SlideShare a Scribd company logo
Mindfull - The Power of Predictive
AGENDA
WHO WE ARE
• Welcome
• Setting the scene
• Data science
• China Mobile
• IBM
• The Blues
• NZ Customs
• Mi Predict
• Questions
Mindfull - The Power of Predictive
3 in 10
7 in 10
2000 sensors
Solved by listening
A single cracked wheel can cause a derailment
Pattern Recognition
Mindfull - The Power of Predictive
Mindfull - The Power of Predictive
TALENT GAP
THE NEED…
DATA FOR DATA’S SAKE
THE NEED…
DATA AND DATA EVERYWHERE
THE NEED…
INFIGHTING
THE NEED…
AIMING TOO HIGH
THE NEED…
• Email / chat transcripts
• Call center notes
• Web click-streams
• In person dialogues
INTERACTION
DATA
USUALLY UNSTRUCTURED
• Opinions
• Preferences
• Needs & desires
• Survey results
• Social media
ATTITUDINAL
DATA
SEMI STRUCTURED
• Attributes
• Characteristics
• Self-declared info
• (Geo)demographics
DESCRIPTIVE
DATA
STRUCTURED
• Orders
• Transactions
• Payment history
• Usage history
BEHAVIORAL
DATA
STRUCTURED
DATA
MINING
Process of discovering meaningful correlations, patterns and
trends by sifting through large amounts of data stored in
repositories.
Data mining employs pattern recognition technologies, as
well as statistical and mathematical techniques
• Evolution of data analysts
• Amalgamation of Mathematics,
Computer Science, Applied
Statistics and Business
Knowledge
• Imagine driving…
DATA SCIENCE
APPLICATIONS
Acquire customers:
• Understand who your best
customers are
• Connect with them in the right
ways
• Take the best action maximize
what you sell to them
PREDICTIVE CUSTOMER ANALYTICS
Grow customers:
• Understand the best mix of things
needed by your customers and
channels
• Maximize the revenue received
from your customers and channels
• Take the best action every time to
interact
APPLICATIONS
PREDICTIVE CUSTOMER ANALYTICS
Retain customers:
• Understand what makes your
customers leave and what
makes them stay
• Keep your best customers
happy
• Take action to prevent them
from leaving
PREDICTIVE CUSTOMER ANALYTICS
APPLICATIONS
APPLICATIONS
PREDICTIVE OPERATIONAL ANALYTICS
Manage operations:
• Maximize the usage of your assets
• Make sure inventory and resources are in the
right place at the right time
• Identify the impact of investment
APPLICATIONS
PREDICTIVE OPERATIONAL ANALYTICS
Maintain infrastructure:
• Understand what causes
failure in your assets
• Maximize uptime of assets
• Reduce costs of upkeep
Maximize capital efficiency:
• Improve the efficiency and
effectiveness of your assets
• Reduce operational costs
• Drive operational excellence
in all phases: procurement,
development, availability and
distribution
APPLICATIONS
PREDICTIVE OPERATIONAL ANALYTICS
APPLICATIONS
Monitor environments:
• Identify leaks
• Increase compliance
• Leverage insights in critical
business functions
PREDICTIVE THREAT AND FRAUD ANALYTICS
Detect suspicious activity:
• Identify fraudulent patterns
• Reduce false positives
• Identity collusive and fraudulent
merchants and employees
• Identify unanticipated transaction
patterns
APPLICATIONS
PREDICTIVE THREAT AND FRAUD ANALYTICSPREDICTIVE THREAT AND FRAUD ANALYTICS
Control outcomes:
• Take action in real-time to
prevent abuse
• Reduce Claims Handling Time
• Alert clients of transaction fraud
APPLICATIONS
PREDICTIVE THREAT AND FRAUD ANALYTICSPREDICTIVE THREAT AND FRAUD ANALYTICS
PREDICTIVE
ANALYTICS
PRESCRIPTIVE
ANALYTICS
BIG DATA
ANALYTICS
DATA
EXPERTISE
CAPABILITIES
SUCCESS STORIES
• Telco Customer Segmentation and Churn
Prediction
– Zong China Mobile
• Predictive Maintenance and Quality – internal
IBM project
• Injury Prevention and Talent Identification
– Auckland Blues Rugby Franchise
CUSTOMER BEHAVIOR SEGMENTATION AND
CHURN PREDICTION ZONG CHINA MOBILE
Zong China Mobile’s key focus
was to build it’s advanced
analytics capability which could
enable them explore more
revenue channels, reduce the
increasing churn and monetise
their existing assets
Business challenge
• The client was facing a high customer
churn rate and the success of this project
was critical for client’s market dominance
• Churn models with 70% accuracy for
predicting timely churn were required
• The data provided for modeling was limited
and noisy
CUSTOMER BEHAVIOR SEGMENTATION AND
CHURN PREDICTION ZONG CHINA MOBILE
Solution
• Churn Prediction models for prepaid, post-paid and early
churn subscribers
• Customer Segmentation for identifying Behavioral
(Revenue, Usage, Dormancy) clusters
• Social network analysis – churn based on influencers
• Identification of Leaders and distinct User Groups
• Revenue Forecasting for zero balance subscribers and the
monthly value Migration model for complete subscriber
base.
• Best Bundle Prediction
CUSTOMER BEHAVIOR SEGMENTATION AND
CHURN PREDICTION ZONG CHINA MOBILE
Benefits
• Identify indicators of customer churn using CDRs
and call center data for tailoring marketing efforts
as a result
• 50% improvement in churn detection and an
initial reduction in average gross churn per month
of 0.43% (approx. 1 million customers).
• Churn reduction resulted saving on potential
revenue loss of half a million USD.
Components
• IBM SPSS Modeler
50%
CUSTOMER BEHAVIOR SEGMENTATION AND
CHURN PREDICTION ZONG CHINA MOBILE
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROJECT
Enable data-based decisions to
guide automation and
optimisation decisions to improve
operations, quality and customer
satisfaction
Business challenge
• Prevent outages from prescribing balanced
configuration of IT assets
• Intercept outages from detecting operational
patterns
• Optimise client experience by prioritisation of
requests
• Root cause isolate problems rapidly and identify
remediation
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROJECT
Solution
• Incident Reduction: Analyse structured and
unstructured tickets data to link incidents to top drivers by
usage type for remediation actions
• Defect Prevention: Analyse ticket data of various types
(i.e., change and incident) to identify patterns and triggers
that can be proactively fixed to prevent similar defects to
propagate
• Voice Operation Issues: Analyse Voice operations ticket
to identify issues from current operations.
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROJECT
Mindfull - The Power of Predictive
Benefits
INCREASED MEAN TIME
BETWEEN FAILURE
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS
REDUCED MEAN TIME
TO RESOLVE
Benefits
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS
COST REDUCTION
Benefits
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS
HARDWARE
COST DOWN
Benefits
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS
Components
IBM SPSS Modeler, ‘R’ and Python
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS
AUCKLAND BLUES
INJURY PREVENTION MODEL
Using sports performance tracking
devices, coupled with medical data
and well-being, Auckland Blues are
predicting the likelihood of a player
getting injured during the game and
practicing off-season. This helps Blues
to modify the training loads and
overcoming performance thresholds of
players.
Business challenge
• Decrease soft tissue injuries
• Study the thresholds of
performance for each player and
positions they play at
• Root cause analysis for known
injuries
AUCKLAND BLUES
INJURY PREVENTION MODEL
Solution
• Used rfid based tracking modules to
calculate the acceleration, distance
traveled, intensity and other factors to
track performance along with medical
data, past injuries, wellbeing and
demographic data to predict the likelihood
of injuries in players.
• Root cause analysis was done on the
known injuries to ensure future prevention
of injuries.
Components
• IBM SPSS Modeler – Batch Mode
AUCKLAND BLUES
INJURY PREVENTION MODEL
Gillian Warren
Manager Data Analytics
Intelligence, Investigations &
Enforcement
Mindfull - The Power of Predictive
The Customs context
• Facilitate trade and travel;
• Collect revenue (duties
and taxes);
• Protect the border
(together with MPI and
Police)
Risk Assessment
Increasing complexity of risks
• Globalisation
• Increased cross-border
movement by people and
goods
• Cross-border connectivity
Why an advanced analytics capability?
• Border transactions and passenger
movements continue to climb exponentially
• Do not have infinite resources at the frontline
• Must facilitate trade and travel as well as
“protect the border”
Increase in trade transactions
Imports: from approx $40b in 2007 to estimated $60b in 2018
Travel volumes are growing
0
50,000
100,000
150,000
200,000
250,000
2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14
Total arriving cruise ship passengers and crew processed by Customs
Cruise passengers: from approx 60,000 in 2006/07 to 235,000 in 2013/14
…and are forecast to increase
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
20002001200220032004200520062007200820092010201120122013201420152016201720182019
Other
US
UK
Korea
Japan
Germany
China
Canada
Australia
Passenger arrival volumes (historical and forecast) by nationalities (excluding New
Zealand passport holders)
Air Arrivals: from 1.7m in 2000 to estimated 3.2m in 2019
Current risk assessment process
Current system:
• Ahead of its time but now challenged by
volumes of data
• Relies on experience and knowledge of
officers to create alerts and profiles for
further examination purposes
Potential benefits?
Opportunity with big data tools to:
• Enlist tools to do the work human brains
cannot (e.g. correlate and interpret big data)
• Gain insights not currently available
• Use information strategically to prioritise work
and ensure effective interventions
Lessons we are learning along the way…
Understanding and preparing Customs for:
• How analytics is going to change the way we work, and even the
way we organise ourselves
• Making sure key resources are available to support analytics:
• Factor in resource for data wrangling/data infrastructure
• Right mix of staff capabilities for this organisation
• Staff are properly trained – on our data
Data data data!
• Change processes to improve the quality of our data – e.g. use
mandatory fields
• Data culture: not just in the backroom
INTRODUCING
DATA SCIENTIST…
IN A BOX!
• Investment up-front software
• Solution architecture and services fees
• Hardware cost
• On-going maintenance and updates
• Data Scientist headcount
• Investment between 250k – 750k*
TRADITIONAL
APPROACH
BUYING AND DEPLOYING SOLUTION
• Industry specific solutions
• Customer Analytics, Predictive Maintenance, Operational
Analytics and more
• Automated solutions (batched and real time) and available as
web service
• Embedded into existing infrastructure of your business
MI PREDICT
CLOUD HOSTED AND MANAGED SOLUTION
DEMO – MI PREDICT
DEMO – MI PREDICT
QUESTIONS

More Related Content

PPTX
CFO Symposium Presentation 'The Muldoon Syndrome'
PPTX
Business intelligence
PDF
Business Intelligence Presentation (1/2)
PPTX
Business intelligence ppt
PPTX
How Eastern Bank Uses Big Data to Better Serve and Protect its Customers
PDF
Barga Galvanize Sept 2015
PPTX
EY + Neo4j: Why graph technology makes sense for fraud detection and customer...
PPTX
2016 DSG Webinar Azure HDInsight 2 V4
CFO Symposium Presentation 'The Muldoon Syndrome'
Business intelligence
Business Intelligence Presentation (1/2)
Business intelligence ppt
How Eastern Bank Uses Big Data to Better Serve and Protect its Customers
Barga Galvanize Sept 2015
EY + Neo4j: Why graph technology makes sense for fraud detection and customer...
2016 DSG Webinar Azure HDInsight 2 V4

Similar to Mindfull - The Power of Predictive (20)

PPTX
2016 DSG Webinar Azure HDInsight 2 V4
PPTX
FinTech
PDF
ADV Slides: Data Curation for Artificial Intelligence Strategies
PDF
Novel analytics for gas stations
PDF
Artificial Intelligence high ROI case studies from around the world: approach...
PDF
Use of Analytics to recover from COVID19 hit economy
PDF
AI Solutions for Industries (short)
PPTX
Big Data solution for multi-national Bank
PPTX
FinTech
PPTX
FinTech
PDF
NZS-4555 - IT Analytics Keynote - IT Analytics for the Enterprise
PPTX
Ai powered credit card fraud detectionnn
PDF
Erfolgreicher agieren mit Analytics_Markus Barmettler_IBM Symposium 2013
PPTX
Applications of Data Science in Banking Sector.pptx
PPTX
Abidin, zainal IBM Software "Data is a New Oil"
PDF
Implementing Advanced Analytics Platform
PDF
Transforming GE Healthcare with Data Platform Strategy
PPTX
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
PDF
Barga ACM DEBS 2013 Keynote
PPTX
Fractional Chief AI Officer Services For Hire
2016 DSG Webinar Azure HDInsight 2 V4
FinTech
ADV Slides: Data Curation for Artificial Intelligence Strategies
Novel analytics for gas stations
Artificial Intelligence high ROI case studies from around the world: approach...
Use of Analytics to recover from COVID19 hit economy
AI Solutions for Industries (short)
Big Data solution for multi-national Bank
FinTech
FinTech
NZS-4555 - IT Analytics Keynote - IT Analytics for the Enterprise
Ai powered credit card fraud detectionnn
Erfolgreicher agieren mit Analytics_Markus Barmettler_IBM Symposium 2013
Applications of Data Science in Banking Sector.pptx
Abidin, zainal IBM Software "Data is a New Oil"
Implementing Advanced Analytics Platform
Transforming GE Healthcare with Data Platform Strategy
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
Barga ACM DEBS 2013 Keynote
Fractional Chief AI Officer Services For Hire
Ad

Recently uploaded (20)

PPTX
mbdjdhjjodule 5-1 rhfhhfjtjjhafbrhfnfbbfnb
PPTX
Introduction-to-Cloud-ComputingFinal.pptx
PPT
Miokarditis (Inflamasi pada Otot Jantung)
PPTX
climate analysis of Dhaka ,Banglades.pptx
PPTX
STUDY DESIGN details- Lt Col Maksud (21).pptx
PPTX
1_Introduction to advance data techniques.pptx
PDF
Galatica Smart Energy Infrastructure Startup Pitch Deck
PDF
.pdf is not working space design for the following data for the following dat...
PPTX
iec ppt-1 pptx icmr ppt on rehabilitation.pptx
PPTX
advance b rammar.pptxfdgdfgdfsgdfgsdgfdfgdfgsdfgdfgdfg
PPTX
Introduction to Knowledge Engineering Part 1
PPT
Quality review (1)_presentation of this 21
PDF
Fluorescence-microscope_Botany_detailed content
PDF
Launch Your Data Science Career in Kochi – 2025
PDF
22.Patil - Early prediction of Alzheimer’s disease using convolutional neural...
PPTX
Introduction to Basics of Ethical Hacking and Penetration Testing -Unit No. 1...
PPTX
The THESIS FINAL-DEFENSE-PRESENTATION.pptx
PPTX
Data_Analytics_and_PowerBI_Presentation.pptx
PDF
BF and FI - Blockchain, fintech and Financial Innovation Lesson 2.pdf
PPTX
Supervised vs unsupervised machine learning algorithms
mbdjdhjjodule 5-1 rhfhhfjtjjhafbrhfnfbbfnb
Introduction-to-Cloud-ComputingFinal.pptx
Miokarditis (Inflamasi pada Otot Jantung)
climate analysis of Dhaka ,Banglades.pptx
STUDY DESIGN details- Lt Col Maksud (21).pptx
1_Introduction to advance data techniques.pptx
Galatica Smart Energy Infrastructure Startup Pitch Deck
.pdf is not working space design for the following data for the following dat...
iec ppt-1 pptx icmr ppt on rehabilitation.pptx
advance b rammar.pptxfdgdfgdfsgdfgsdgfdfgdfgsdfgdfgdfg
Introduction to Knowledge Engineering Part 1
Quality review (1)_presentation of this 21
Fluorescence-microscope_Botany_detailed content
Launch Your Data Science Career in Kochi – 2025
22.Patil - Early prediction of Alzheimer’s disease using convolutional neural...
Introduction to Basics of Ethical Hacking and Penetration Testing -Unit No. 1...
The THESIS FINAL-DEFENSE-PRESENTATION.pptx
Data_Analytics_and_PowerBI_Presentation.pptx
BF and FI - Blockchain, fintech and Financial Innovation Lesson 2.pdf
Supervised vs unsupervised machine learning algorithms
Ad

Mindfull - The Power of Predictive

  • 2. AGENDA WHO WE ARE • Welcome • Setting the scene • Data science • China Mobile • IBM • The Blues • NZ Customs • Mi Predict • Questions
  • 7. Solved by listening A single cracked wheel can cause a derailment
  • 12. DATA FOR DATA’S SAKE THE NEED…
  • 13. DATA AND DATA EVERYWHERE THE NEED…
  • 16. • Email / chat transcripts • Call center notes • Web click-streams • In person dialogues INTERACTION DATA USUALLY UNSTRUCTURED
  • 17. • Opinions • Preferences • Needs & desires • Survey results • Social media ATTITUDINAL DATA SEMI STRUCTURED
  • 18. • Attributes • Characteristics • Self-declared info • (Geo)demographics DESCRIPTIVE DATA STRUCTURED
  • 19. • Orders • Transactions • Payment history • Usage history BEHAVIORAL DATA STRUCTURED
  • 20. DATA MINING Process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories. Data mining employs pattern recognition technologies, as well as statistical and mathematical techniques
  • 21. • Evolution of data analysts • Amalgamation of Mathematics, Computer Science, Applied Statistics and Business Knowledge • Imagine driving… DATA SCIENCE
  • 22. APPLICATIONS Acquire customers: • Understand who your best customers are • Connect with them in the right ways • Take the best action maximize what you sell to them PREDICTIVE CUSTOMER ANALYTICS
  • 23. Grow customers: • Understand the best mix of things needed by your customers and channels • Maximize the revenue received from your customers and channels • Take the best action every time to interact APPLICATIONS PREDICTIVE CUSTOMER ANALYTICS
  • 24. Retain customers: • Understand what makes your customers leave and what makes them stay • Keep your best customers happy • Take action to prevent them from leaving PREDICTIVE CUSTOMER ANALYTICS APPLICATIONS
  • 25. APPLICATIONS PREDICTIVE OPERATIONAL ANALYTICS Manage operations: • Maximize the usage of your assets • Make sure inventory and resources are in the right place at the right time • Identify the impact of investment
  • 26. APPLICATIONS PREDICTIVE OPERATIONAL ANALYTICS Maintain infrastructure: • Understand what causes failure in your assets • Maximize uptime of assets • Reduce costs of upkeep
  • 27. Maximize capital efficiency: • Improve the efficiency and effectiveness of your assets • Reduce operational costs • Drive operational excellence in all phases: procurement, development, availability and distribution APPLICATIONS PREDICTIVE OPERATIONAL ANALYTICS
  • 28. APPLICATIONS Monitor environments: • Identify leaks • Increase compliance • Leverage insights in critical business functions PREDICTIVE THREAT AND FRAUD ANALYTICS
  • 29. Detect suspicious activity: • Identify fraudulent patterns • Reduce false positives • Identity collusive and fraudulent merchants and employees • Identify unanticipated transaction patterns APPLICATIONS PREDICTIVE THREAT AND FRAUD ANALYTICSPREDICTIVE THREAT AND FRAUD ANALYTICS
  • 30. Control outcomes: • Take action in real-time to prevent abuse • Reduce Claims Handling Time • Alert clients of transaction fraud APPLICATIONS PREDICTIVE THREAT AND FRAUD ANALYTICSPREDICTIVE THREAT AND FRAUD ANALYTICS
  • 32. SUCCESS STORIES • Telco Customer Segmentation and Churn Prediction – Zong China Mobile • Predictive Maintenance and Quality – internal IBM project • Injury Prevention and Talent Identification – Auckland Blues Rugby Franchise
  • 33. CUSTOMER BEHAVIOR SEGMENTATION AND CHURN PREDICTION ZONG CHINA MOBILE Zong China Mobile’s key focus was to build it’s advanced analytics capability which could enable them explore more revenue channels, reduce the increasing churn and monetise their existing assets
  • 34. Business challenge • The client was facing a high customer churn rate and the success of this project was critical for client’s market dominance • Churn models with 70% accuracy for predicting timely churn were required • The data provided for modeling was limited and noisy CUSTOMER BEHAVIOR SEGMENTATION AND CHURN PREDICTION ZONG CHINA MOBILE
  • 35. Solution • Churn Prediction models for prepaid, post-paid and early churn subscribers • Customer Segmentation for identifying Behavioral (Revenue, Usage, Dormancy) clusters • Social network analysis – churn based on influencers • Identification of Leaders and distinct User Groups • Revenue Forecasting for zero balance subscribers and the monthly value Migration model for complete subscriber base. • Best Bundle Prediction CUSTOMER BEHAVIOR SEGMENTATION AND CHURN PREDICTION ZONG CHINA MOBILE
  • 36. Benefits • Identify indicators of customer churn using CDRs and call center data for tailoring marketing efforts as a result • 50% improvement in churn detection and an initial reduction in average gross churn per month of 0.43% (approx. 1 million customers). • Churn reduction resulted saving on potential revenue loss of half a million USD. Components • IBM SPSS Modeler 50% CUSTOMER BEHAVIOR SEGMENTATION AND CHURN PREDICTION ZONG CHINA MOBILE
  • 37. IT OPERATIONAL ANALYTICS INTERNAL IBM PROJECT Enable data-based decisions to guide automation and optimisation decisions to improve operations, quality and customer satisfaction
  • 38. Business challenge • Prevent outages from prescribing balanced configuration of IT assets • Intercept outages from detecting operational patterns • Optimise client experience by prioritisation of requests • Root cause isolate problems rapidly and identify remediation IT OPERATIONAL ANALYTICS INTERNAL IBM PROJECT
  • 39. Solution • Incident Reduction: Analyse structured and unstructured tickets data to link incidents to top drivers by usage type for remediation actions • Defect Prevention: Analyse ticket data of various types (i.e., change and incident) to identify patterns and triggers that can be proactively fixed to prevent similar defects to propagate • Voice Operation Issues: Analyse Voice operations ticket to identify issues from current operations. IT OPERATIONAL ANALYTICS INTERNAL IBM PROJECT
  • 41. Benefits INCREASED MEAN TIME BETWEEN FAILURE IT OPERATIONAL ANALYTICS INTERNAL IBM PROCESS - GTS
  • 42. REDUCED MEAN TIME TO RESOLVE Benefits IT OPERATIONAL ANALYTICS INTERNAL IBM PROCESS - GTS
  • 43. COST REDUCTION Benefits IT OPERATIONAL ANALYTICS INTERNAL IBM PROCESS - GTS
  • 44. HARDWARE COST DOWN Benefits IT OPERATIONAL ANALYTICS INTERNAL IBM PROCESS - GTS
  • 45. Components IBM SPSS Modeler, ‘R’ and Python IT OPERATIONAL ANALYTICS INTERNAL IBM PROCESS - GTS
  • 46. AUCKLAND BLUES INJURY PREVENTION MODEL Using sports performance tracking devices, coupled with medical data and well-being, Auckland Blues are predicting the likelihood of a player getting injured during the game and practicing off-season. This helps Blues to modify the training loads and overcoming performance thresholds of players.
  • 47. Business challenge • Decrease soft tissue injuries • Study the thresholds of performance for each player and positions they play at • Root cause analysis for known injuries AUCKLAND BLUES INJURY PREVENTION MODEL
  • 48. Solution • Used rfid based tracking modules to calculate the acceleration, distance traveled, intensity and other factors to track performance along with medical data, past injuries, wellbeing and demographic data to predict the likelihood of injuries in players. • Root cause analysis was done on the known injuries to ensure future prevention of injuries. Components • IBM SPSS Modeler – Batch Mode AUCKLAND BLUES INJURY PREVENTION MODEL
  • 49. Gillian Warren Manager Data Analytics Intelligence, Investigations & Enforcement
  • 51. The Customs context • Facilitate trade and travel; • Collect revenue (duties and taxes); • Protect the border (together with MPI and Police)
  • 52. Risk Assessment Increasing complexity of risks • Globalisation • Increased cross-border movement by people and goods • Cross-border connectivity
  • 53. Why an advanced analytics capability? • Border transactions and passenger movements continue to climb exponentially • Do not have infinite resources at the frontline • Must facilitate trade and travel as well as “protect the border”
  • 54. Increase in trade transactions Imports: from approx $40b in 2007 to estimated $60b in 2018
  • 55. Travel volumes are growing 0 50,000 100,000 150,000 200,000 250,000 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 Total arriving cruise ship passengers and crew processed by Customs Cruise passengers: from approx 60,000 in 2006/07 to 235,000 in 2013/14
  • 56. …and are forecast to increase 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 20002001200220032004200520062007200820092010201120122013201420152016201720182019 Other US UK Korea Japan Germany China Canada Australia Passenger arrival volumes (historical and forecast) by nationalities (excluding New Zealand passport holders) Air Arrivals: from 1.7m in 2000 to estimated 3.2m in 2019
  • 57. Current risk assessment process Current system: • Ahead of its time but now challenged by volumes of data • Relies on experience and knowledge of officers to create alerts and profiles for further examination purposes
  • 58. Potential benefits? Opportunity with big data tools to: • Enlist tools to do the work human brains cannot (e.g. correlate and interpret big data) • Gain insights not currently available • Use information strategically to prioritise work and ensure effective interventions
  • 59. Lessons we are learning along the way… Understanding and preparing Customs for: • How analytics is going to change the way we work, and even the way we organise ourselves • Making sure key resources are available to support analytics: • Factor in resource for data wrangling/data infrastructure • Right mix of staff capabilities for this organisation • Staff are properly trained – on our data Data data data! • Change processes to improve the quality of our data – e.g. use mandatory fields • Data culture: not just in the backroom
  • 62. • Investment up-front software • Solution architecture and services fees • Hardware cost • On-going maintenance and updates • Data Scientist headcount • Investment between 250k – 750k* TRADITIONAL APPROACH BUYING AND DEPLOYING SOLUTION
  • 63. • Industry specific solutions • Customer Analytics, Predictive Maintenance, Operational Analytics and more • Automated solutions (batched and real time) and available as web service • Embedded into existing infrastructure of your business MI PREDICT CLOUD HOSTED AND MANAGED SOLUTION
  • 64. DEMO – MI PREDICT
  • 65. DEMO – MI PREDICT

Editor's Notes

  • #4: Quick Intro to what Predictive Analytics is all about. https://www.youtube.com/watch?v=w1-hbFOytNg&index=6&list=PLd3MHD3LUqCw6YOpAw-iOw3xknNWaWM-4
  • #5: Check this out https://youtu.be/iiv4X1K7iX4 What are the factors that determine their likelihood to do so? It’s not just grades… Distance Multiple jobs- night shift One with other responsibilities
  • #6: Who can predict the intentions of a storm cloud? Power companies have to. Seven out of ten power outages in the US are caused by weather. Utilities can’t send crews to chase every storm So they’re working with IBM to combine micro weather forecasts with detailed local data from sensors analyzing Topography soil saturation even the number of trees. So they can predict within a few city blocks where an outage is most likely to occur And send crews exactly where they’re needed when they’re needed. https://youtu.be/cnaAL7wUzPw
  • #7: Two thousand sensors detecting changes in temperature, Vibration, alignment. https://youtu.be/hDFe2uWxFO8
  • #8: A single cracked wheel can cause a derailment Out of more than one and a half million wheels, how do you find the right one? By listening You may not be able to hear it But analytics can. a railroad analyzes one hundred thousand data points a day To help stop trouble before it starts.
  • #9: MRI’s growing by 10% year on Year More and more, data is visual. In fact, the number of MRIs has increased by ten percent a year. And a radiologist might view a thousand images to find one tiny abnormality in shape, contrast or movement. Because it’s so challenging, Analytics is being used to help clinicians spot key patterns quickly and precisely. https://youtu.be/hH2pCP5fBEk
  • #10: https://youtu.be/Mi4ZPtKSbMs In Africa 1 in 5 malaria pills are counterfeit. Predictive analytics is being used to detect which ones are. We’ll here more about this sort of thing in Gillian’s presentation.
  • #11: Handing over Farhad. Hear is a perspective on how you can use predictive to reshape customer experiences. https://www.youtube.com/watch?v=b82Quinl7ac
  • #21: Setting the scene: Data analysts vs data scientists, how they are different and what that means for organisations that want to drive actionable insights out of their data using predictive analytics.
  • #41: https://www.youtube.com/watch?v=hDFe2uWxFO8&list=PLd3MHD3LUqCw6YOpAw-iOw3xknNWaWM-4&index=2
  • #62: Setting the scene: Wrapping up
  • #63: Make it a range of numbers – consult with sales guy for real figures (approximate) 250 k - 750k