Data and analytics

Riding the digitalisation wave

July 2021
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 Data and analytics: Riding the digitalisation wave

The role of analytics in

the digital world
Data analytics matter
Data is the new capital of the global economy. As organisations seek renewed growth, stronger performance and
more meaningful customer engagement, the pressure to exploit data is immense.

In 2018, the global big data and business analytics market was valued at USD168.8 billion. It is forecasted to grow
to USD274.3 billion by 2022.1

The current COVID-19 pandemic has showed that digitally native organisations that are “insight-driven by default”
show much higher resilience and are able to tighten their dominant market positions, even growing share value
while stock markets tumble. These organisations are equipped to manage the crisis better, and are expected to
recover and excel faster once markets and regulatory efforts return to normal.

Having data and analytics at their core, insight-driven organisations are prepared to make the best decisions in an
efficient manner. It enables them to manage core business operations in the most cost-effective way and react on
a day-to-day basis.

1. Revenue from big data and business analytics worldwide from 2015 to 2022(in billion U.S. dollars), 2020, https://www.statista.com/
statistics/551501/worldwide-big-data-business-analytics-revenue/

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Data and analytics: Riding the digitalisation wave

Are you data rich

but insight poor?
Using analytics to convert data into insights and make smarter decisions
Analytics is the practice of capturing, managing, and analysing data to drive
business strategy and performance. It includes a range of approaches and
solutions, from looking backward to evaluating what happened in the past,
to forward-looking scenario planning and predictive modelling.

Hindsight
Analytics Applied In
De for
ict sig m The Strategy
ed n
Pr | What data we need
aly ts ®

at ple

to work out what

|

io
Optomise | An igh

Im
se

n M ent | Manager

happens in our
s
c In

business?
Foresight
ana
m

• Systems
Analyti

gement

• Security
The Future • Governance
Why is this happening? • Software
What will happen in the • Strategy
future? > Facts/Data
How do we take
advantage? rf
Pe R

> Knowledge o
ev r m a
ie n
w n c e O p ti m is atio
|A
n aly s
e | R e p o rt

Insight
Past and Present
What is happening in our business?
How many, how much, how often?
How are we performing?
>Information

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 Data and analytics: Riding the digitalisation wave

How can analytics generate

actionable insights?
Business functions Applying analytics… Use case example

Customer & growth …to enhance customer lifecycle, • Detailed segmentation to better target cross-
sales and pricing processes, and sell and up-sell activity
overall customer experience
• Understanding customer profile to improve
pricing & risk calculations Predicting the
impact of different compliance actions

• Identifying and managing the most profitable

customers (customer lifetime value) across a
portfolio of products and services

Operations …to provide insights across the • Analysing spend to identify efficiencies across
organisation’s value chain the value chain

• Monitoring process to identify potential

synergy opportunities to reduce cost and
effort

• Identifying candidate locations for new branch

based on a range of geospatial factors

Finance …to measure, control, and • Consolidating financial reporting with other
optimise financial management data to provide multi-dimensional views and
processes more accurate financial forecasts

• Simulating the impact of changes in the

financial markets (e.g. stress testing of the
banking system)

• Analysing funding and capital requirement to

ensure sufficient liquidity

Risk & regulatory …to measure, monitor, and • Identifying and investigating instances of fraud
mitigate enterprise risk and error in payment systems

• Monitoring compliance with financial

regulations (e.g. sanctions, anti-bribery &
corruption laws, etc)

• Identifying cyber-security breaches from

patterns of user behaviour

Talent management …to enhance and optimise • Reducing overtime by optimising staff
workforce processes and scheduling
intelligence
• Forecasting demand to improve workforce
planning

• Identifying early indicators of attrition to

improve retention

• Analysing employee data to identify those

most at risk of workplace hazard

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Data and analytics: Riding the digitalisation wave

What analytics maturity stage

is your organisation at?

Organisation
Insight Driven

Analytical
Companies
Transforming
analytics to
Analytical
Aspirations streamline
Industrialising decision making
analytics to across all
Localised aggregate & business
Analytics combine data from
Expanding functions.
broad sources into
ad-hoc analytical
Analytically meaningful content
capabilities beyond
Impaired and new ideas.
silos and into
Adopting
mainstream
analytics, building
business functions.
capability and
Aware of analytics, articulating an
but little to no analytics strategy
infrastructure and in silos.
poorly defined
analytics strategy.

BI/ Visualisation Managed Crowd Machine

Reporting Analytics Sourcing Learning

Analytics Rise of the Data Big Data Internet Digital

Scientist of Things Enterprise

There is no one-size-fits-all approach in adopting analytics at work. Organisations at

different maturity stages need to look into their current capabilities and strategise
according to business requirements. This will allow organisations to achieve effective
resource allocation and maximum return on the investment in the given period of
time.

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 Data and analytics: Riding the digitalisation wave

What makes analytics difficult

to be adopted?

Data Technical perception

Confidence in data is low due to Image is ‘techy’, complex, and
inconsistent definitions and differing related to math and statistics, and
answers to the same question. The hence difficult to comprehend or
reluctance to share data also hinders thought to be IT-only.
access to timely data.

Cultural change Analytics skill shortages

The bigger and older the organisation, Talent is a critical hurdle in analytics
the more difficult it is to drive a cultural adoption. The skill gap might delay some
change or analytics transformation. of the analytics implementation and
integration.

Insight communication Poor implementation

Marred by jargons, and complicated Analytics is developed in silos and data is
tables. The results and insights are often duplicated across the organisation. It lacks
‘lost in translation. implementation vision and or strategy for
enterprise-wide integration.

Inaccurate metrics,
expectations, models
Over simplistic models, overconfident
analysts, lack of clarity on outcomes with
inaccurate assumptions have led to
incorrect results.

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Data and analytics: Riding the digitalisation wave

How do we kickstart the

analytics adoption journey?
In the analytics adoption journey, the five building blocks for transformation are:
Strategy, People, Process, Technology, and Data. Each building block plays different
roles and importance depending on the analytics maturity level of the organisation.

ess needs
Busin

People

Familiarising yourself with the

five building blocks in the
Technology Process transformation journey enables
Strategy you to anticipate and manage
risks towards a successful
journey.

Data

Pe
rfo nt
rm a me
nce m eas ure

Illustration of an analytics adoption journey

Identify business Acquire senior Clarify on the Decide on Devise and implement
needs and management support for target operating development Analytics Strategic Plan
painpoints a successful roll-out model path and system and perform periodical
needs review and update

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 Data and analytics: Riding the digitalisation wave

Building blocks
for analytics
adoption

9
Data and analytics: Riding the digitalisation wave

Building blocks for analytics adoption

Strategy
nge management
Cha

Leadership

To truly embed analytics, focus is

required in 5 key areas:
Capability Embedding Organisation • Leadership
development analytics design • Capability development
• Analytics culture
• Talent management
• Change management

Talent management

Com
m u n i c a ti o n s

Setting strategy requires executive

sponsors and champions to carefully
define their analytics objectives, identify
desired outputs, and align the analytics
journey with the organisation’s broader
goals, business plans, as well as win
strategy.

The strategy components should include

describing the organisation’s vision,
building a business case, committing to
continuous improvement, gaining and
maintaining key stakeholder support.

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 Data and analytics: Riding the digitalisation wave

Building blocks for analytics adoption

People
Success also hinges on organisational ability to create purple teams — those that combine analytics-savvy people
(red skills) with seasoned business communicators (blue skills) to deliver actionable business insights.

Simply stated, organisations that try to win at analytics by hiring predominantly red talent - data scientists and data
engineer may have difficulty translating skills into results. The red people cannot deliver value without a business
use case.

It has now become eminently clear that blue people - change managers, business owners and subject matter
experts are required to promote a culture that embraces analytics insight to actively drive decision-making.
Organisations have to assess their current capabilities and strike a balance between the red and blue talents such
that business needs are supported by the analytics team. In the process of doing so, organisations may also have
to consider an organisational structure that facilitates smooth collaboration between the two groups of talents.

Technical & Business &

Analytical Communication

Testing & validation Technology alignment

Defininh, developing, and Understanding how
implementing quality assurance technology can be leveraged
pracdures for technical solutions to solve business problems.
Business acument

and validating hypotheses.

Tehnical skills

Micro-perspective
SQL querying Understanding of the company’s
Querying and manipulating business strategy, current
data to facilitate the solving of business issues and priorities
more complex problems and current industry trends.

Data modelling Business knowledge

Structure data to enable the Understanding of business
analysis of information, both measurement of key
internal and extenal to the perfomance indicators and
business business frameworks.
Data analysis

Data analysis Business commentary

Storytelling

Valuating data using analytical Articulation of insight to explain

and logical reasoning for the current and forecasted trends,
dicovery of insight, e.g. their impact and oppprtunities
predictive modelling for the business.

Reporting software Soft skills

Understanding of the underlying Communication and interpersonal
theory and application of key skills are necessary to articulate
reporting software. insight gained from analysis.

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Data and analytics: Riding the digitalisation wave

Building blocks for analytics adoption

Process
Beyond capturing, certifying the accuracy of, and distributing the right data, organisations need processes to turn
data into insights, and to act upon that insight.

This involves more than generating retrospective insights limited to silo-ed teams or functions. Instead, it
enables prescriptive insights capable of guiding an organisation’s decision-making. In developing this capacity,
organisations need a solid governance framework and operating model, embedded measurement frameworks,
and a feedback mechanism.

To encourage analytics adoption, business processes need to be flexible for users to make changes and cater for
data collection, technology implementation and insight-driven action. Key Performance Indicators (KPIs) generated
using analytics insight can be introduced as part of the adoption process and foster an analytics culture.

Analytics Process

1 2 3 4 5 6
Business Data Data Analysis &
modeling Evaluation Deployment
understanding understanding preparation

What is the When is data What is the What analytic Which analytic How can analytic
operating captured in the operating techniques can be techniques techniques be
culture? Where system? culture? Where used to identify implemented leveraged to
have controls have controls known high risk are most identify potential
failed in the failed in the scenarios in the reliable in risks on an
past? past? data? Are there identifying ongoing basis?
other scenarios potential risks What does the
that look similar? in the data? end-state
solution loo like?
What are the Where is data What analytic
key business stored and in techniques can be
processes in what format? used to identify
the operation? potential unknown
What are the scenarios of
performance control failures in
measurement the data?
metrics?

Familiarise Analyse Operationalise

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 Data and analytics: Riding the digitalisation wave

Building blocks for analytics adoption

Technology
With digitisation happening at full speed in the current business environment, collecting data has never been easier.
Ranging from customer phone calls, sensor monitoring, to process tracking, data can be generated at any time with
the right tools for analytics work.

Technology in analytics refer not only to the physical hardware, but the software which speeds up digitalisation and
enable analytics work to be performed. More importantly, setting up a well-structured technology infrastructure and
digitalised process allows quality data of high volume to be readily available for analytics.

Furthermore, identifying the right Business Intelligence tools for specific business function is essential as the
requirements for business use case may vary significantly. Currently there are many Business Intelligence tools on
the market catering to different user needs. For example, tools to create business dashboards, to build advanced
models, as well as to manage data.

Visualisation &
Sources Integration Data warehouse Analysis layer
consumption

Billing
Advanced
Supply chain
Enterprise services backbone

report
CRM analytics statistics

CRM
analytics OLAP
E-Business Enterprise
data
ETL

Legacy Margin
warehouse Dashboard
operational analytics
data

External
data
Human
ERP specific capital Reports
ERP
store analytics

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Data and analytics: Riding the digitalisation wave

Building blocks for analytics adoption

Data
Analytics rely heavily on the quality of data available to produce meaningful insights, and people within the
organisation should work to ensure that data is secured and well managed. This ensures investment in analytics
brings sustained returns.

Data management and internal controls are especially critical to ensure that the data is accurate and complete at
the input stage, and any anomaly can be quickly detected before data reach the final users.

In addressing their data requirements, organisations should also put well-designed information models in place,
adopt a realistic approach to data quality, ensure regulatory compliance, and carefully consider the ethical
implications of how they use data.

Data
governance

Data
Data privacy strategy &
& security architecture Enterprise data management
covers the entire data lifecycle,
ensuring the correct data is input
Enterprise
Data at the entry stage, inaccurate data
Management is cleansed at the maintenance
stage, and the right data is
acquired by its intended users at
the analytics stage.
Master data Data quality
management management

Metadata
management

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 Data and analytics: Riding the digitalisation wave

Case study

15
Data and analytics: Riding the digitalisation wave

Becoming an insight-driven
organisation, one step at a time

As the age of disruption continues to vastly alter business realities, The analytics adoption journey can be a complex undertaking,
non-digital natives are scrambling to keep up. The knee-jerk starting with a complex process and attempting too many
reaction to invest in technology solutions and a horde of red talent initiatives at once may be overwhelming and confusing.
is understandable.
The best approach is to focus on the basic, build a solid foundation
However, it’s simply not enough. It has become increasingly clear around business use case, and from there, more advanced
that organisations can only succeed in their quest to become an capabilities can be developed.
Insight-Driven Organisation if they successfully engage the power
of their people, both red and blue. In this aspect, knowing an organisation’s analytics maturity level
and current business need is essential. By strategising with the five
There is no one-size-fits-all approach in adopting analytics at work. building blocks - Strategy, People, Process, Technology and Data,
More often than not, orgnisations with an overly ambitious plan organisations gain the ability to roll out analytics projects and build
may suffer from trying to do too much too soon. out their analytics capabilities efficiently and in a more
cost-effective manner.

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 Data and analytics: Riding the digitalisation wave

Selected case studies from

clients in the SEA region

Country Project background Value to the client

Malaysia The banking client requires assistance from Deloitte to Deloitte’s Forensic Analytics team developed a dashboard
Malaysia
conduct an AML/CFT Institutional Risk Assessment and to provide visualisation of the AML/CFT activities such as
Enterprise-Wide Risk Assessment. Transaction Monitoring, Institutional Risk Assessment,
Customer On-boarding, and Cash Threshold Report.

Data analytics was brought in to develop a dashboard for

ongoing monitoring and reporting.

Malaysia An industrial research institituon has engaged Deloitte Deloitte was engaged to analyse the existing process and
Malaysia
to rollout a cost management initiative and Enterprise setup the configuration for the new Enterprise Resource
Resource Planning implementation with the aim to utilise Planning platform.
data for better resource allocation.
The team designed a reporting dashboard to improve
visibility on costs incurred and business profitability. With
the data collected, a detailed analysis on cost drivers was
performed to redesign cost allocation basis and assess
business profitability at group level

Singapore The superannuation fund has embarked on a digital Deloitte partnered with the superannuation fund to
SG
transformation journey encompassing Operating Model design the required changes to its operating models
for Digital, Data & Analytics and Technology for an Asia across: Customer experience, Customer servicing, Digital
Pacific Superannuation Fund. marketing, Data & analytics, and Technology.

The future operating model design will ensure that

the client has the right processes and skills in place to
maximise the investments made in new technology and
deliver the desired benefits.

Indonesia A multinational plantation company has engaged Deloitte Deloitte developed a data governance target operating
Indonesia
to develop and implement a Master Data Governance model for the client and laid out the framework with
framework to facilitate resolution of data issues across a data governance organisation structure, roles and
multiple business functions. responsibilities matrix, policies and procedures, and data
standards for customer, vendor, and material master data.

The data governance team also assisted the client

in enhancing data quality as well as setting up a new
platform to govern the client’s master data creation and
changes.

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Data and analytics: Riding the digitalisation wave

Data and analytics

one-stop solution centre

ta lifecycle
The da

Enterprise data Analytics services

management services

Data governance Finance & reporting

nt
me

• Data ownership • Financial reporting analytics

• Data stewardship • Business performance management
tire

• Data policies • Price optimisation

& re

• Data standards • Cost management

storage, movement

Master data Data strategy Marketing Financial crime &

management • Vision and • Social media & transactions
• Reference data planning marketing analytics • Fraud

Data usage
management • Data architecture • Campaign analytics • Transaction monitoring
• Metadata • Sentiment analysis & benchmarking
management • Third party risk rating
Human Resource • Financial crime-related
• Payroll analytics
Data quality Data management
Customer / vendor
• Data profiling • Data migration
data

Tax management
• Data cleansing • Data storage
• Tax analytics • Customer and vendor
• Data monitoring • Data access
profiling, segment
,

• Data compliance • Data archiving

n

analysis
tio

• Data traceability • Data retirement Operation

a

• Performance monitoring
re

• Performance improvement
a c

Data security
t
Da

• Data privacy
Risk Management
• Data classification
Risk analytics
• Data leakage prevention
• Regulatory compliance
monitoring
• Control assessment
• Internal audit

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 Data and analytics: Riding the digitalisation wave

Innovation Council

Justin Ong Su Je Hui

Malaysia Innovation Leader Malaysia Innovation Team
keaong@deloitte.com jehusu@deloitte.com

Mak Wai Kit Senthuran Elalingam Oo Yang Ping

Audit & Assurance Tax Innovation Leader Financial Advisory
Innovation Leader Innovation Leader Innovation Leader
mwaikit@deloitte.com selalingam@deloitte.com yoo@deloitte.com

Chee Wei Han Shahariz Abdul Aziz

Consulting Risk Advisory
Innovation Leader Innovation Leader
wchee@deloitte.com shaharizaziz@deloitte.com
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