Analytics done well can transform the way that decisions are made across an organisation. The proliferation of data, matched with the accessibility of new technologies such as AI and Machine Learning, means answers to more and more business questions are within reach. Having a clear strategy for building a data driven culture, to realise the value in analytics, is now a business imperative. This presentation covered: • The Amazon Story: A Culture of Innovation and History of Machine Learning • Deloitte & Amazon: Perspectives on Building a Data Driven Culture • Customer Discussion: Predictions & practical advice This was presented in Australia and New Zealand in October 2018

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Vicky Falconer, Head of Analytics and Big Data - ANZ, AWS
Craig Stires, Head of AI, Analytics, Big Data - Asia Pacific, AWS
October 2018
Data Driven Decisions
Building an Insight Driven Culture

2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Inventing on behalf of the customer

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8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Innovation with Machine Learning at Amazon
Personalized
recommendations
Inventing
entirely new
customer
experiences
Fulfillment
automation
/ inventory
management
Drones Voice driven
interactions

9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Invention requires two things:
the ability to try a lot of experiments,
and not having to live with collateral damage of failed experiments
Andy Jassy, CEO Amazon Web Services

10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The Amazon Data Lake - Objectives
Provide an analytic ecosystem that
Scales with the Amazon Business
Leverage the broad AWS platform to
drive a better customer experience
for all Amazon customers
To Provide Choice and Options in New Analytic Technologies
- Focus on new analytic approaches including Machine Learning and Programmatic Analysis
- Enable both “Bring Your Own Cluster” and “Bring your Own Query” approaches

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Table Subscriptions - The VisionGoverned and secure - data subscriptions

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Quote
“We've had three big ideas at Amazon that we've stuck
with for 18 years, and they're the reason we're
successful: put the customer first, invent, and be patient”
- Jeff Bezos

15. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Perspectives on a data driven
culture…

16. How good are you at using insights to make decisions?
Stage 1
Analytically
Impaired
Stage 2
Localised
Analytics
Stage 4
Analytical
Companies
Stage 3
Analytical
Aspirations
Stage 5
Insight Driven
Organisation
Aware of analytics, but
little to no infrastructure
and poorly defined
analytics strategy
Adopting analytics,
building capability and
articulating an analytics
strategy in silos
Expanding ad-hoc
analytical capabilities
beyond silos and into
mainstream business
functions
Industrialising analytics
to aggregate & combine
data from broad sources
into meaningful content
and new ideas
Transforming analytics
to streamline decision
making across all
business functions

17. Being insight driven is all about…..
Process
Demand and
Prioritisation
Process
Re-engineering
Agility and
Scalability
Governance
Benefits
Realisation
Technology
Solution
Architecture
Vendor
Management
Cloud vs.
On Premises
Security &
Reliability
Sandbox &
Industrialising
Data
Data Quality
and
Management
Information
Model and
Data Sources
Regulation and
Compliance
Ethics and
Sharing
Privacy &
Security
People Talent
Change
Journey
Leadership
Knowledge
Management
Organisation
Design
Strategy
Stakeholder
Management
Analytics
Vision
Innovation
Value Drivers
and
Business Case
Operating
Model1. Asking the right
questions
2. Doing the
right analysis
3. Taking the right
actions
The building blocks of an IDO

18. DATA WAREHOUSING
BUSINESS INTELLIGENCE
ERP APPLICATIONS
DATA MODELING
CLOUD
VIRTUALISATION
INTERNET
OF THINGS
INTELLIGENT
AGENTS
TEXT
ANALYTICS
MACHINE LEARNING &
PREDICTIVE ANALYTICS
ARTIFICIAL INTELLIGENCE
& COGNITIVE ANALYTICS
CROWD-SOURCING
& COMPETITIONS
ADVANCED HUMAN
COMPUTER INTERFACE
TABLE STAKES MODERNISERS EXPONENTIALS
LEVELOFENTERPRISE-WIDEADOPTION
CYBER SECURITY
VISUALISATION DATA LAKES BIG DATA
… and applying the right technology for the right problem

19. Purple people help deliver meaningful (and actionable) insights
Testing and Validation
Data Querying /
Integration
Data
Modelling
Data Analysis
Reporting &
Analytics
Alignment to
Business Value
Macro-
Perspective
Business
Knowledge
Business
Commentary
Soft
Skills

20. Becoming an IDO requires an incremental approach
Operating
model
Iterative capabilities
and platform
IDO
Use Case 1
Use Case 2
Use Case 3
Use Case 4
Now
Incremental delivery of
value…
…driving strategic decisions

21. Vague Specific
And starts with asking the right “crunchy” question

22. Real life example:
How many public
transport services do I
need to bring forward
to evacuate the CBD in
less than 30 minutes
due to an emergency?
What do we mean by a crunchy question?

23. Where to start?
Digital Intent PredictionVideo Analytics Revenue Assurance
Cognitive Chatbots &
Predictions
IoT/Telematics Data Sharing

24. Bringing it together - AWS Deloitte ML lab
• Explore the Art of the Possible, through relevant
examples of global machine learning best practice
• Describe your vision for insight-driven decisions,
and understand your current IDO maturity
• Capture and prioritise crunchy questions, for
selection as an ML proof of concept

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Table Discussion
What one outcome is most
compelling to becoming a data
driven business?
What do you believe is (or was)
the hardest change in achieving
this?
Actions being intelligently produced from the collected
data.
Quality & Speed of decision making
More informed decision making.
Mitigation of risk
Engaging, empowering and taking the users on the
journey. The users need to realise the value in the
information they seek and use make informed decisions.
Exposing accurate information to allow the business to
self service, otherwise adoption is very difficult
Superior, revenue-generating, actionable growth insights
Educating stakeholders on capability and building the
business case
Communication between end-points and ensuring the
appropriate amount of data is being collected (not too
much or too little).
Culture and mindset
Finding a case for this in our business.
Reconciling outputs of models with traditional decision
making processes
Prioritising and obtaining near real time data sets from
"older" source systems
Lack of understanding by senior leadership

26. Bringing it together - AWS Deloitte ML lab
• Explore the Art of the Possible, through relevant
examples of global machine learning best practice
• Describe your vision for insight-driven decisions,
and understand your current IDO maturity
• Capture and prioritise crunchy questions, for
selection as an ML proof of concept

27. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Thank you…