InfoGraphic about Intelligence Hubs as accelerator of the Digital organisation. Five steps how you could think big, and act small to unlock value of Data in your organisation. Contact me for the office A0 poster.

1. Intelligence hubs as accelerator of the digital organisation
smartphone went on to
purchased on a smartphone
Forecast of European online retail sales in 2017
by country in billion Euro's
Challenges
UNLOCKING VALUE FROM DATA MEANS
THINKING BIG – WHILE ACTING SMALL.
Understanding the dynamics.
Implement through a stepwise approach:
• Acting small to make ‘fact-based decisions’ essential
• Thinking big: Intelligence hubs as accelerator the
digital organization
• Acting big to make it stick
OVER THE LAST DECADE, DATA HAS BEEN
INCREASING IN VOLUME AND DIVERSITY –
and the speed of production continues to grow.
62
Central Europe
Middle East
and Africa
MAKE DECISIONS BASED ON FACTS:
The end of assumption-based decision making.
THE DIGITISATION OF DAY-TO-DAY LIFE IS DRIVING MARKET CHANGE.
Classical business models across industries are under pressure.
BUSINESSES HAVE TO INNOVATE AND
SECURE THEIR BUSINESS MODEL TO
RELEASE THE VALUE OF DATA.
Some are more successful than others to
overcome the key challenges.
Non-mobile
devices
Mobile devices
Interactions through mobile devices will grow.
Today there are 7 billion connected devices in the
world, in 2020 there will be 32 billion devices.
Data transactions through these devices will grow
at 78% a year. In 2016, there will be 12 exabyte
data transactions a day - in other words 66 billion
pictures sent a day!
The digital customer is on the rise.
The internet has redefined the consumer decision-making process. The average number of information sources used by shoppers has
doubled in the last years. Customer loyalty is reducing as switching becomes easier (80% of customers will abandon a mobile site
following a bad user experience). Online sales are growing at 11% per year in Europe. The European internet economy is already 3.5% of total
GDP - this will double by 2016, and triple by 2020.
There has been an increase of 18% in the number of retailers going
bankrupt over the last three years as they have not adapted their
business model in time. In businesses, only 5% of data is used, while
There are several challenges that businesses face in this process
of change:
Global challenges.
Security:
of people have no idea
who holds their personal
data. The number of
privacy injunction applications more than
doubled in the last year.
People have little trust in the way
companies handle their personal data and
what they use it for.
Online crime:
2.7 billion people use the
internet today and this is
expected to grow to
4 billion by 2017.
As a result, online crime
is growing.
Organisational challenges.
Value: “I want to do big data, but don’t
know how to apply any of the outcomes”
Capability:
professional level”
Governance: “Data management and
governance processes aren’t defined and
are fragmented throughout the
organisation”
Technology: “We have a fragmented
landscape of applications and warehouses”
Sweden
$ 6.5
Germany
The digital organisation is on the rise.
Mass customisation has become the norm, boosting flexible production
and changing the traditional organisation. Next to that, the value chain is
increasingly digitised to support flexible production, personalised service
and products.
The big data market has exploded.
A consequence of the digitisation and the growing amount of data, is the
growth of the big data industry. The big data technology and services
market will grow 27% per year to $32 billion, and that the internet of things
will generate 30 billion autonomously-connected endpoints in 2017.
In a connected world, emerging economies
will drive further data creation.
of data today is generated by the West, other parts of the
world are emerging. So is Asia expected to be the biggest
data producer by 2019.
The internet of things will
generate 30 billion
autonomously-connected
endpoints in 2017.
Total data volume will
increase exponentially.
The total amount of data
will grow from 5 zettabytes -
to nearly 45 zettabytes,
that is the equivalent of 62
billion iPhones.
Talent matters
as much as
technology
routinely run experiments to test the impact of
changes to things like marketing strategies and
recommendation systems. Amazon is able to
monitor the impact of tiny changes, such as a
of making the change,” says Mr Wiengend of the
Social Data Lab.
MANAGE THE PENDULUM EFFECT BETWEEN
STRICT SECURITY AND MASSIVE OPPORTUNITIES
Accompanying the digital revolution are multiple
security and privacy concerns.
Cyber criminals in search of financial gain (representing 60% of
cyber crime) and intellectual property spies (about 25%) give
cause for concern. Companies have to be aware of the security,
moral and legal choices they make regarding data protection.

Digital shoppers are on the rise and represent a
massive opportunity
Despite the risks accompanying the increase in online data, there are
also opportunities:
• Reputation can be positively influenced by a security strategy –
when a large bank openly informed their customers about
phishing emails, positive sentiment increased.
• Customer satisfaction increases when companies react quickly to
public opinion. 95% of popular brands have a webcare strategy,
including service delivery. Successful companies have response
times varying between zero and two hours, while the average
response time in the Netherlands is 15 hours.
Mobile phones
Profit increase 27.5% Fraud detection 80%
Security Marketing
VS.
Fraud reduction 30%
Business unit
operations
Identify a visionary to sponsor your first data insights project that can
be executed in eight weeks (small) and will delver insights that
exceed the investment at least five times (essential). Data insight
projects follow a five step proven approach:
A. Formulate hypotheses as your point of departure:
• Engage resources from business, data science and consultancy,
and bring them together into a team to brainstorm on hypotheses
• Ensure the insights are tangible input for a realistic business case
B-D. Prepare, analyse, validate your data:
• Use your existing infrastructure and analytics tooling, where
necessary, complemented by low-investment, open-source
software, for the first data insight projects
• Continuously manage and iterate scope and outcomes with
business and project teams
E. Manage benefits realisation with a disciplined approach:
• Formulate clear next steps to capitalise your insights
• Monitor the insights regularly to justify the realised benefits
• Share success throughout the organisation to trigger demand
We believe that a digital organisation requires an enterprise-functional
approach to maximise the potential of available data.
By thinking big and initiating an enterprise-wide intelligence hub, companies
can speed up their journey to becoming digital organisations.
Stakeholders – Who should we take into account?
• Your data playing field should give you insights into who your stakeholders
are (customers, regulators, shareholders or employees)
• This rich set of stakeholders, and limited capacity of an intelligence
hub, requires a careful prioritisation to deliver high value to all
stakeholders
Customer and Value - Why do you need an intelligence hub?
What is the customer value the intelligence hub creates? What services do
the intelligence hub oer to stakeholders? This goes beyond the value of
the analysis itself . The type of service dimensions are: the speed/flexibility
of the analytics and type of data that is requested by the business.
Capability – What are the resources and how do we organise them?
• Building the capability for intelligence hubs goes beyond recruiting data
scientists and procuring software tools.
• A digitally capable organisation has the following ingredients in place: data
driven marketeers and managers, processes to deliver insight projects,
benefit reporting platforms, data governance, agile infrastructure and a
view on roles and responsibilities
Financial - How do we finance and keep costs in control?
Most organisations use financial triggers as a driver for change. Initiating
data insight projects requires a short-term return on investment (ROI) and
shoud be used to drive such experiments. But, a long-term investment is
required in the near future to accelerate the transformation into a data-driven
organisation. Hence, one should define a budget and cost and
performance mechanism that fits the requirements to manage the pendulum
between short and long-term investment requirements.
Hacking
Malware
Social
Physical
Misuse
Negative
Neutral
Positive
BUILD A DATA CAPABILITY BEYOND RECRUITING
DATA SCIENTISTS AND BUYING BIG DATA PLATFORMS.
Building a data capability requires an integrated approach over
The skills and knowledge of data scientists are precious.
The number of job opportunities for data scientists are increasing: the US is expected
to create around 400,000 new data science jobs, but is likely to produce only about
140,000 qualified graduates to fill them in 2015.
Analytical methods have not evolved as quickly as technology.
Most methods used today stem from the 1950s to 1980s. Due to innovation in
technology, application of several analytical methods is now possible compared
with a couple of years ago.
0,02
0,01
Risk of mis-using oversimplified analytical software.
Analytical software tools make data analytics mainstream, but there
is a risk of over-simplification. Programming skills are less important
than before. This increases the risk of miss-use of the applications
New platforms challenge the way
corporations look at IT.
There are an increasing number of open-source,
service subscription and cloud
solutions that companies can benefit from.
For example: Hadoop, an open-source
technology, has become mainstream.
Stage gate | Deliverable Stage gate | Deliverable Stage gate | Deliverable
It is our experience that digital organisations develop incrementally
instead of a traditional belief of top-down design. Acting big requires
finding sponsorship to initiate an ocial intelligence hub.
We use four incremental steps to make intelligence hubs true
accelerators of a digital organisation:
Make it essential - acting small to create urgency
and commitment:
• Build success by promoting the success of your first
client-insight projects
• Identify stakeholders and support throughout the organisation
for new insight projects
• Build a community around the successful projects and show
benefits delivered
Make it ready - design the business model of the
intelligence hub:
• Create leadership and mobilise first movers into a virtual team
• Start defining the business model of an intelligence hub
• Agree on a corporate-wide data governance and strategy,
balancing speed and flexibility of analysis with data quality
management
Make it happen - secure advanced data analytics in your
organisation through the intelligence hub:
• Create a roadmap that allows short-term
experimenting and long-term capability
• Incorporate data activities in existing processes and adopt
new ways of working
• Make the intelligence hub an ocial team
Netezza Terradata
Google Hadoop
BQ
Storm Spark
Exploration | A successful implementation of information
services will be built over time through stage gates. In every
stage gate high impact business requirement will be fullfilled.
Operation | Through the delivery of the stage gate
deliverables, an accumulated experience is built that
will be embedded in the organisation (playing field
customer, capabilities and finance).
Make it stick - continuously innovate while producing
regular insights:
• Incorporate new ways of working in your organisation’s
performance management system
• Continuously allow and invest in innovation and discover the
unknown (allow for a lab mentality)
• Create a ecosystem of clients, competitors and suppliers -
understanding that ideas and innovation are born in partnership
and new coalitions
1. Make decisions
based on facts:
the end of
assumption-based
decision making
2. Break through
organisational
silos by focusing
on the client
5. Manage the
between strict
security and
massive
opportunities
3. Establish a
leadership that
facilitates a
data-driven way
of working
4. Buil a data
capability beyond
recruiting data
scientists and
buying big data
platforms
BREAK THROUGH ORGANISATIONAL SILOS BY
FOCUSING ON THE CLIENT.
ESTABLISH LEADERSHIP THAT FACILITATES
A DATA-DRIVEN WAY OF WORKING.
ACTING SMALL TO MAKE
‘FACT-BASED DECISIONS’
ESSENTIAL.
THINKING BIG:
INTELLIGENCE HUBS AS
ACCELETATOR OF THE
DIGITAL ORGANIZATION
ACTING BIG TO MAKE
THE ‘DATA CHANGE’ HAPPEN.
Marketing is building brands through
personalised campaigns. They are using
customer data to understand their
customer better and to make personal
on your location or previous buying
behaviour.
Security is worried about the impact of
personalised campaigns that may breach
privacy and regulations.
VS.
IT
Business unit operations develop their own
data-related projects based on their
are often not reused and can’t be used
by other units.
IT tends to introduce large-scale data
projects that try to provide a one-size
fits-all solution. They are looking for
eciency gains through standardisation
and predictable quality.
Data provider Data user
VS.
The data provider is focused on local
benefits of data and compliant to
regulations. He experiences no incentive
to realise corporate overall benefits.
The data user is focused on the upside
potential of data and reuses data of
Integrate data
governance
responsibility
Fewer than one in five companies have
“a well-defined data management strategy that
focuses resources on collecting and analysing the
most valuable data. We need to balance our
initiatives across our silos in the bank and
meanwhile maintain the drive to use data in an
agile way.” CIO of a Dutch retail bank.
Balance big
data investments
with real business
need
“Many executives believe that the right
technology can produce ‘magical results’.
But companies should start by prioritising the
challenges they want to tackle, and then build
an appropriate data strategy around those
objectives. You need to know what problem
you want to solve.” Mr Dumbill, chair of
the O’Reilly Strata Conference, a leading big
data event.
Job trends from Indeed.com
% of data science job postings
Jan ’06
0
Jan ’07 Jan ’08 Jan ’09 Jan ’10 Jan ’11
Linear
regression
1950s
Neural
networks
1960s
Decision treed
1970-1980s
Temporal dierence
learning
1990s
Deep learning
2000s
Generic use case
Proven
Leading edge
Specific use case
Mongo
DB
Neo4J
Commercial
open source
Number of breaches per threat category over time
600
400
200
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Error
70% of organisations
indicate that
information security
policies are owned
at the highest
organisation level
3500
3000
2500
2000
1500
1000
500
0
1
apr
10
apr
20
apr
30
apr
Strongly negative
Strongly positive
Managing security breaches leads to positvie sentiment
Security breach
Proven approach to drive insights and benefits
Benefits
realisation
Data
preparation
B
Validate Analyse
Hypotheses
business case
A
D C
E
Value for the business
Continuously improving information factory
Playing field
Customer
Capabilities
Finance
Make it essential Make it ready Make it happen Make it stick
Exabyte per Month
2011 2012 2013 2014 2015 2016
North America
Latin America
Asia Pacific
2019 2019
2019
2013 2013
2019 2013
2019
2013
2013
Western Europe
Mobile PCs,
tablets and
mobile routers
Billion
iPhones
46%researched on a
smartphone went on
to purchase in store
41% researched on a
19% researched on a smartphone,
visited the store and then
bought by computer
UK
$ 170
France
$ 60
Spain
$ 30
Netherlands
$ 12.5
Italy
$ 20
$
110
Global internet device
installed base forecast
0
2004 2005
Devices
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
20,000,000
15,000,000
10,000,000
5,000,000
Wearables
Smart TVs
Internet Of things
Tablets
Smartphones
Personal Computers
(Desktop and Notebooks)
Play
78%
increase in mobile
data transactions
66bn
pictures sent
per day
Real-life example
Improving conversion ratio through improved
understanding of the omni-channel client journey
Due to changing regulations, the mortgage services of a
retail bank needed to be digitised to become more ecient.
However, it was unclear whether customers could find their
PA analysed the online click and transaction data in a eight
week’s project, to understand the bottlenecks especially where
online visitors did not convert into potential customers.
With the results, the client improved the conversion ratio
and decreased throughput time in mortgage process
dramatically.
Real-life example
Securing governance and structural
data insights at a large Dutch insurer
We were asked to help this insurer accelerate the
creation of business value f internal and external
data. Through iterative projects, and within a short
timeframe, the business value from available data
was demonstrated through an ‘intelligence’ cockpit.
Existing data sources revealed business value through
clever permutations and combinations which were
previously unthinkable. This is the foundation of
a true digital journey for this client.
Business model
Playing field
Stakeholders Value
Who should we take
Why do you need an
intelligenge hub?
Operating model
How do we finance and
keep costs in control?
into account
How do we create value
for our customers?
What are the resources and
how do we organise them?
Real-life example
Customer
Capability
Financial
Accelerating omni-channel
A financial company in the process of transforming into
a omni-channel service provider closed local branches
and ramped up new online services. To accelerate this
change process, additional customer insights from digital
channels were essential. Hence the CCO and CIO agreed to
imple ment a joint customer insights competence centre
to build up the required capabilities that the existing
organisation either lacks or has scattered across divisions.
PA supported the creation of this intellgence hub which
now successfully completes client insights projects.
1. Business creation
and innovation
Alternate and
realtime pricing
scenarios
Product and
service reinvention
based on data and
sensoring
Customer
relationship dynamic
pricing
Churn prevention Breakthrough
product innovation
in healthcare
Store location,
supply chain
optimisation
and operations
management
optimisation
Stock
replenishment,
asset management
and forecast of
performance and
equipment failures
End-to-end
omni-channel
customer journey
optimisation
Forecasting
bandwidth in
response to
customer
behaviour
Scenario analysis
of the impact of
tax policy and
budget decisions
3. Risk control and
fraud detection
Fraud detection
prevention
Individual risk
profiling
Determination of
level of credit
exposure to
particular
customers
Fraud prevention Identification of tax
and benefit fraud
Retail Manufacturing Banking Telecoms
utility
Government
Internet users in bn
2014
4
2
0
2017
5% used data
Customer intelligence:
There is an average profit
increase of 27.5% at selected US
banks through analysing client
motivation and behaviour
patterns, or predicting customer
churn.
Data analytics:
Analytical platforms for fraud
detection highlight suspicious
behaviour with 80% accuracy -
leading to 30% reduction in
fraud cost.
‘Watch our ’Digital
Democracy’ video’
www.paconsulting.com/digital-insights/
‘Watch our ’Future Role
of Insurance’ video’
www.paconsulting.com/digital-insights/
European internet economy
% of total GDP
2014
10.5
7
3.5
0
2016 2020
“Machine learning finally matured
and now outperforms humans in most
data analysis problems. I have seen
the number of machine-learning
students more than quadruple in
the last three years because of this
renewed interest in the field.”
Prof. Dr. Patrick van der Smagt,
Associate Professor of Biomimetic
robotics and machine learning.
DATA IN THE DIGITAL AGE
Setting the scene
Suggestions
“It’s dicult to find and