelling (SCIM) Tool

7

Aim

To develop Smart Cities Information Modelling (SCIM) tool that will provide Impact estimator and optimum service planner.

Cost: £700,000Funding body: Innovate UKTimescale: 2016 - 2018Partners:

Conclusion

Big Data Analytics + Circular Economy

• Real Game Changer for delivering Sustainability• Reactive Decision making – What happened?

• Being very Proactive – What can we do better?

Thank You