PwC

Digital Transformation Data Driven Insights

9 November 2019

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The power of Big Data

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Global Big Data volumes in Zettabytes

Data volumes to rise to 163 zettabytes by 2025,

Big data is becoming a necessitating

1 2 3

Key Trends

Businesses becoming Analytics Driven Organisations

Businesses driven by Analytics will see $430 bn

in productivity benefits over competition by 2020

Businesses want to monetise data

By 2020, 90 percent of large enterprises will

generate revenue, selling raw or derived data

1 International Institute for Analytics

Amazon

How “Data” becomes “Big Data”

01 Big Data drivers

• Analytics is the new business driver

• Growing demands of real-time insight monitoring

• Coming data complexity is what creates change

• Data storage costs

02 Big Data Challenges

• Variety

• Security

• Technology

• Data management

• Culture/changing the organisation

03 Big Data Needs

• Need for advanced analytics

• Visualisation

• Increase of the demand of data scientist

• Increase the data storage capacities

Internet of things is 50 billion connections by 2020, 500 billion connections by 2030,

data will be everywhere.

Source: CISCO

Velocity

VarietyVolume

Source: Gartner 2015: IoT World Forum 2015

Average data volume stored in 1000+ people companies

InterpersonalElectronic communications, mails, social networks …

Man/MachineDigital data, Credit card archives, browser history …

Inter-machineCaptors, GPS, cameras

250 BillionE-mail sent a day

50 MillionTweets posted a day

165 MillionBank transactions a day in the Eurozone

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What is big data?

“Big data is data that exceeds the processing capacity of conventional database systems. The data is

too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from

this data, you must choose an alternative way to process it. “

- Ed Dumbill, Principal Analyst, O’Reilly

Data Sources

Sensors

Activities, conversations

Browser Logs

Social Media

Photos, videos

etc

Volume

Velocity

Variety

Veracity

Analysing Data

Sentiment analysis

Text analytics

Voice analytics

Face recognition

Movement analytics

etc

V

a

l

u

e

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Structured vs Unstructured Data

So what exactly makes big data so difficult for normal data processing tools to handle? The answer is unstructured data, which makes up a large portion of data being generated.

Structured data

• Usually text files

• Displayed in a format which can be easily ordered and processed by data mining tools.

• Makes up less than 50% of the information available for use.

• Metadata, relational databases, flat files

Unstructured data

• Usually binary data

• No identifiable internal structure

• Useless until contents are identified and organized

• Email, video, audio, social media posts

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Big Data trends to watch today

Big Data &

Analytics

Cloud Big Data

Challenges

Analytic Apps Data

scientist & beyond

The internet of

things

Data governance

Data as a service

Real-time insight

NOSQL databases

Security & regulatory

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Questions asked in CEO Survey:

Thinking about the data that you personally use to make decisions about the long-term success and durability of your business, how important are the following? (showing only ‘critical/important’)

How adequate is the data that you receive? (showing only ‘comprehensive’)

CEOs face issues with their own capabilities, mostly in terms of data adequacy, with a huge gap that remains ten years on.

According to PwC 22nd Annual Global CEO Survey (2019)

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Resolving information gap is a must to feed the AI Engine

85% of CEOs agree that Artificial Intelligences (“AI”) will significantly change the way they do business in next five years. Most Asia Pacific CEOs considers AI have a larger impact to their business than the internet.Therefore resolving information and talent gaps is a critical barrier to successfully exploiting the benefits of AI.

34

38

28

46

49

45

60

42

North America

Western Europe

Africa

Latin American

CEE

Asia-Pacific

Middle East

Global

Source: PwC, 22nd Annual Global CEO Survey Strongly disagree Disagree Agree Strongly agree

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While 51% of financially successful

companies have embraced the

most advanced definitions of

digital, that's just a start.

The path to success rests on four

core pillars, where top performers

excel.

Transform

Companies say they’re

digital, but many aren’t

investing and behaving

digitally. It’s time for them to

change.

Leadership

Employees have more

digital know-how than

leadership does. That’s a

problem. Leaders need

more knowledge and new

ways of managing to

succeed.

Workforce

There’s a disconnect

between the skills and

technologies that

companies say matter most

and what they’re investing

in. More than half don’t

even have a structure for

delivering training.

Disruption

Only 31% of companies say

digital disruption is a threat

to their business. They’re

wrong. But it’s not too late.

2Confidential information for the sole benefit and use of PwC’s client.

What is Video Analytics (VA)?

It is a mathematical algorithm to

monitor, analyze and manage large

volumes of videos in near ‘Real-time’

Digitally analyzes video and image

inputs - transforming them into

intelligent data which help clients in

taking smart decisions for become

Data-driven.

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Dashboard

Store A - Analysis

4,300,320HKD

Sales MTD

300,320HKD

Sales DTD

4.2 %

Conversion

4,185

Storefront Traffic

6 mins

Zone C

1 mins

Zone E

5 mins

Zone D

4 mins

Zone F

Store Heatmap

12 mins

Zone A

7 mins

Zone B

0

50

100

150

200

10-Apr 11-Apr 12-Apr 13-Apr 14-Apr 15-Apr 16-Apr 17-Apr 18-Apr 19-Apr 20-Apr

Store Footfall

Visit Duration

14 mins. 31 secs

56%26%

14%

4%

0 - 5 Mins

5 - 10 Mins

10 - 20 Mins

> 30 Mins

154

In-store Traffic

5.1%vs. Lt month vs. Last day

0.1%

vs. Last day

1mins 06secs

0.2%

vs. Last day

8.3%

0.1%

vs. Last dayvs. Last day

c

Art of the possible

ObjectDetection

Customer

Flow

People

Counting

Inventory

Monitoring

Brand

Analysis

Integrated

Marketing

Customer

Behaviour

Security

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Prescriptive…so what should I do?

Leverage descriptive and predictive analytics to develop optimal actions to achieve defined results.

Engagement & Information Scale & Action

Predictive

What is likely to happen?

Historical Future

Predictive modeling of likely results using statistical and machine learning techniques

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

FY10 FY11 FY12 FY13 FY14 FY15 FY16 FY17 FY18 FY19 FY20

Performing analytics

Descriptive

What happened?

Gain insight from historical data with reporting, scorecards and trend analysis

0

1

2

3

4

5

6

FY10 FY11 FY12 FY13 FY14

Strategic ActionsEnterprise

Value Impact

1. Invest in X, Y & Z

businesses…$1.5bn

2. Acquire capability in the

following adjacent

markets…

$750m

3. Divest the following non-

core businesses/brands….

$500m

4. Expand into the following

territories…

$1.0m

Probability weighted EV

impact (over 5 year period)$3.0bn

Diagnostic

Why did it happen?

Examine data or content to answer questions using drill-down, data discovery, mining and correlations

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Turning data into actionable insight

Manage

data

Information, both

quantitative and

qualitative

Perform

analytics

Data discovery and

insights driving

decisions and actions

Create

visualisations

Visual representation

of data for faster,

easier, and enhanced

understanding

Generate

insights

Application of

analytics for more

insights, better

decision making

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Enhanced data visualization presents a significant opportunity to optimize business efficiency

Enhanced Performance Accountability

Impactful KPI Measurement

Targeted Resource Allocation

Proactive ProblemSolutioning

Financial Processes Analyser (FPA)Discover unique insights into suspicious transactions and business

processes in one integrated application

.

Trends

View transaction

processed trends and

drill down on analysis

details.

Data quality

Assess quality of data

and take remediation

actions when required.

Q&A Virtual Analyst

Questions

answered using

intuitive natural

language

capabilities.

Continuous Controls

Monitoring (CCM)

Workflow,

exception

management and

reporting in one

integrated system.

Detect fraud

Strong fraud lens identifies

fraudulent transaction

patterns.

Management insights

CCM process provides high level

of comfort for management and

deeper insights to support

financial reporting and disclosure

requirements.

Increase productivity

Hundreds of man-hours

traditionally spent on gathering

audit support documents will be

reduced leading to increased

productivity.

Results

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“In the hands of the skilled CFO, data analytics can bring

tremendous rewards, including boosting revenue

and profits, increasing operational efficiency and

enhancing financial reporting and communication”

“Fluency with data analytics is becoming a

key differentiator for high-performing CFOs”

Unlocking the opportunities for CFOs

*Source: "Capitalising on the promise of Big Data: How a buzzword morphed into a leading trend that will transform the way to do business", Jan 2013

of information technology and business executives agree that using data analytics can create business advantages*.

“CFOs are under pressure to base more of his or her decisions on data than on

intuition”

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Taking the lead to build the company’s data & analytics

DataInsights

What CFO’s should do now• Educate themselves on data analytics and

identify technology / data currently available within the organisation

• Partner with other functions to identify existing or potential projects and analytics skills

• Identify opportunities to draw better insights from existing data

• Leverage emerging low-cost IT Solutions such as cloud computing

• Start small with a proof of concept / pilot projects

Short-term actions• Use successful pilot projects to

demonstrate potential and gain buy-in from business sponsors to support analytics initiatives

• Assess existing technology limitations and skills gaps to determine required technology investments and strengthen existing finance team by hiring externally

• Identify and incorporate additional data sources, either internal or external

• Establish data governance strategy and processes

Longer-term actions• Engage in business process reengineering

based on new insights uncovered by analytics

• Maintain a closed-loop learning environment based on data-driven decision making, and adjust investment priorities

• Continue to support analytics initiatives through balanced resource allocation (technology, people, and funding)

PwC

21

Kristine ChungPartnerRisk AssuranceTel: +852-2289-1902Email: kristine.ky.chung@hk.pwc.com

Who to contact

pwc.com

Thank you

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