Deloitte ItalyAnalytics Experience Bootcamp

IDO 2019 Italian Survey

Results Summary

Alfredo Maria Garibaldi, 02.04.2019

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

IDO - Insight Driven Organization

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 aformal mandate

C-suite member has formal mandate to drive analytics acrosstechnology, people, process, data and strategy

Chief Data sits on the executive committee, ensures thatanalytics 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/developingspecific roles

Analytics needs are defined and influence hiring and talentdevelopment strategies for all roles

Analytics needs broken down at capability level and embeddedinto hiring and talent development strategies and processes

An analytics mindset seen as essential from the board room tothe front office and processes exist to hire or develop teams

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 analyticalendeavors

Centralised location with documentation of some previous analyticalendeavors but not fully accessible

Centralised location with documentation of some previous analyticalendeavors, accessible to all

Centralised location with standard, guidelines, documentation ofprevious analytical endeavors is made aware of and accessible to all.

Conferences and events are organised to share the best practiceswithin the business line

Centralised classified repository with standards, guidelines, goodpractice, documentation of all analytical endeavors accessible to all

within the enterprise. Frequent enterprise-wideconferences/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, governanceis currently achieved through some key individuals working

together

Rather than having a process and a formal structure, governanceis 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 allowanyone within a group or a business line to ask questions of datagovernance so that they are tracked and if necessary investigated

There is a well-defined governance structure and a process toallow 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 andassigned

Data quality technologies and processes standardisedcross-enterprise

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

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 businessunits for their process and data requirements

The reference architecture is considered by the keybusiness units for their process and data requirements

Within the enterprise, the reference architecture isrecognized as a standard for all data-related projects

To become an IDO, companies must implement a successful Analytics Strategy, by developing one coherent vision and a comprehensive tactical implementation plan.

Actionable Advice

1. Assessment

Analysis of current analytics

capabilities and identification of

a list of challenges and

opportunities to focus on moving

forwards

2. Design

Creating a long term strategy

and vision for analytics, the

services and capabilities required

and the design the structure of

the operating model

3. Roadmap

Planning the programme and

change process, including

prioritization and dependencies

between business and

technology aspects

Stakeholder engagement including education and training, workshops and IDO Labs

• Conduct an IDO Capabilities

Assessment and create a heat-

map to understand existing

capabilities and ensure a holistic

view of organizational

requirements

• Identify top analytics priorities

including improving existing

execution and brainstorming

pressing ‘Crunchy Questions’ the

business would like to explore

• Alignment against and analytics vision, formulation of an analytics strategy and education of senior stakeholders to ensure buy-in

• Development of a business case including immediate investment needs and expected quick-wins

• Definition of a target operating model provides a foundation for the journey

• IDO prioritization to reduce a long list of opportunities to a selection of prioritized and manageable strategic and tactical projects

• Transition states between current and future state are defined as programme phases

• Roadmap is based on agile methodology and adapts as the organization matures along the IDO journey

15

Thank you.

Alfredo Maria GaribaldiPartner – Analytics Country leader agaribaldi@deloitte.it

Giuseppe TarantoDirector – Lead IDO ITALYgitaranto@deloitte.it

Data Will Rock YouE x p e r i e n c e A n a l y t i c s B o o t c a m p

Stefano MagniManager -Member of IDO Leadership Teamstmagni@deloitte.it