cpa one 2016 - big data: big decisions or big fallacy

Big data big decisions or big fallacy

THE ONE NATIONAL CONFERENCE SEPTEMBER 19-20 2016 VANCOUVER BC

1Big data big decisions or big fallacy presented September 20 at

CPA CANADA THE ONE NATIONAL CONFERENCE 2016

What is big dataWhat is the language of big data and analyticsHow is it relevant for youWhat are the lessons learned so far

Laurie DesautelsDirector Digital

Part of the PwC network

1

2

3

4

Information is the oil of the 21st century and analytics the combustion enginemdash Peter Sondergaard Gartner







Is your organization hellip

Highly data-driven

Somewhat data-driven

Rarely data-driven

1

2

3



Source PwCs Global Data and Analytics Survey 2016 | Canadian insights

Organizations are seeking the right mix of mind and machine to leverage data understand risk and gain a competitive edge



SOURCE

What is big data

1

ldquoThe techniques and technologies that make handling data at extreme scale affordablerdquo ndash Forrester

ldquoBig data is high volume high velocity and high variety information assets requiring new forms of processingrdquo ndash Gartner





ldquoBig Data is all about finding correlations but Small Data

is all about finding the causation the reason whyrdquo

ndash Martin Lindstrom author of ldquoSmall Data The Tiny Clues That Uncover Huge Trendsrdquo

SOURCE



SOURCE

and this was from 2012

Everyday we create 25 quintillion bytes of

data ndash so much that 90 of the data in the world today has been created in the last two

years alone

Where does big data come from



The nature of the data keeps changing as the software platforms evolve

iMessage

2016

SOURCE httpwwwkpcbcominternet-trends



Ellen Degeneresrsquo tweet from the Oscarrsquos in 2014 had over 33m retweets SOURCE

Wal-Mart has 100000000 customers per weekSOURCE

In 2000 Sloan Digital Sky Survey collected more data in its first few weeks than the entire data collection in the history of astronomy

SOURCE

Sequencing the human genome originally took 10 years An ancestry DNA test can now be purchased for less than $200 and results received within a few weeks SOURCE

What does big data look like

The lexicon of big data



2

Big data has no value without the insights human expertise and

analytics can tease out of it

Analytics is the combustion engine of the information age



Examples

Techniques

Questions

Diagnosticdiscover amp explore

Why is it happening

Where is the problem

What are the trends

bull Agile Dashboards

bull Cause and effect

bull Correlations

bull Behavioral analytics

bull Data amp text mining

bull HALO

bull Risk Analytics

bull Rapid BI apps

bull Workforce analytics

bull Analytical apps

Prescriptiveanticipative

What should I do

What is the next best

action

bull Optimization

bull Artificial Intelligence

bull Machine learning

bull Simulations

bull Analytical apps with

simulated outcomes

Descriptivereporting

What happened

What is happening

bull Business Reporting

bull Scorecards

bull Business Intelligence

bull HALO

bull Financial performance

results

bull Staff performance

scorecards

Predictiveforecast

What is likely to

happen next

bull Predictive modeling and

statistical analytics

bull Regression analysis

bull Forecast modeling

bull Strategy amp growth analytics

bull Customer analytics

bull Fraud amp Cyber analytics

etc

The increasing value of analytics



Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive

Data has traditionally been actively captured Today data is increasingly passively captured

OT IoTThe Industrial

Internet



IT operational technologies (OT) and the internet of things (IoT) are converging to create the industrial internet

(or what PwC calls Industry 40)

Big data is an output of the industrial internet

Data and analytics are core competencies in this new world of Industry 40

IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB

new large scale data

that is semi-structured

unstructured or

unproven (with potential value)

proven structured and semi-

structured data sources

Multiple new technologies and the cloud deliver big data capabilities

What are the emerging data platforms

NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances

Distributed File System



The value of a data lake is in finding clues to help your organization answer high

priority questions

SOURCE

Modern data architectures leverage data lakes as a repository for large quantities and varieties of data both structured and unstructured

Value is created by using traditional and big data human and machine learning BI and analytics

Traditional mindset Big data mindset



Reporting Analysis Needs Discovery predictive

Large population with focused needs Audience Small user base with unfocused needs

Return on Investment Value for investment Option-creating investments

Waterfall Execution Iterative Agile

Model then store Approach Store then model

Transactional Sources Interactions

Internal Location Outside the company

Structured Format Semi-structured and un-structured

Business Intelligence Tools Analytics simulation visualization

SQL Languages MapReduce Embedded R etc

Relational Storage Data Lakes (Hadoop Cassandra Mongo etc)

Traditional ETL (Extract Transform Load) Integration Data wrangling late binding

Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists

Data Scientists Data Modelers Data Extraction

Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

SAS etc)

Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers

The data needs to tell a story but to get there you need a variety of skillsets



SOURCE

A great visualization

httpwwwinformationisbeautifulnetvis

ualizationsworlds-biggest-data-

breaches-hacks

What does it mean for you



3

SOURCE Artwork by David Somerville based on an original drawing by Hugh McLeod



days for finalization

of monthly annual

reports

Monthly amp

annual reportingBudgeting

Controlling

FTEs

Business

InsightsCost of Finance

days to complete

the budgetless FTEs in

Controlling than

peers

more time spent on

data analysis vs

data gathering

less cost of

Finance than

peers

+20 -40-203047

Source PwC Finance Effectiveness Benchmark amp Digital Controlling Study 2015

The finance function in best practice companies spend increased time generating insights from data



Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct

The concept of big data says you donrsquot know what data to collect because you donrsquot even know what the questions are now or in the future

Are you asking the right questions



GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question

Agile Analytics takes a ldquofast failrdquo approach to developing analytics solutions



A significant role for machines is emerging and companies are taking advantage of what machines offer

A machine learning exampleSource PwCrsquos Global Data and Analytics Survey July 2016 Q What will the analytis informing your next

strategic decision require Global base 2106 senior executives

Machine algorithms Human judgement



The new role of finance Balancing mind and machine

A spend-analysis machine (SAM) compiles and classifies

millions of financial transactions and gets smarter the more

data it processes

SAM finds optimization opportunities and makes timely

recommendationsmdashsuch as how much you could save by

taking advantage of volume discountsmdashenabling you to make

decisions on negotiations and spending to realize savings



ldquoOne thing is certain the profession is moving away from the basic bookkeeping chores toward the more sophisticated analytical tasksrdquondash Monique Morden Chief Revenue Officer at Lendified in Vancouver

Source ldquoI robot CPArdquo Yan Barlow CPA Magazine August 2-016

httpswwwcpacanadacaenconnecting-and-newscpa-magazinearticles2016augusti-robot-cpa

Do you need a decision diagnostic

What are the lessons learned to date



4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics

Low HighSophistication

Sp

eed

Lo

wH

igh

Decision Archetypes

bull Data-driven decisions trump intuition

bull Hindsight amp foresight with all available data

bull Slow consensus driven and analytic decisions

bull Intuition based decisions ndash little analysis

bull Descriptive reporting with internal data

bull Low frequency data and model refresh

bull Speedy decisions trump analysis consensus


bull Rapid analyse-decide-act feedback loop

bull Data amp intuition drive decisions


bull Advanced analytics with feedback loop

You must apply analytics for your big decisions For each type of decision what do you need



Improving both speed and sophistication helps maximize the return on investment for data and analytics

SOURCE

Increasing sophistication should

simplify not increase complexity

Speed is as much about structure as

it is about data and analytics



Enterprise

adoptionDeliver and scalePilot and proveDue diligenceIdeation

Innovation processes

The big data value chain intakes rough ideas on how to use information strategically and create actionable insights

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Refer Defer Kill Refer Defer Kill



Consumer attitudes are hardening as more data is gathered used shared and sold Lawmakers and regulators will respond



7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18

Infrastructure andor architecture

Obtaining skills and capabilities needed

Funding for Big Data-related initiatives

Risk and governance issues

Integrating multiple data sources

Defining our strategy

Understanding what is Big Data

Determining how to get value from Big Data

of respondents

Top challenge

2nd

Source Gartner Big Data Industry Insights

What are the top hurdles or challenges with big data

As the tools and philosophies of big data spread they will change long-

standing ideas about the value of experience the nature of expertise and

the practice of management



SOURCE

If yoursquore making decisions trusting data shouldnrsquot be holding you back What you should be thinking about is how to frame the problem how you can take advantage of the available data thatrsquos out there and what the strengths and weaknesses are of the approaches to use the data

mdash Dan DiFilippo Global and US Data amp Analytics Leader PwC



38

SOURCE

Big data big decisions or big fallacy presented September 20 at


39

copy 2016 PwC All rights reserved

PwC refers to the PwC network andor one or more of its member firms each of

which is a separate legal entity Please see wwwpwccomstructure for further details

This content is general information purposes only and should not be used as a

substitute for consultation with professional advisors

Thank you



lauriedesautelspwccom

wwwstrategyandpwccom



What is big dataWhat is the language of big data and analyticsHow is it relevant for youWhat are the lessons learned so far



1

2

3

4









Highly data-driven


Rarely data-driven

1

2

3







SOURCE

What is big data

1










SOURCE



SOURCE




years alone





iMessage

2016







SOURCE






2






Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



unstructured or






NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

SAS etc)

Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


days to complete


Controlling than

peers

more time spent on

data analysis vs

data gathering

less cost of

Finance than

peers

+20 -40-203047





Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you













Highly data-driven


Rarely data-driven

1

2

3







SOURCE

What is big data

1










SOURCE



SOURCE




years alone





iMessage

2016







SOURCE






2






Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



unstructured or






NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

SAS etc)

Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


days to complete


Controlling than

peers

more time spent on

data analysis vs

data gathering

less cost of

Finance than

peers

+20 -40-203047





Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you











SOURCE

What is big data

1










SOURCE



SOURCE




years alone





iMessage

2016







SOURCE






2






Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



unstructured or






NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

SAS etc)

Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


days to complete


Controlling than

peers

more time spent on

data analysis vs

data gathering

less cost of

Finance than

peers

+20 -40-203047





Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you














SOURCE



SOURCE




years alone





iMessage

2016







SOURCE






2






Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



unstructured or






NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

SAS etc)

Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

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Our customers

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can we prevent

that

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force behaving

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conduct





GoalWhat is the

question

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Identify

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data

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data

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Data

Did we

answer the

question













data it processes














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SOURCE




years alone





iMessage

2016







SOURCE






2






Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

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NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

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Info

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Te

ch

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Storytellers

Visualization

Specialists

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Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

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Creative

investigative

analytical minds

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Information and

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management skills

Statistical

programming

skills adept at

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techniques

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languages (R

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Programming

skills and

development

methodology

Application

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experience

Programming

skills with data

discovery and

mashingblending

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DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


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Controlling than

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more time spent on

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less cost of

Finance than

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+20 -40-203047





Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

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Decision Archetypes

















SOURCE







Enterprise




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7

8

8

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10

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12

15

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of respondents

Top challenge

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SOURCE





38

SOURCE



39






Thank you








iMessage

2016







SOURCE






2






Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



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NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

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log

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Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

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management skills

Statistical

programming

skills adept at

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techniques

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languages (R

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Programming

skills and

development

methodology

Application

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implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


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Controlling than

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more time spent on

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less cost of

Finance than

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Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

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Intelligence in

the moment

Cover the

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Sp

eed

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SOURCE







Enterprise




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7

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8

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10

12

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of respondents

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SOURCE





38

SOURCE



39






Thank you










SOURCE






2






Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



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NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

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data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

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Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


days to complete


Controlling than

peers

more time spent on

data analysis vs

data gathering

less cost of

Finance than

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+20 -40-203047





Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

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igh

Decision Archetypes

















SOURCE







Enterprise




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ce

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7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you








2






Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



unstructured or






NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

SAS etc)

Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


days to complete


Controlling than

peers

more time spent on

data analysis vs

data gathering

less cost of

Finance than

peers

+20 -40-203047





Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

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ce

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7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you







Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



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NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

SAS etc)

Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


days to complete


Controlling than

peers

more time spent on

data analysis vs

data gathering

less cost of

Finance than

peers

+20 -40-203047





Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




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ce

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7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you







Examples

Techniques

Questions


Why is it happening


What are the trends



bull Correlations



bull HALO

bull Risk Analytics

bull Rapid BI apps




What should I do


action

bull Optimization



bull Simulations


simulated outcomes


What happened

What is happening


bull Scorecards


bull HALO


results


scorecards

Predictiveforecast

What is likely to

happen next








etc




Active

Passive



Internet







IT



Volume

GB TB PB

EB ZB

Variety

Structured

unstructured

and semi-

structured such

clickstream text

image video

geolocation hellip

Velocity

Speed In which

analysis of data

occurs and data

is delivered for

analysis

Veracity

Uncertainty

predictability

and integrity of

data

The 4Vs of big data



data measured in TB

data measured in ZB



unstructured or






NoSQL DB

Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

on

Te

ch

no

log

y



Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

advanced

techniques

(algorithms) and

languages (R

SAS etc)

Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

mashingblending

large amounts of

data skills

DBMS skills data

extraction

transformation

load Detail

oriented to ensure

completeness and

accuracy

Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

FTEs

Business


days to complete


Controlling than

peers

more time spent on

data analysis vs

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Finance than

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+20 -40-203047





Our customers

are more

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How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

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Master the

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Intelligence in

the moment

Cover the

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eed

Lo

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igh

Decision Archetypes

















SOURCE







Enterprise




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7

8

8

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of respondents

Top challenge

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SOURCE





38

SOURCE



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Thank you







Active

Passive



Internet







IT



Volume

GB TB PB

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Variety

Structured

unstructured

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Velocity

Speed In which

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Veracity

Uncertainty

predictability

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The 4Vs of big data



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data measured in ZB



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NoSQL DB

Columnar DB

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priority questions

SOURCE


















Bu

sin

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Info

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Te

ch

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Storytellers

Visualization

Specialists

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Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

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Information and

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master data

management skills

Statistical

programming

skills adept at

advanced

techniques

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Programming

skills and

development

methodology

Application

development and

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experience

Programming

skills with data

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mashingblending

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Analytics

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SOURCE




breaches-hacks




3





of monthly annual

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Our customers

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How do we

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conduct





GoalWhat is the

question

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Identify

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data

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Data

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data it processes














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8

8

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of respondents

Top challenge

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SOURCE





38

SOURCE



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Thank you






Internet







IT



Volume

GB TB PB

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Variety

Structured

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structured such

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Velocity

Speed In which

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occurs and data

is delivered for

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Veracity

Uncertainty

predictability

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Columnar DB

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priority questions

SOURCE


















Bu

sin

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Info

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Te

ch

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Storytellers

Visualization

Specialists

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Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

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Information and

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master data

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Statistical

programming

skills adept at

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Programming

skills and

development

methodology

Application

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experience

Programming

skills with data

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mashingblending

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load Detail

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Analytics

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Implementers




SOURCE




breaches-hacks




3





of monthly annual

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Monthly amp


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Our customers

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GoalWhat is the

question

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Identify

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7

8

8

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of respondents

Top challenge

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38

SOURCE



39






Thank you







Volume

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Variety

Structured

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Velocity

Speed In which

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occurs and data

is delivered for

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Veracity

Uncertainty

predictability

and integrity of

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Columnar DB

NewSQL DB

Big Data Appliances





priority questions

SOURCE


















Bu

sin

ess

Info

rmati

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Te

ch

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Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

Information and

data architecture

data quality and

master data

management skills

Statistical

programming

skills adept at

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techniques

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languages (R

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Programming

skills and

development

methodology

Application

development and

implementation

experience

Programming

skills with data

discovery and

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load Detail

oriented to ensure

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Analytics

Applications

Implementers




SOURCE




breaches-hacks




3





of monthly annual

reports

Monthly amp


Controlling

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Controlling than

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more time spent on

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Our customers

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How do we

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What drives

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force behaving

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conduct





GoalWhat is the

question

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Identify

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4



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Master the

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7

7

8

8

8

12

15

26

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12

10

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of respondents

Top challenge

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SOURCE





38

SOURCE



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data measured in TB

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priority questions

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Bu

sin

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Info

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Te

ch

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Storytellers

Visualization

Specialists

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Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

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knowledge

Information and

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master data

management skills

Statistical

programming

skills adept at

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techniques

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languages (R

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skills and

development

methodology

Application

development and

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experience

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skills with data

discovery and

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load Detail

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3





of monthly annual

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Our customers

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How do we

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What drives

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GoalWhat is the

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8

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of respondents

Top challenge

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38

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priority questions

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Bu

sin

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Info

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Te

ch

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Storytellers

Visualization

Specialists

and Business

Analysts

Information

Architects

Master Data

Management

Specialists


Specialists

Ability to

communicate and

evangelize

Creative

investigative

analytical minds

with Industry or

business domain

knowledge

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master data

management skills

Statistical

programming

skills adept at

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techniques

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skills and

development

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3





of monthly annual

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GoalWhat is the

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of respondents

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Storytellers

Visualization

Specialists

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Information

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Master Data

Management

Specialists


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Ability to

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of monthly annual

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Our customers

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GoalWhat is the

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of respondents

Top challenge

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38

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Thank you







Storytellers

Visualization

Specialists

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Information

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Specialists


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Ability to

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of respondents

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of respondents

Top challenge

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38

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39






Thank you








of monthly annual

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customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you







Our customers

are more

sophisticated

How do we

provide better

value

What drives

customer

satisfaction in my

businessWho from my

team is likely to

leave and how

can we prevent

that

Is my sales

force behaving

with proper

conduct





GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you







GoalWhat is the

question

you are

asking

Identify

required

data

Obtain data

Prepare

data

Analyze

Data

Did we

answer the

question













data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you
















data it processes














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you














4



Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you







Accelerated

agility

Master the

chess moves

Intelligence in

the moment

Cover the

basics


Sp

eed

Lo

wH

igh

Decision Archetypes

















SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you








SOURCE







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you







Enterprise




IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

IT G

overn

an

ce

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Investm

en

t

Refer Defer Kill

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce

Bu

sin

ess G

overn

an

ce







7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you










7

7

8

8

8

12

15

26

11

12

10

10

12

15

4

18









of respondents

Top challenge

2nd








SOURCE





38

SOURCE



39






Thank you










SOURCE





38

SOURCE



39






Thank you









38

SOURCE



39






Thank you





39






Thank you



