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

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Big data: big decisions or big fallacy, presented September 20 at
CPA CANADA THE ONE NATIONAL CONFERENCE 2016
What is big data?
What is the language of big data and analytics?
How is it relevant for you?
What are the lessons learned so far?
Laurie Desautels
Director Digital
Part of the PwC network
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Information is the oil of the
21st century and analytics
the combustion engine.
— Peter Sondergaard, Gartner
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Is your organization …
Highly data-driven
Somewhat data-driven
Rarely data-driven
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Source: PwC's 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.

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@SOURCE
What is big data?
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“The techniques and technologies that
make handling data at extreme scale
affordable” – Forrester
“Big data is high volume, high velocity, and
high variety information assets requiring
new forms of processing” – Gartner
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“Big Data is all about finding
correlations, but Small Data
is all about finding the
causation, the reason why.”
– Martin Lindstrom, author of “Small Data:
The Tiny Clues That Uncover Huge Trends”
@SOURCE

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@SOURCE
and this was from 2012!
Everyday, we create
2.5 quintillion bytes of
data – 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?

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The nature of the data keeps changing as the software platforms evolve
iMessage
2016
@SOURCE: http://www.kpcb.com/internet-trends

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Ellen Degeneres’ tweet from the
Oscar’s in 2014 had over 3.3m
retweets.
@SOURCE
Wal-Mart has
100,000,000
customers per week
@SOURCE
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
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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

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Examples
Techniques
Questions
Diagnostic
discover & explore
Why is it happening?
Where is the problem?
What are the trends?
• Agile Dashboards
• Cause and effect
• Correlations
• Behavioral analytics
• Data & text mining
• HALO
• Risk Analytics
• Rapid BI apps
• Workforce analytics
• Analytical apps
Prescriptive
anticipative
What should I do?
What is the next best
action?
• Optimization
• Artificial Intelligence
• Machine learning
• Simulations
• Analytical apps with
simulated outcomes
Descriptive
reporting
What happened?
What is happening?
• Business Reporting
• Scorecards
• Business Intelligence
• HALO
• Financial performance
results
• Staff performance
scorecards
Predictive
forecast
What is likely to
happen next?
• Predictive modeling and
statistical analytics
• Regression analysis
• Forecast modeling
• Strategy & growth analytics
• Customer analytics
• Fraud & Cyber analytics,
etc.
The increasing value of analytics

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Examples
Techniques
Questions
Diagnostic
discover & explore
Why is it happening?
Where is the problem?
What are the trends?
• Agile Dashboards
• Cause and effect
• Correlations
• Behavioral analytics
• Data & text mining
• HALO
• Risk Analytics
• Rapid BI apps
• Workforce analytics
• Analytical apps
Prescriptive
anticipative
What should I do?
What is the next best
action?
• Optimization
• Artificial Intelligence
• Machine learning
• Simulations
• Analytical apps with
simulated outcomes
Descriptive
reporting
What happened?
What is happening?
• Business Reporting
• Scorecards
• Business Intelligence
• HALO
• Financial performance
results
• Staff performance
scorecards
Predictive
forecast
What is likely to
happen next?
• Predictive modeling and
statistical analytics
• Regression analysis
• Forecast modeling
• Strategy & growth analytics
• Customer analytics
• Fraud & Cyber analytics,
etc.
The increasing value of analytics

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Active
Passive
Data has traditionally been actively captured. Today, data is
increasingly passively captured.

OT IoT
The Industrial
Internet
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IT, operational technologies (OT) and the internet of things (IoT)
are converging to create the industrial internet
(or what PwC calls Industry 4.0)
Big data is an output of the industrial internet.
Data and analytics are core competencies in this new world of Industry 4.0.
IT

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



Volume
GB, TB, PB,
EB, ZB




Variety
Structured,
unstructured,
and semi-
structured such
clickstream, text,
image, video,
geolocation, …



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

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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

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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
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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
BusinessInformationTechnology

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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
mashing/blending
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

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@SOURCE
A great visualization ...
http://www.informationisbeautiful.net/vis
ualizations/worlds-biggest-data-
breaches-hacks/

What does it mean for you?
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3
@SOURCE: Artwork by David Somerville, based on an original drawing by Hugh McLeod

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days for finalization
of monthly / annual
reports
Monthly &
annual reporting
Budgeting
Controlling
FTEs
Business
Insights
Cost of Finance
days to complete
the budget
less FTEs in
Controlling than
peers
more time spent on
data analysis vs.
data gathering
less cost of
Finance than
peers
+20% -40%-20%304/7
Source: PwC, Finance Effectiveness Benchmark & Digital Controlling Study, 2015
The finance function in best practice companies spend
increased time generating insights from data

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Our customers
are more
sophisticated.
How do we
provide better
value?
What drives
customer
satisfaction in my
business?
Who 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 don’t know what data to collect because
you don’t even know what the questions are, now or in the future.
Are you asking the right questions?

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Goal
What is the
question
you are
asking?
Identify
required
data
Obtain data
Prepare
data
Analyze
Data
Did we
answer the
question?
Agile Analytics takes a “fast fail” approach to developing
analytics solutions

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A significant role for machines is emerging and companies are
taking advantage of what machines offer
A machine learning example
Source: PwC’s Global Data and Analytics Survey, July 2016. Q: What will the analytis informing your next
strategic decision require? Global base: 2,106 senior executives.
Machine algorithms Human judgement

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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
recommendations—such as how much you could save by
taking advantage of volume discounts—enabling you to make
decisions on negotiations and spending to realize savings.

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“One thing is certain: the
profession is moving away from
the basic bookkeeping chores
toward the more sophisticated
analytical tasks.”
– Monique Morden, Chief Revenue Officer at
Lendified in Vancouver
Source: “I robot, CPA”, Yan Barlow, CPA Magazine, August 2-016
https://www.cpacanada.ca/en/connecting-and-news/cpa-magazine/articles/2016/august/i-robot-cpa

Do you need a decision
diagnostic?
What are the lessons learned to date?
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Accelerated
agility
Master the
chess moves
Intelligence in
the moment
Cover the
basics
Low HighSophistication
SpeedLowHigh
Decision Archetypes
• Data-driven decisions trump intuition
• Hindsight & foresight with all available data
• Slow consensus driven and analytic decisions
• Intuition based decisions – little analysis
• Descriptive reporting with internal data
• Low frequency data and model refresh
• Speedy decisions trump analysis / consensus
• Descriptive reporting with internal data
• Rapid analyse-decide-act feedback loop
• Data & intuition drive decisions
• Hindsight & foresight with all available data
• Advanced analytics with feedback loop
You must apply analytics for your big decisions.
For each type of decision, what do you need?

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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

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Enterprise
adoption
Deliver 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
ITGovernance
ITGovernance
ITGovernance
ITGovernance
ITGovernance
Investment
Investment
Investment
Investment
Investment
Refer, Defer, Kill
BusinessGovernance
BusinessGovernance
BusinessGovernance
BusinessGovernance
BusinessGovernance
Refer, Defer, Kill Refer, Defer, Kill

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

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7%
7%
8%
8%
8%
12%
15%
26%
11%
12%
10%
10%
12%
15%
4%
18%
Infrastructure and/or 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.
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@SOURCE

If you’re making decisions, trusting data shouldn’t 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 that’s out there, and what the
strengths and weaknesses are of the approaches to use
the data.
— Dan DiFilippo, Global and U.S. Data & Analytics Leader, PwC
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@SOURCE

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© 2016 PwC. All rights reserved.
PwC refers to the PwC network and/or one or more of its member firms, each of
which is a separate legal entity. Please see www.pwc.com/structure 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.
Laurie Desautels
Director Digital
Part of the PwC network
laurie.desautels@pwc.com
www.strategyand.pwc.com

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