Whitepaper BigData (2).pdf

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

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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.


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


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.


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.


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.

.


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


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?


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



Starting with our support in overcoming the challenge


of our “Big Data” offerings.

Big Data Technology Understanding



processing (MPP), petabyte scaling, and in-database




cloud-oriented approach known as Hadoop.

We regard Hadoop as the nucleus of the next-


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




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


valued on a regular basis; it will need maintenance and





systems architecture, and is not seen to have a



Leveraging the oppertunities in the Internet of Things



Starting with our support in overcoming the challenges




database




-generation


that are at the heart of most modern EDWs: cloud-facing

database analytics, mixed workload

the reinvention of the

centric business models




eir offerings to

3000 qualified

in the Enterprise Information Management

aving global

partnerships with the big four technology vendors in this

Cloudera.

treats data


maintenance and





not seen to have a




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.


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


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 [email protected]

Benelux Thomas Rodenburg [email protected]

France Fredrik Ware [email protected]

UK Phil Smith [email protected]

Denmark Pelle Eiland [email protected]

Germany Markus Kollas [email protected]

Sweden Niklas Karlsson [email protected]

Finland Kari Natunen [email protected]

Iberia Pedro Machado [email protected]

USA Craig Bauhaus [email protected]

Latin America Rodrigo Aguiar [email protected]

India Jaganmohan Ramani [email protected]

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

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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.

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