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Excellent Business Decisions – Powered by Big Data Whitepaper | June 2012
Excellent Business Decisions Powered by Big Data
Content
2
Introduction 3
The opportunity 5
Health Care 6
Public Sector 6
Retail 7
Manufacturing 7
Transportation 8
Oil and Gas 8
Utilities 9
Finance Sector 9
The challenge 11
Embracing Innovative Technologies 12
Data Governance 12
Development Paradigm 13
Organisational Characteristics 13
Leveraging our capabilities 14
Big Data Technology Understanding 14
Big Data Governance by Value 14
Big Data Life Cycle Support 15
Big Data Organisational Support 16
Machine to Machine 17
Collaborative Data 3.0 18
Applied Customer Insight 19
Contact Information 20
This document contains information which is confidential and of value to Logica. It may be used
only for the agreed purpose for which it has been provided. Logica’s prior written consent is
required before any part is reproduced. Except where indicated otherwise, all names,
trademarks, and service marks referred to in this document are the property of a company in the
Logica group or its licensors.
Introduction
3
With this whitepaper Logica introduces the business
opportunities for you to leverage the power of “Big Data”.
Across sectors and regions, several cross-cutting trends
have fuelled growth in data generation and will continue to
propel the rapidly expanding pools of data. These trends
include growth in traditional transactional databases,
continued expansion of multimedia content, increasing
popularity of social media, and proliferation of applications
of sensors in the Internet of Things.
The number of devices capable of automatically gathering
and storing digital data is increasing fast: our mobile
phones, home appliances, digital televisions, cars,
industrial process monitoring systems, email clients, web
browsers, social media applications, traffic and security
cameras, and numerous other sources of digital
information produce vast masses of data all the time.
Global trend setters like Google, Yahoo, Netflix, Amazon
and Autonomy have already shown that it is possible to
transform data to economic value by producing novel,
immensely popular and profitable services based on
intelligent analysis of massive data sets.
As big data and its levers become an increasingly valuable
asset, their intelligent exploitation will be critical for
enterprises to compete effectively. We already see
organizations that understand and embrace the use of big
data pulling ahead of their peers in tangible corporate
performance measures. The use of big data will become a
key basis of competition across sectors, so it is imperative
that organizational leaders begin to incorporate big data
into their business plans.
Digital data is now everywhere—in every sector, in every
economy, in every organization and user of digital
technology. While this topic might once have concerned
only a few data geeks, big data is now relevant for leaders
across every sector, and consumers of products and
services stand to benefit from its application.
Nevertheless, new user-centric role based approaches and
cooperative organization networks require ever more
intelligent ways to utilize the available data. The content
should be available automatically and be based, on user
role, context requirements and process perspectives. This
The Large Hadron
Collider at CERN
generates 40 terabytes of data
every second
Wal-Mart is registering
more than 1M customer
transactions every hour which is feeding a
database of 2.5 petabytes
Google handles around half of the world’s internet searches, answering around
35000 queries every second.
The world is creating
2.700 Exabyte in 2012, growing with
60% each consecutive
year
Twitter processes over
500M tweets a day
30 billion pieces of content shared on
Facebook every month
Introduction
4
means that data sources often cross traditional
organization borders and may also be utilizing open data
reserves.
An additional important issue is that the data is not only
big, but it is also often unstructured. 95% of the emerging
data is unstructured, consisting of not clean numerical
data, but text, images, videos, audio and other forms of
data that humans can process effortlessly, but that are
most difficult to process automatically by computers. The
magnitude of this data easily prevents straightforward
human-assisted manual solutions where the data is
enriched with “computer-friendly” tags or other forms of
supplementary information.
Enterprises are collecting data with greater granularity
and frequency, capturing every customer transaction,
attaching more personal information, and also collecting
more information about consumer behaviour in many
different environments. Big data are datasets that grow so
large that they become awkward to work with using on-
hand database management tools. Difficulties include
capture, storage, search, sharing, analytics, and
visualizing. Today challenges are:
• Data is BIG - You need automated predictive big data
analytics.
• Data is HETEROGENEOUS - You need methods for
integrating heterogeneous, and parcelled data sources.
• Data is HARD TO UNDERSTAND – You need
visualization/summarization to support informed
decision-making
• Data needs to be ACCESSED – You need context-sensitive,
on-line, open and role based personalized information
• Data is NOISY – You need anomaly detection, filtering,
pruning, cleansing and enriched data
How do you handle these challenges?
With this paper we introduce the business opportunities
leveraging today’s Big Data, how to benefit from
technology innovations in this area, how to cope with the
challenges and how to effectively engage in Big Data
initiatives.
40% projected growth
in global data generated
per year vs. 5% growth
in global IT spending
7 billion mobile phones in use in 2011
$600 to buy a disk drive that can store all of
the world’s Music
Facebook is home to
more than 40 billion photos
Youtube processes 48 hours of video every
minute
235 terabytes data collected by the US
Library of Congress by April 2011
15 out of 17 sectors in the United States have
more data stored per company than the US Library of Congress
The opportunity Why you should be interested
5
There are many ways that big data can be used to create
value across the sectors of the global economy. Key sector
agnostic business opportunities are:
• Creating transparency - Simply making big data
more easily accessible to relevant stakeholders in a
timely manner can create tremendous value. Making
relevant data more readily accessible across otherwise
separated departments creates significant
opportunities to save costs, increase quality and
improve time to market.
• Enabling experimentation to discover needs,
expose variability, and improve performance - As
they create and store more transactional data in digital
form, organizations can collect more accurate and
detailed performance data (in real or near real time)
on everything from product inventories to personnel
sick days.
• Segmenting populations to customize actions -
Big data allows organizations to create highly specific
segmentations and to tailor products and services
precisely to meet those needs. This approach is well
known in marketing and risk management but can be
revolutionary elsewhere—for example, in the public
sector where an ethos of treating all citizens in the
same way is commonplace.
• Replacing/supporting human decision making
with automated algorithms - Sophisticated analytics
can substantially improve decision making, minimize
risks, and unearth valuable insights that would
otherwise remain hidden. In some cases, decisions will
not necessarily be automated but augmented by
analyzing huge, entire datasets using big data
techniques and technologies rather than just smaller
samples that individuals with spreadsheets can handle
and understand.
Key sectors in which Big Data has already proven to be of
value by the early adaptors of Big Data are Health Care,
Public Sector, Retail, Manufacturing, Transportation, Oil
and Gas. In the next sections we will provide you with an
overview of business opportunities to leverage Big Data,
per sector.
Equens, a payments processor, uses a
sophisticated system to
discover and prevent fraudulent cards
transactions. Every transaction is compared in real-time with 1M previous transactions to detect this and to block suspicious
transactions and associated cards.
Leading players in
advanced industries are already embracing the
collaborative use of BigData and controlled
experimentation. Toyota, Fiat, and Nissan have all
cut new-model
development time by 30 to 50 percent; Toyota claims to have eliminated 80
percent of defects prior to building the first physical
prototype.
Amazon uses customer data to power its
recommendation engine “you may also like …” based
on a type of predictive modeling technique called collaborative filtering.
Tesco’s loyalty program
generates a tremendous amount of customer data that the company mines to inform decisions from promotions to strategic
segmentation of customers.
The opportunity Why you should be interested
6
Health Care
In the future, emergence of personalised medicine and
particularly personal genome sequencing means that
individuals will want to interpret their data in the context
of data from other people. A huge market is likely to
emerge in producing software solutions for interpreting
personal health related data, aimed at consumers instead
of healthcare professionals.
In the well-being area increasingly more activity and
cooperation is taking place between public and private
sector organizations in services provided for home care. In
this environment intelligent and interoperable data can
support flexible cross-organizational processes with
customer caring services and simultaneously increase the
cost efficiency of operations.
Public Sector
The data produced by organizations in the public sector
will be opened up to all actors as required by legislation
(e.g. EU Inspire Directive). This will bring data sources of
various kinds and different quality to the market; the
challenge will be to find relevant data and information
through processing of these data sources.
The growth in open data and its availability for real-time
use offer new challenges to data processing and to the
design of useful services around the data. The public
sector aims to open up their data sources for service
producers to exploit and commercialize which will promote
new business and help to enhance existing service
business. It will also help to give direction and focus to
well-being services for user groups where this will have
the best impact.
The B2B customers of the private sector have the same
objectives as (again) there is a need to consolidate
services, to develop new services that are introduced by
regulations and the standardization (of also existing)
services brought by EU regulations.
Erasmus Academic
Medical Centre uses
sophisticated sequencing algorithms to process DNA patterns. In one single test
over 2 Terabyte of information is processed.
The data storage is
optimised using BigData techniques, improving
performance of comparison tests from 11 minutes to less than a second. A huge improvement to enable more efficient and cost
effective cancer research.
The city of Boston has launched an application called Street Bump that takes advantage of
personal location data to detect potholes. Street Bump uses technology
already built into smartphones, including GPS and accelerometers, and
notes the location of the car and the size of the potholes
it crosses.
The German Federal Labour Agency has
sharply improved its customer services and cut around €10 billion of costs in recent years by using big data strategies. They are now able to analyze outcomes data for its
placement programs more accurately, spotting those programs that are relatively ineffective and improving or eliminating them. They developed a segmented
approach that helps the agency offer more effective placement and counselling to more carefully targeted
customer segments
The opportunity Why you should be interested
7
Retail
The next marketing-related big data lever is customer
micro-segmentation. Although this is a familiar idea in
retail, big data has enabled tremendous innovation in
recent years. The amount of data available for
segmentation has exploded, and the increasing
sophistication in analytic tools has enabled the division
into ever more granular micro-segments—to the point at
which some retailers can claim to be engaged in
personalization, rather than simply segmentation.
A particularly interesting direction is to combine the user
profile data with some contextual data, e.g., location data:
this would allow time- and location specific personalized
advertisements and notifications, route optimization,
navigation support etc.
Sentiment analysis leverages the voluminous streams of
data generated by consumers in the various forms of
social media to help inform a variety of business decisions.
For example, retailers can use sentiment analysis to gauge
the real-time response to marketing campaigns and adjust
course accordingly.
Manufacturing
The use of big data offers further opportunities to
accelerate product development, help designers home in
on the most important and valuable features based on
concrete customer inputs as well as designs that minimize
production costs, and harness consumer insights to reduce
development costs through approaches including open
innovation.
Open innovation through big data has been extended to
advanced industries as well. An additional benefit of these
open innovation techniques is that they create more brand
engagement from participants in these efforts, as well as a
positive “halo” effect as these initiatives become more
widely recognized.
The proliferation of Internet of Things applications allows
manufacturers to optimize operations by embedding real-
time, highly granular data from networked sensors in the
supply chain and production processes. This data allows
ubiquitous process control and optimization to reduce
waste and maximize yield or throughput.
Geo-targeted mobile ads ShopAlerts is a location-based “push SMS” product to drive traffic into stores. The company reports that 65 percent of respondents said they made a purchase
because of the message.
Smart, a leading wireless player in the Philippines, analyzes its penetration, retailer coverage, and
average revenue per user at the city or town level in order to focus on the micro markets with the most
potential.
Harrah’s, the US hotels
and casinos group, compiles detailed holistic profiles of its customers and uses them to tailor
marketing in a way that has
increased customer loyalty.
Rolls-Royce use sensors in its engines, sending real-
time performance measurements to a
centralised data centre. They are now able to
predict and prevent engine failure as well as invoicing their customers based on
engine usage.
Li & Fung Inc of Guangzhou in Southern China is one of the largest supply chain operators in the world. Clients are able
to monitor the details at every stage of an order from the start of the production run to the shipping; data flowing through the Li & Fung
network exceeds 1 terabyte
per day.
The opportunity Why you should be interested
8
Transportation
There are strong motives to promote stable and
sustainable traffic service development. Driving forces are
the consolidation of services, the development of new
services that are introduced by regulations and the
standardization services brought by EU regulations.
Smart routing based on real-time traffic information is one
of the most heavily used applications of personal location
data. The more advanced navigation systems can receive
information about traffic in real time, including accidents,
scheduled roadwork, and congested areas.
Over coming years, an increasing number of automobiles
will be equipped with GPS and telemetries that can enable
a range of personal safety and monitoring services.
Systems such as this can alert drivers to when they need
repairs or software upgrades, or can locate vehicles during
emergencies
Oil and Gas
Amid growing demand for energy, exploration and
production of energy sources requires the continual
development and deployment of innovative and complex
technologies. The combination of the increased technical
complexity and the increased demand equals information
explosion. Complexity and increasing demands leads to a
growth of information.
Oil and gas enterprises are driven to collaborate with each
other in joint ventures (JVs) to share risks by sharing the
investments. Time to first oil has to be accelerated to fulfil
increasing energy demands and to satisfy shareholders.
This adds to the demand for innovative and complex
technologies, which in turn fuels the data explosions, and
drives operators into further collaborations with various
technology and service partners.
The operation and monitoring systems now work with
huge sets of data that are collected routinely and only
parts of which can be analyzed in any meaningful way but
which should provide a basis for developing more efficient
operations. Proactive and preventive maintenance build on
advanced knowledge of the processes and effective
diagnostics systems that can identify upcoming problems.
BMW’s ConnectedDrive offers drivers directions
based on real-time traffic information, automatically
calling for help when sensors indicate trouble,
alerts drivers of maintenance needs based on the actual condition of
the car, and feeds operation data directly to
service centers.
Sense Networks is commercializing a machine-learning technology model that aggregates historical and real-time mobile phone location data to show the
overall activity level of the city, hotspots, and places with unexpectedly high activity, all in real time.
For decades, the oil industry has used huge amounts of real-time data to develop ever more hard-to-reach deposits. Now, the industry has extended its use of big data to the
production side to the automated, remotely monitored oil field. The
benefit of this approach is that it cuts operations and maintenance costs that can
account for 60 percent of wasted expenses.
Some of the best examples of using big data from
sensor networks come from the oil industry to optimise process
manufacturing such as oil refining.
The opportunity Why you should be interested
9
Utilities
Sustainable energy is our challenge for the future. To
realise this challenge generation of electricity will be more
and more decentralized. Local consumers will use solar
cells and small combined heat and power (CHP) units to
generate electricity.
To enable this local production and to manage a two-way
flow of electricity from producer to consumer we need
intelligent electricity networks with smart meters as part
of smart grids. Smart meters give information on local
consumption and production of electricity at intervals of
15 minutes. Sensors on the smart grids give
instantaneous information on the stability of the electrical
current and the location of possible outages.
During the day, depending on local needs of the
households and the power of the sun, the local consumer
can switch continuously between a consumer and
producer mode. In this two-way flow situation it becomes
more and more complex to maintain the stability of the
electrical grid on the desired high level. By using the
smart meters and sensors the smart grids create a
massive flow of data with a high granularity which has to
be analysed in a near real-time mode. To manage this Big
Data flow in the coming years in a proper way is a big
challenge for the Utilities.
Financial Sector
For the finance industry, the volume of the information
now stored in data warehouses is already overwhelming.
In most financial institutions the immediate use of big
data is in containing fraud and complying with rules on
money-laundering and sanctions. Even seemingly simple
tasks, such as checking the names of clients against those
on a sanctions blacklist, become immensely complicated in
the real world, where banks may have thousands of
customers with the same names as those on the blacklist.
EPD and Logica have realized in Evora Portugal a working pilot for 60.000 inhabitants in which the
interconnection of decentralized energy generation, advanced
sensor technology, smart metering and smart grids is demonstrated in a live
environment.
Bankinter, the tech-savvy
small Spanish bank, last year started using a system to analyse complex loan
portfolios. Cloud computing enables it to hire massive number-crunching capacity
whenever it needs it.
.
The opportunity Why you should be interested
10
When moving on to more complex tasks, such as
identifying the tiny percentage of fraudulent transactions
among the millions of legitimate ones, the demands
become ever greater. The problem is getting bigger
because as financial institutes have moved onto
computers and mobile phones, and payments have shifted
from cash to cards or electronic transfers, the
opportunities for fraud have proliferated.
Based on the power of Big Data solutions actuaries in the
Insurance firms can give better results on how well the
carrier is doing in terms of meeting their risk appetite,
how various products are performing, where the gaps are,
what the trends are. The information is now moving from
having an accounting, or retrospective, use to becoming
proactive, forward-looking information.
As the ability to process large amounts of data becomes
ubiquitous, financial institutes are discovering that it is
good for far more than fighting fraud. These data also
contain hidden nuggets of gold. Some banks have been
able to double the share of customers that accept offers of
loans and reduce loan losses by a quarter, simply by using
data they already have. Adoption of Big Data turns
unstructured data into intelligence to make the claims
process more efficient as well as move toward a customer-
centric approach.
Citi Group has more than 250 people in Asia working on data analysis. Last year it opened a new “innovation
lab” in Singapore that brings together those data
analysts with big
institutional customers and a large analytics centre in
Bangalore.
“We have deep and rich information about
customers that we can use to give them better
insights, rather than just providing us with better insight to improve our risk management,” says Alison Brittain, head of consumer
banking at Lloyds. .
The challenge To harnas the power of Big Data
11
The challenge we face is that we know we have the
technology to produce more data than we can ever hope
to make sense of. With data becoming a key competitive
asset, leaders must understand the assets that they hold
or to which they could have access. Organizations should
conduct an inventory of their own proprietary data, and
also systematically catalogue other data to which they
could potentially gain access, including publicly available
data, and data that can be purchased. Also a set of
technology challenges will often have to be addressed to
ensure consistent, reliable, and timely access to external
data.
The first challenge is caused by the sheer magnitude of
the data: handling of this type of astronomical data
sources calls for new, more efficient data analytics as
currently available solutions become unfeasible with
terabyte or petabyte level data sets that require
computationally extremely efficient algorithmic solutions,
and in many cases completely new, on-line methods that
can process and model the data sequentially at the time of
collection.
The second challenge is that once the information in the
data has been extracted and compiled into higher-level
models, we need to be able to access quickly the
relevant data or information that is most useful to the
user in the current context. An additional difficulty is
caused by the fact that the data is often not only big, but
it is also parcelled, consisting of potentially several data
sources that may contain heterogeneous data types. The
nature of this type of data makes it very difficult to
retrieve relevant pieces of data or information in a given
context, in particular when the links between the different
data elements in different data sources are not explicit, as
is the case in traditional multi-view learning, but implicit,
and have to inferred with the help of the constructed
models.
The third challenge is that the data is not only big, but it is
often extremely high-dimensional, which makes it very
difficult to understand the underlying phenomena. What is
needed is a rich toolbox of methods for representing the
information extracted from the raw data in such a manner
that the results help the user to understand the domain
better, and support decision-making processes by helping
in drawing conclusions about future events and in
The challenge To harnas the power of Big Data
12
estimating their probabilities. Collaboration of business
and IT in traditional Business Intelligence engagements
has been identified as one of the critical success factors a
long time ago. However this will not be enough to cope
with the challenges of harnessing Big Data. Close
collaboration of various IT disciplines, especially in the
areas of Enterprise Content Management, Data
Management and Business Intelligence is required. On top
of that we need collaboration with technology vendors as
well as data vendors. Key topics to address with the fore
mentioned collaborative teams are; embracing innovative
technologies, improving data governance, development
paradigms and organisational characteristics.
Embracing Innovative Technologies
To capture value from big data, organizations will have to
deploy new technologies and techniques. The range of
technology challenges and the priorities set for tackling
them will differ depending on the data maturity of the
institution. Legacy systems and incompatible standards
and formats too often prevent the integration of data and
the more sophisticated analytics that create value from big
data. New problems and growing computing power will
spur the development of new analytical techniques. There
is also a need for ongoing innovation in technologies and
techniques that will help individuals and organizations to
integrate, analyze, visualize, and consume the growing
torrent of big data.
Data Governance
As an ever larger amount of data is digitized and travels
across organizational boundaries, there is a set of policy
issues that will become increasingly important, including,
but not limited to, privacy, security, intellectual property,
and liability. Big data’s increasing economic importance
also raises a number of legal issues, especially when
coupled with the fact that data are fundamentally different
from many other assets. Data can be copied perfectly and
easily combined with other data. The same piece of data
can be used simultaneously by more than one person. All
of these are unique characteristics of data compared with
physical assets. Questions about the intellectual property
rights attached to data will have to be answered: Who
“owns” a piece of data and what rights come attached with
a dataset? What defines “fair use” of data?
The challenge To harnas the power of Big Data
13
Development Paradigm
New skill sets, new organizations, new development
paradigms, and new technology will need to be absorbed
by many enterprises, especially those facing the use cases
described in this paper. Even before the arrival of big data
analytics, data warehousing has been transforming itself
to provide more rapid response to new opportunities and
to be more in touch with the business community. Some
of the practices of the agile software development
movement have been successfully adopted by the data
warehouse community, although realistically this has not
been a highly visible transformation. But, in particular, the
agile development approach supports the data warehouse
by being organized around small teams driven by the
business, not typically by IT. With the introduction of Big
Data the need for an agile development approach has
become even more significant.
Organisational Characteristics
Organizational leaders often lack the understanding of the
value in big data as well as how to unlock this value. In
competitive sectors this may prove to be an Achilles heel
for some companies since their established competitors as
well as new entrants are likely to leverage big data to
compete against them. And, as we have discussed, many
organizations do not have the talent in place to derive
insights from big data. In addition, many organizations
today do not structure workflows and incentives in ways
that optimize the use of big data to make better decisions
and take more informed action. At this early stage of the
big data analytics revolution, there is no question that the
analysts must be part of the business organization, both
to understand the microscopic workings of the business,
but also to be able to conduct the kind of rapid turnaround
experiments and investigations we have described in this
paper. As we have described, these analysts must be
heavily supported in a technical sense, with potentially
massive compute power and data transfer bandwidth. So
although the analysts may reside in the business
organizations, this is a great opportunity for IT to gain
credibility and presence with the business.
Machine to MachineLeveraging the oppertunities in the Internet of Things
14
In this section we elaborate on our abilities to support you
in leveraging the power of “Big Data” in your organisation.
Starting with our support in overcoming the challenge
mentioned in the previous section, followed by a selection
of our “Big Data” offerings.
Big Data Technology
A growing number of EDW vendors support such key big
data features as shared nothing massively parallel
processing (MPP), petabyte s
analytics. However, the cost, proprietary nature,
inflexibility, and scalability issues of some MPP EDWs have
spawned the development of an emerging open source,
cloud
We regard Hadoop as the nuc
EDW in the cloud. Hadoop implements the core features
that are at the heart of most modern EDWs: cloud
architectures, MPP, in
management, and a hybrid storage layer.
Essentially
EDW for the new age of cloud
that require rapid execution of advanced, embedded
analytics against big data. Consistent with this trend,
many EDW vendors, such as EMC Greenplum, IBM,
Microsoft, and Oracle, ar
support Hadoop.
As a global System Integrator with over
consultants
area
partnerships with the big four technology vendors i
area
Big
Logica’s
as an asset: it is recorded in an inventory; it needs to be
valued on a regular basis; it will need
improvement and finally, once its economic value has
declined, to be disposed of. The challenge for DGBV is that
data does not have a physical form, can exist
simultaneously in a number of locations within the
systems architecture, and is
marketable value. However, it should have a design; it
should have standards defining its quality and fitness for
Logica evaluated by Gartner, Jan 2012... “Clients looking to utilize BI services for their business intelligence competency center (BICC) strategy should look to Logica” “Clients should look to include Logica on RFIs or RFPs for projects, particularly in large organizations, in which they can utilize and combine its business,
industry and technology knowledge for a more complete solution that provides business value.” “Logica is a good fit for clients looking for a provider that can provide deep industry skills in one of its focus verticals comprising the public sector, transportation, trade and industrial, energy and utilities, financial services, telecommunications and media.”
Machine to Machine Leveraging the oppertunities in the Internet of Things
14
In this section we elaborate on our abilities to support you
in leveraging the power of “Big Data” in your organisation.
Starting with our support in overcoming the challenge
mentioned in the previous section, followed by a selection
of our “Big Data” offerings.
Big Data Technology Understanding
A growing number of EDW vendors support such key big
data features as shared nothing massively parallel
processing (MPP), petabyte scaling, and in-database
analytics. However, the cost, proprietary nature,
inflexibility, and scalability issues of some MPP EDWs have
spawned the development of an emerging open source,
cloud-oriented approach known as Hadoop.
We regard Hadoop as the nucleus of the next-
EDW in the cloud. Hadoop implements the core features
that are at the heart of most modern EDWs: cloud
architectures, MPP, in-database analytics, mixed workload
management, and a hybrid storage layer.
Essentially the Hadoop market is the reinvention of the
EDW for the new age of cloud-centric business models
that require rapid execution of advanced, embedded
analytics against big data. Consistent with this trend,
many EDW vendors, such as EMC Greenplum, IBM,
Microsoft, and Oracle, are evolving their offerings to
support Hadoop.
As a global System Integrator with over 3000 qualified
consultants in the Enterprise Information Management
area we leverage a rich eco-system, having global
partnerships with the big four technology vendors i
area, as well as with innovative partners like Cloudera.
ig Data Governance by Value (DGBV)
Logica’s Data Governance by Value approach treats data
as an asset: it is recorded in an inventory; it needs to be
valued on a regular basis; it will need maintenance and
improvement and finally, once its economic value has
declined, to be disposed of. The challenge for DGBV is that
data does not have a physical form, can exist
simultaneously in a number of locations within the
systems architecture, and is not seen to have a
marketable value. However, it should have a design; it
should have standards defining its quality and fitness for
Leveraging the oppertunities in the Internet of Things
In this section we elaborate on our abilities to support you
in leveraging the power of “Big Data” in your organisation.
Starting with our support in overcoming the challenges
mentioned in the previous section, followed by a selection
A growing number of EDW vendors support such key big
data features as shared nothing massively parallel
database
analytics. However, the cost, proprietary nature,
inflexibility, and scalability issues of some MPP EDWs have
spawned the development of an emerging open source,
-generation
EDW in the cloud. Hadoop implements the core features
that are at the heart of most modern EDWs: cloud-facing
database analytics, mixed workload
the reinvention of the
centric business models
that require rapid execution of advanced, embedded
analytics against big data. Consistent with this trend,
many EDW vendors, such as EMC Greenplum, IBM,
eir offerings to
3000 qualified
in the Enterprise Information Management
aving global
partnerships with the big four technology vendors in this
Cloudera.
treats data
as an asset: it is recorded in an inventory; it needs to be
maintenance and
improvement and finally, once its economic value has
declined, to be disposed of. The challenge for DGBV is that
data does not have a physical form, can exist
simultaneously in a number of locations within the
not seen to have a
marketable value. However, it should have a design; it
should have standards defining its quality and fitness for
Machine to Machine Leveraging the oppertunities in the Internet of Things
15
use; it does have a cost to maintain; it needs to be
protected, all of which should be defined and recorded by
the enterprise. Finally, and most importantly, data does
have a value; there is the benefit arising from good
quality data that enables enterprises to make decisions
quickly, build world class processes, etc. or conversely the
lost opportunity costs of poor quality data, which requires
constant reconciliation and correction, and impedes an
enterprise’s agility to respond to new market
opportunities. By establishing DGBV, an enterprise
recognises it has a Data Portfolio so that, like any other
assets that the enterprise has, it can ensure that the right
data exists at the right time and in the right systems for
the right person. Most importantly if an enterprise’s data
is considered as an asset then it can be subjected to the
financial rigour that all assets are treated with, valued,
and reported in performance score-cards against budgeted
targets. Implementing Data Governance by Value is key in
leveraging “Big Data” opportunities in your organisation.
We support you in establishing the required Data
Governance Framework, Data models and standards, Data
Lifecycle, Data Ownership Lifecycle, Data Value Lifecycle.
Big Data Life Cycle Support
Logica’s knowledge and experience of
many years in Enterprise Information
Management is consolidated in a
practical framework, the Logica BI
Framework. Many approaches,
methodologies and architectures are
available for BI solutions in the market.
Each of them has its advantages,
disadvantages and specific application
areas. The Logica BI Framework offers
guidelines in the complex world of
Business Intelligence to make the right
choices and trade-offs between the
many possibilities offered by the
market. The BI framework consists of
four stages, covering the business and
ICT perspective as well as the change
and the service perspective. It represents a dynamic
system of interaction between business and ICT, and
between development and maintenance. It considers
Business Intelligence as a lifecycle, implemented by a
continuous business improvement program. It uses a BI
Logica optimised Data
Management at over 100
of its clients, leveraging
its Data Governance by
Value Framework.
Data Governance: Establishing a management structure, with clear roles,
responsibilities, and ownership for all data within an enterprise.
By Value: Establishing a standard data taxonomy,
metrics, and reporting, that delivers a data portfolio with a pseudo real-time cost benefit valuation.
Machine to Machine Leveraging the oppertunities in the Internet of Things
16
maturity model, supporting an organisation in each stage
of the BI lifecycle. It provides a comprehensive inventory
of the activities, models and products needed in the full BI
lifecycle. This inventory is based on well-established
principles in the ICT architecture arena and supports
structured and consistent delivery of BI. Also each stage is
supported by standardised evaluation and review
packages. Best Practices from our early engagements in
“Big Data” type initiatives at our clients as well as our
continuous collaboration within our eco-system of
technology partners are included in the BI Framework.
With that we provide a comprehensive and secure
foundation to build on with your “Big Data” initiative,
preventing common pitfalls and leveraging tangible
experience in the field.
Big Data Organisational Support
For engagement in “Big Data” it is
important that the right disciplines are
available. Business-, analytical- and IT
skills have to work together as one
community, either virtually or physically
implemented in the organisation.
The key responsibilities of this group
include; defining vision and strategy,
managing programs, developing user
skills, organising methodology
leadership, building technology blueprint,
establishing standards as last but not
least control funding. That is why we
consider “Big Data” initiatives as an
integrated value creation process, a
process that brings together the
company’s strategy, the construction of
the solution and applying the resulting products. In
addition to the obligatory project management activities
this also concerns the lifecycle management on the long
term. A structured growth of “Big Data” in an organisation
is crucial as otherwise sub-optimisation will introduce
bottlenecks resulting in an unreliable and inefficient
solutions. A structured life cycle management, as
presented here, enables you to prevent these issues from
happening.
5.
Def ine
Functional
scope
6.
Identify
Data &
sources
7.
Evaluate
& select
tools
1.
Analyse
Objectives
& CSF
8.
Develop
Implement
Train
2.
Articulate
strategy
3.
Prioritise
4.
Balance
KPI’s9.
Discover
& Explore 10.
Access,
Monitor &
analyse
11.
Develop
Decision
alternatives
12.
Share &
collaborate
Effectchange
Machine to Machine Leveraging the oppertunities in the Internet of Things
17
Industry insight says that: “Among traditional players in
the telecom space, Logica has the strongest focus on
M2M” (Berg insight 2010). The need to reduce costs
through greater operational efficiency is a common driver
across all sectors. New service offerings are being
created – Pay as You Drive Insurance, Smart Metering for
utilities, in-car “Infotainment” – which have not been
possible in the past. These business opportunities offer
added value for consumers and new revenue streams for
Logica’s clients. Logica’s deep sector knowledge and client
intimacy – combined with our technical expertise and
innovation – provide us with the ability to deliver the end
to end service clients are seeking.
Logica has a developed service offering which is based
upon careful analysis of the market and solid client
experience. Market analysis tells us that clients are
looking for a single company which can deliver a robust
end to end service. The innovative service offerings which
we have developed and our position in key markets give
us credibility with high profile partners. These partners
include big telco sector companies such as: Alcatel Lucent,
Ericsson, Nokia Siemens, O2, Orange, TeliaSonera,
Telenor, and Vodafone. They also include organisations
such as Intel, Mobistar, Ertico and Landis and Gyr.
Logica’s end to end service provides our clients with game
changing business analytics harnessing (near) real time
data from remote devices. This service is built upon
transaction based pricing and a 'pay-as-you- grow'
strategy.
This strategy allows clients to avoid huge up-front costs
and enables them to build a business where their revenue
and cost profiles are aligned.
Our global M2M development environment enables clients
and ecosystem partners to develop and test new M2M
solutions by using a common open source toolset in the
cloud.
.
Logica EMO is a solution to
help reduce vehicular emissions. It does this through a monitoring
system placed inside the vehicle connected to the vehicle computer. The
system calculates emissions
in real time using the data from the vehicle computer
and also captures associated driving characteristics. This
information is then sent wirelessly to Logica
backend servers for further analysis and reporting.
Using Logica EMO, vehicle owners can reduce
emissions and save money on fuel through better
driving habits.
Logica MEG is a vehicle tracking system designed to
monitor vehicle location in real-time. It provides features such as vehicle track and trace on a map, geo fencing and route based notifications. The system consists of a GPS
device fitted on the vehicle that wirelessly sends information to backend servers using a standard GPRS connection. Vehicle location can be seen
through a web interface or can also be fetched by
sending a SMS.
Collaborative Data 3.0 Fueling effective Joint Ventures in Oil & Gas Industry
18
Oil and gas enterprises are driven to collaborate with each
other in joint ventures (JVs) to share risks by sharing the
investments. Time to first oil has to be accelerated to fulfil
increasing energy demands and to satisfy shareholders.
This adds to the demand for innovative and complex
technologies, which in turn fuels the data explosions, and
drives operators into further collaborations with various
technology and service partners.
At one oil major, we’ve been working on an ambitious and
innovative project which gathers and offers lots of
information about commodity markets. It ensures
information reliability and provides users with powerful
analysis software that allows the sharing of knowledge
and knowhow. The service platform improves the whole
decision-making process for top management. The
solution combines software and shared practices to
manage strategic and marketing intelligence for gas and
power. It is based on four integrated and interconnected
parts:
• Portal and search – including functions of semantic analysis
and collaborative modules
• Business intelligence – including a worldwide energy market
data base
• Geographical business intelligence – combination of GIS and
data base indicators
• Content management – internal and external publications.
• It enables users to get to all their information through a single
access point. Its main features are:
o Search engine: for intuitive data selection
(documents, BI reports, maps)
o Analysis creation and publication to Microsoft Office:
everything published is constantly linked to the
database
Analysis sharing: with online expert communities.
This allows:
• Easy access to data
• Ability to monitor data sources and share Information on a
joint database
• Power to capitalise on collaborators’ knowhow.
Applied Customer Insight Anticipate and respond to your customer needs
19
Applied Customer Insight (ACI) enables organisations to
capture, interpret and act upon data acquired from the
multiple places where customers engage, and apply that
insight collaboratively across the whole business
operation.
ACI starts by combining information from line of business
and CRM systems with data to provide the information
from which customer insight can be created. The data can
be sourced from marketing campaigns, customer interface
analytics, transactional behaviour, social media traffic and
other digital channels (referred to as ‘Big Data’).
The key to ACI success is to concentrate on the data
metrics that marketing campaigns and sale transactions
can affect. To put it simply, measure what can be affected
through sales, don’t try and measure everything. To be
able to action the insight, the timescales require a radical
new approach to sharing the insight across the
organisation. The most effective way to do this is to create
online communities that reach beyond the boundaries of
the organisation to its partners and supply chain.
Our clients usually already successfully create value from
the large amounts of data that it manages – both for itself
and for its customers. However there are increasing
external sources of data that our clients may not have
used before that might provide very relevant customer
and market information. Understanding the behaviour and
sentiment of customers requires social listening, capturing
what is said about your client in the social world,
establishing the clients social footprint.
Capturing that data, infer meaning from it and integrate it
with client's internal data sources requires solutions
referred to in the marketplace as "BigData". Based on our
global ECM&BI practices as well as our specific
partnerships with Microsoft and IBM on Big Data we are
able to provide your clients with the right approach and
technology solutions, avoiding common pitfalls, providing
the foundation for true customer insight. Big Data is the
fuel that powers ACI.
In just 6 months a worldwide mobile
operator achieved a
double-digit reduction in percentage churn and generated additional
revenues of €1.3 million. The operator now runs more than 200 highly
targeted campaigns a year to stimulate usage and sell specific bundles. Return on investment in marketing campaigns exceeds the best previous results by
over 230%
Allianz created an operational and integral customer view, 360
degrees, including; Unique
Broker and contact data, unique retail customer data; in all LoB’s and
Allianz NL wide secondary processes; Centralised and
standardised broker
management and client management; support and synchronisation across all
channels.
Contact Information Who to engage with…
20
We welcome you to contact us to engage with you in exploring your business
opportunities with “Big Data” in more detail through your regular Logica
contact person or our local contacts on “Big Data”...
Region Name Email
Global Henk van Roekel henk.van.roekel@logica.com
Benelux Thomas Rodenburg thomas.rodenburg@logica.com
France Fredrik Ware fredrik.ware@logica.com
UK Phil Smith philip.smith@logica.com
Denmark Pelle Eiland pelle.eiland@logica.com
Germany Markus Kollas markus.kollas@logica.com
Sweden Niklas Karlsson niklas.karlsson@logica.com
Finland Kari Natunen kari.natunen@logica.com
Iberia Pedro Machado pedro.machado@logica.com
USA Craig Bauhaus craig.bauhaus@logica.com
Latin America Rodrigo Aguiar rodrigo.aguiar@logica.com
India Jaganmohan Ramani jaganmohan.ramani@logica.com
Insights presented in this whitepaper are based on Logica’s experts and
evaluation of acknowledged research in our eco-system, including...
Resource Document Date
McKinsey Big data: The next frontier for innovation, competition, and
productivity
June 2011
TiViT Data to Intelligence (D2I) Strategic Research Agenda June 2011
Kimball Group The Evolving Role of the Enterprise Data Warehouse in the Era of
Big Data Analytics
Q1 2012
Forrester The Forrester Wave - Enterprise Hadoop Solutions Q1 2012
21
Logica is a business and technology service company, employing 41,000 people. It provides
business consulting, systems integration and outsourcing to clients around the world, including
many of Europe's largest businesses. Logica creates value for clients by successfully integrating
people, business and technology. It is committed to long term collaboration, applying insight to
create innovative answers to clients’ business needs. Logica is listed on both the London Stock
Exchange and Euronext (Amsterdam) (LSE: LOG; Euronext: LOG). More information is available
at www.logica.com.