Copyright © 2019 -Deloitte Analytics Deloitte Analytics ...€¦ · growing analytical trends...

IDO Survey - Are the italian

companies ready to cope with

future challenges?

Copyright © 2019 -Deloitte Analytics - All rights reserved.

Global presence & local excellences

WHA T WE O F F E R

We’ve landed on seven main elements to

achieve results, and filter everything we

deliver through some or all of them:

Insight

strategy

Information

delivery &

automation

Advanced

analytics

Cognitive /

AI

Data & Content

management

28DIGITAL STUDIOS

10.000+GLOBAL HEADCOUNT

28GREENHOUSES

GLOBAL

29GLOBAL DELIVERY CENTERS

Americas

$0.7bn revenue

5k+ practitioners

24 countries

Asia Pacific

$0,27 bn revenue

2K+ practitioners

15 countries

Europa, Middle East & Africa

$0,4 bn revenue

2,8K+ practitioners

61 countries

50+CLIENTS

30+BROAD TECH SKILLS

ITALY

200+RESOURCES IN ITALY

More than $1.3 billion practice with 10K+ practitioners


AUTOMATION WITH INTELLIGENCE

SCIENCE WITH ACUMEN

DATA WITH PURPOSE

SCALE WITH SPEED

DECISION WITH CONFIDENCE

HUMANS WITH MACHINE



Several Tech trends reshaped the market!

“The number of transistors on a microchip doubles every two years, though the cost of

computers is halved”G. Moore (1965)

DIGITAL TRANSFORMATION

COMPUTATIONAL POWERCLOUD CONSOLIDATION

AND ADOPTION

BUSINESS APPLICATION OF AI &

COGNITIVE

IoT

AR & VR

NEAR REAL TIME

AND STREAMING DATA


Welcome to a new era: Data Intelligence

“A computer would deserve to be

called intelligent if it could deceive

a human into believing that it was

human”Alan Turing

(Father of Computer Science and AI)

“More Data equals more

innovation“

Robert Contri(FSI Leader at Deloitte Global)


From mystery to mastery…Data impact

“A strong sense of purpose drives businesses to take the long view and invest for

growth”

Punit Renjen(CEO at Deloitte Global)

“We don’t have better algorithms, we just have more data.”

Peter Norvig(Research Director at Google)


“Big Data is like teenage sex: everyone talks about it, nobody really knows

how to do it, everyone thinks everyone else is doing it, so everyone claims they

are doing it.”

Dan Ariely(Author of Predictably Irrational)

Smarter together with Data

“Data is a precious thing and will last longer than the systems themselves.”

Tim Berners-Lee(Inventor of the World Wide Web)


”In God we trust, all others must

bring data”

W. Edwards Deming(American Statistician & Professor)

Artificial Intelligence delivers, but missteps can yield bridges to nowhere

“The most valuable commodity I

know of is information.”

Gordon Gekko(Wall Street movie quotes)

“Information is the oil of the 21st century, and analytics is the combustion engine”

Peter Sondergaard(SVP Gartner)


“War is ninety percent information.”

Napoleone Bonaparte

Insights

“In action be primitive; in foresight,

a strategist.”

Ed Koch(Former United States Representative)

Foresights

The Data-Hungry nature of AI, Insight vs Foresight The Dawn of a New Intelligence



It's time to exploit the value of your datamodernizing infrastructure and the architecture stack

With business strategies linked inseparably to technology, organizations have torethink how they envision, deliver, and evolve technology solutions

IT departments to be transformed into engines

Top down for organizingBottom up approach for modernizing

Capabilities and efficiency strategic and impactful when coupled with transformation

T O P D O W N

B O T T O M U P

MISSION

BUDGETING

OPERATING MODEL

AUTOMATION

ARCHITECTURE

INFRASTRUCTURE

Innovation department to become a must



Be an Insight Driven Organization to lead the change

An Insight Driven Organization turns data and insights into action through successful analytics

projects, building the mindset and ability to make data-supported decisions

Make innovations in products and services

Make the organization more agile in order to address rapid

technology disruption

Harness digital capabilities to bring insights to everyone

Deliver forward-looking insights rather than historical insights

Improve speed and quality of decisions, while reducing the costs of

decision making

Gain lasting competitive advantage from existing data assets

Transform growing volume of data into measurable business value

Strategic alignment, talent and leadership,

business processes featuring the

entire information lifecycle and systems

Ask the right question, view Analytics as a

core capability & a continuous improving

process featuring the identification of the

most appropriate action


IDOs see analytics as a core capability to tackle

complex business problems and to address the

growing analytical trends

ANALYTICS

They do not view Analytics as a project with a start and

end date but as a continuous improving process

ALWAYS

Insight Driven Organization (IDO)

An Insight Drive Organization is one which embeds

analysis, data and reasoning into its decision making

processes


IDO Maturity Curve

Based on Deloitte’s global IDO Model methodology, an

organization can assess itself against the Insights Maturity

Curve which has 5 key stages

Aware of analytics,

but little to non

infrastructure and

poorly defined

analytics strategy.

Adopting analytics,

building capability

and articulating an

analytics strategy in

silos.

Expanding ad-hoc

analytical capabilities

beyond silos and

into mainstream

business functions.

Industrializing

analytics to aggregate

and combine data

from broad sources

into meaningful

content.

Transforming

analytics to

streamline across all

business functions.

Stage 1

Analytically

Impaired

Stage 2

Localised

Analytics

Stage 3

Analytical

Aspirations

Stage 4

Analytical

Companies

Stage 5

Insight Driven

Organization

Data Driven

Insight Driven


For the first time in Italy

Conducted online between January and February 2019, it

was directed to C-Levels across a wide spread of industries

and organization sizes.

We proposed 45 questions, mainly grouped in 5 analysis

dimensions: Strategy, People, Process, Data and Technology.

With this survey we tried to understand how much Italian

companies are embedding analysis, data and reasoning into

their decision making processes.

#Benchmarking your analytics journey

2019 IDO Maturity Survey


34% of Italian companies consider themselves in the Stage 4 -

‘Analytical Companies’ of the IDO Maturity Curve, but no

company seems evaluating itself as an Insight Driven

Organization.

A business-driven analytics strategy to map your journey

0 5 10 15 20 25 30 35 40 45

Stage 1 - Analytically Impaired

Stage 2 - Localised Analytics

Stage 3 - Analytical Aspirations

Stage 4 - Analytical Companies

Stage 5 - Insight Driven Organisation

% ITA 2019 % UK 2018

The following chart illustrates how companies have self-assessed with regard to the IDO

Maturity Curve in 2019 in Italy and in 2018 in the UK


Only 5% of our respondents has a Chief Data Officer or a Chief Analytics Officer, but the most relevant point is that we found “Analytics are endorsed informally across the organization”.

The Ownership Debate

0 5 10 15 20 25 30 35 40 45

Analytics informally championed in organisation

Visible and active analytics leadership at functional level (business

unit/group)

C-suite or senior leadership champion analytics without a formal

mandate

C-suite member has formal mandate to drive analytics across

technology, people, process, data and strategy

Chief Data sits on the executive committee, ensures that analytics is

driven using people, processes, technology, data and strategy lens

% ITA 2019 % UK 2018

C-Suite Leadership - Which of the following statements best describes who supports Analytics at the board table and guides organizational changes?


Analytics talents have a combination of technical skills, analytical skills, storytelling and data visualization skills. These kinds of profiles are hard to find, and companies face hard challenges to cover the needs in analytics fields.

People are the main resource

Talent - All cross-industry responses to the question, “What statement best describes how analytics is embedded in the talent life cycle?”

19%

38%

28%

10%

5%

0% 5% 10% 15% 20% 25% 30% 35% 40%

Analytics needs are not taken into consideration for hiring

Analytics needs defined and considered for hiring/developing specific

roles

Analytics needs are defined and influence hiring and talent development

strategies for all roles

Analytics needs broken down at capability level and embedded into hiring

and talent development strategies and processes

An analytics mindset seen as essential from the board room to the front

office and processes exist to hire or develop teams


The new Role: Purple People

Technic

al skills

Data

analy

sis

Busin

ess a

cum

en

Sto

ryte

lling

Understanding how technology can

be leveraged to solve business

problems.

Evaluating data using analytical and

logical reasoning for the discovery of

insight, e.g. predictive modelling.

Understanding of the company’s

business strategy, current issues,

priorities and industry trends.

Articulation of insight to explain

current and forecasted trends, their

impact and opportunities.

Communication and interpersonal

skills are necessary to articulate insight

gained from analysis.

Purple People

Data Analysis

Technology Alignment

Macro-Perspective

Business knowledge

Business Commentary

Soft Skills

Defining, developing, and implementing

quality assurance practices and procedures

for technical solutions and validating

hypothesis.

Testing & Validation

SQL querying

Data Modelling

Understanding of the underlying

theory and application of key

reporting software.

Reporting Software

TECHNICAL & ANALYTICAL

BUSINESS & COMMUNICATION

Building a dedicated team with the right balance of technical skills (red

skills) and industry knowledge / business acumen (blue skills) is crucial

for the success of a specialised Insights program. We define this new

valuable blend as Purple People.

Querying and manipulating data to

facilitate the solving of more

complex problems.

Understanding of the main key

performance indicators and

business related context.

Structuring data to enable the analysis of

information both internal and external to

the business.


48% of the total respondents do not have a centralised structure to share details of the previous analytical activities across the firm.

Knowledge Management

Knowledge Management - All-cross industry responses to, “How do you leverage the knowledge and skills you develop as you become more analytical?”

48%

19%

19%

5%

9%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

No centralised location with documentation of previous analytical endeavors

Centralised location with documentation of some previous analytical endeavors

but not fully accessible

Centralised location with documentation of some previous analytical endeavors,

accessible to all

Centralised location with standard, guidelines, documentation of previous

analytical endeavors is made aware of and accessible to all.

Conferences and events are organised to share the best practices within the

business line

Centralised classified repository with standards, guidelines, good practice,

documentation of all analytical endeavors accessible to all within the enterprise.

Frequent enterprise-wide conferences/networking events organised


We tried to understand where companies find the biggest barriers in the usage of analytics. The most common answer (almost 62%) is “Embedding analytics into processes”.

Process is the main barrier

All-cross industry responses to “in which phase of the analytics process is your organization more exposed to challenges?”

50%

40%

35%

2%

30%

20%

62%

15%

20%

2%

0% 10% 20% 30% 40% 50% 60%

Understanding analytics

Managing your data

Implementing analytics

Delivering information

Using cognitive technology

Communicating value

Embedding analytics into processes

Optimising insights

Measuring the output of analytics

None of the above


Which of the following statements best describes how your analytics governance structure is defined?

Most of respondents - 62% - told us that rather than having a

process and a formal structure, governance is currently achieved

through a few individuals or teams working together.

Know your Data

29%

33%

14%

10%

14%

Rather than having a process and a formal structure, governance is currently

achieved through some key individuals working together

Rather than having a process and a formal structure, governance is currently

achieved through a few teams working together

There is a well-defined governance structure in your organization

There is a well-defined governance structure and process to allow anyone

within a group or a business line to ask questions of data governance so that

they are tracked and if necessary investigated

There is a well-defined governance structure and a process to allow anyone

within the organization to raise issues of data governance so that they are

tracked and if necessary investigated

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


Which of the following statements best describes how your organization relies on Data Quality and Data completeness and monitor them?

Improvements are necessary to define Data ownership and

stewardship roles - 24% of respondents indicated that these

roles are not yet defined – although, Data quality processes are

standardized for a good 19% of our respondents.

Data Quality as an asset

24%

33%

14%

10%

19%

Data ownership and stewardship roles not yet defined

Data quality issues addressed in a reactive mode

Data quality measured and proactively monitored

Data ownership and stewardship roles clearly defined and assigned

Data quality technologies and processes standardised cross-

enterprise

0% 5% 10% 15% 20% 25% 30% 35%Copyright © 2019 -Deloitte Analytics - All rights reserved.

What kind of vendor ecosystems supports your analytic programme?

The survey’s answers highlight companies understanding that

designing a cross-project enterprise architecture is a proactive

goal. However, only 19% recognize the reference architecture as

an enterprise standard for all data-related projects.

Towards an advanced Technological

Framework

5%

4%

34%

38%

19%

0% 5% 10% 15% 20% 25% 30% 35% 40%

There is no reference architecture

The reference architecture is under development

The reference architecture is considered by some business units for

their process and data requirements

The reference architecture is considered by the key business units for

their process and data requirements

Within the enterprise, the reference architecture is recognized as a

standard for all data-related projects


25

Thank you Daniele Pier Giorgio BobbaPartner – Digital Analytics Leader [email protected]

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Copyright © 2019 -Deloitte Analytics Deloitte Analytics ...€¦ · growing analytical trends...

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