paul blase, pwc presentation at the chief data & analytics officer forum, singapore

Decision Making in the Age of AI

Insights from PwC’s Big DecisionsTM Research

July 2016

PwC

Decision making models…..

2

“Guitar groups are on the way out.”Dick Rowe, Decca Records executive, 1962

2

I’m bringing you into the decision making process

Ruggles, here – flip this coin!

Problem Solving / Decision Making

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Think of a bad decision your company has made?

Think of a good decision your company has made?

What was the difference?

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Mind vs. Machine

10,000 Brains Anywhere, Anytime

To Trust or Not to Trust

Data Ecosystems

4 V’s of Data

Show Me a Picture, Please

What will you do differently?

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PwC’s 2016 global data and analytics surveyBig Decisionstm

5

Why

• Strategic decisions create value for an organisation.

• Decision-makers are now face-to-face with an opportunity to learn from massive amounts of data.

• How can we apply data analytics to create greater value?

What

• What types of decisions will you need to make between now and 2020?

• What types of data and analytics do these decisions require?

• What is the role of machines in decision making?

• What’s your ambition for improving your company’s decision speed and sophistication to make these decisions?

Who

• 2,100+ senior decision-makers

• 50+ countries • 15 industries

PwC‘s Global Data and Analytics Survey 2016: Big Decisions™

PwC

Developing or launching new products or services

Entering new markets with existing products or services

Developing Partnerships

Investment in IT

Change to business operations

Corporate restructuring or outsourcing

Entering a new industry or starting a new business

Shrinking existing business

Other Decision

0% 5% 10% 15% 20% 25% 30% 35%

Which one of the following best describes this key strategic decision?

Global

The leading “big decision” across Global Markets is “developing or launching new products” followed by “entering markets” and “investment in IT ”, all projected to increase shareholder value

Most Important Strategic Decisions & Impact

3

Across all strategic decision types, on average

90% of leadership thinks

their strategic decision will

increase shareholder value, with the majority estimating 5-50%

increase and 1/3rd estimating

50-200% increase


*n = total # of the top key coming strategic decisions

PwC

Organizations are in different stages in their approach to using data and analytics to support strategic decision making

Evolving capabilities, finding their way…..

Reconciling how to integrate “gut based” approach and

avoid bias…..

Hampered by structure…..

Somewhat detailed incorporating lengthy periods

for reflection and refinement. Hierarchical

validation within a fragmented decision

making structure

We are growing our use of complex

data sets and relying more and more on

external market data to make

decisions.

Generally analytics are rarely relied upon…from a business

perspective data does not drive our decisions.

We make decisions and then find supporting data to justify

them.

Fragmented & ad hoc

Especially manual data processing (low

speed and small

amount of data that we

can handle at on-time)

…improving...data is becoming more and more

key in decision making

It is patchy. There is still a noticeable reliance on gut based on what has been experienced in

the past.Continuing to evolve; have recently implemented big

data effort/strategy to enhance use

The use of comprehensive

analytics to inform pro-active decision

making7


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39%

53%

8%

Highly data-driven Somewhat data-drivenRarely data-driven

Most companies are not “highly data driven” and rely on descriptive and diagnostic analytics the most

Global

Which of the following best describes decision-making in your

organization?

Majority Aren’t Highly Data Driven.. …Or Using Predictive or Prescriptive

Similar pattern across industries

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*n = # of type of data-driven organization *n = # of type of data-driven organization by type of analytical technique applied

Descriptive (What has happened?)

Diagnostic (Why did it happen?)

Predictive (What will/could happen?)

Prescriptive (What should happen and

how?)

0%

5%

10%

15%

20%

25%

30%

35%

The use of analytics in your organization is mostly…

Highly data-driven Somewhat data-drivenRarely data driven

Global

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The new order will change the balance of algorithms and human judgment used in decision making and make “unknown” risks “known”

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Reliance on Judgment vs. Machine Analysis by Risk Profile (n= # of Decisions)

• Complement human judgment with machine algorithms (i.e. AI)

• Continuously improve algorithms

Strike the right balance of mind & machine….

• Know something your competitors don’t• Be the first to react to emerging, latent

demand• Migrate from “beta” to “alpha”

Address risks by making them known….


Opportunities

Stri

ke t

he r

ight

bal

ance

of

Min

d &

& M

achi

ne

Known Manageable…...….RISK…….….Unknown, Uncertain

Mac

hine

Alg

orith

ms.

...AN

ALYS

IS…

..Hum

an

Judg

emen

t

Address risks by making them known

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Finding the right mix of “mind and machine”…..Use Satellite Imagery to Size Markets

Collect data in novel ways…

Perform market sizing analysis in emerging markets…


Slum Tiles

Mid and High RiseMumbai

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Finding the right mix of “mind and machine”…..Use Drone Imagery to Assess Capital

Projects

Identify likely safety and code violations

Reduce cost overruns…


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Finding the right mix of “mind and machine”…..

1. Image Source: Lee, Grosse, Ranganath, and Ng. "Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations." (2009)

Assess Consumer Response to Design Changes

Explore higher number of varied designs…

Avoid designs that destroy perceived satisfaction…


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The use of human judgment and machine algorithms varies by country

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Singapore China Japan

United States Australia


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Companies are at different levels of maturity in decision making “speed” and “sophistication” to create value……

Speed• Time to answer question• Time to decide action• Time to implement / measure

Sophistication•Analytics maturity•Data breadth & depth•Decision approach

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Sophistication

Accelerated Agility

Master the Chess Moves

Intelligence in the

Moment

Cover the Basics

Low High

Low

Hig

hSp

eed

Increasing sophistication should simplify, not increase complexity

Speed is as much about structure as it is about data

& analytics

PwC’s Decision Sophistication & Speed Matrix (n=# of decisions)

PwC

Increase “speed” and “sophistication”

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Simulate Market Adoption of Personal Mobility Solutionsto Inform Investment Plans

More Speed…..

More Sophistication…..

• Detailed visualization of the strategies helped senior executives select the right strategies for each market and make strategic and operational decisions

• More than a million ‘consumer’ agents and their purchase choices were simulated based on deep causal reasoning

• Over 200k strategic scenarios were simulated to explore the ‘least regret’ strategy to enter and dominate markets

PwC

Increase “speed” and “sophistication”

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Machine Learning/NLP: Modeling Willingness to Pay

More Speed…..

More Sophistication…..

• Reduce time of market research

• Implement targeted outbound campaign messaging

• Leverage Word2Vec NLP techniques to go beyond “positive / negative sentiment”

• Design more targeted price points

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Ambition is high to improve decision speed and sophistication

Orange shows today; blue shows where companies want to be by 2020

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Global Singapore Rest of South East Asia


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Capabilities vary by country for speed and sophistication…

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Low

Hig

h

Singapore

China Japan

United States Australia


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Low

Hig

h

Spee

d

Low High

Sophistication

Low

Hig

h

Spee

d

Low High

Sophistication

Low

Hig

h

Spee

d

Low High

Sophistication

… and the same is true for industriesThe Insurance industry is known for advances in analytics. Compared with other sectors, they give today’s capabilities only modest marks.

Technology

Insurance

Government and Public Sector


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Organizations view leadership courage, budgetary constraints, and resource availability as barriers data driven decision making…

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Barriers to Decisions

The C-Suite is marginally more

confident in leadership

courage, with it’s top two concerns

being #1 Budgetary

considerations and #2 Available resource/manpo

wer


Leadership courage

Budgetary considerations

Availability of resource/manpower

Operational capacity

Policy regulations

Issues with implementation

Poor market response

Ability to analyse data

Data limitations

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%

The Decision will likely be limited by…

Global - Total*n = top decision by top limitation

PwC

PwC uses our Data & Analytics expertise to help our clients generate value by creating data driven organizations

Confidential

Data View Data Type Analytics Maturity

Need Account-ability

Workflow Targets Results

• Timely• Accurate• Comprehensive

• External• Internal

• Descriptive• Diagnostic• Predictive• Prescriptive

• Opportunistic• Proactive• Reactive

• Decision Rules• Person/Group• Org Links

• Steps• Decision Nodes• Impact Points

• Financial• Operational

• Financial• Operational• Gap to target

Business Value• Strategy Connection• Use Case Value Potential• Speed &

Sophistication Needs

Culture• Decision styles & biases• Experimentation• Visible Symbols• Machine / Mind Mix

Operating Model• Structure & Engagement • Talent Model• Metrics / Incentives• Repeatable Solutions

102030

40 50 607080

900 100

Dimension Maturity

Data-Driven Organization – 12 Dimensions

Ask why, close the loop

Discovery OutcomesDecisions/Actions

Insights/Answers

Data-Driven Decision Process

Trust data with judgement

Appropriate Speed Appropriate Sophistication

Technology• Big data• New technologies• Scalable arch• Managed data

Slide 22
