Mphasi s agil_analytics_life_cycle_business_style_for_big_data_services[1]

A White Paper
by MS Balaje Viswanaathan
Big Data and BIDW Practitioner, Analytics
MphasiS
balajeviswanaathan.m@mphasis.com
MphasiS AGIL
Analytics Life
Cycle Business
Style (MAALBS) for
Big Data Services

A White Paper on MphasiS AGIL Analytics Life Cycle Business Style (MAALBS) for Big Data Services MphasiS 2

Contents
Executive Summary....................................................4
What is “Big Data”......................................................4
Business and Process Drivers for Big Data...............5
MAALBS for BAAS
A Road Map for Big Data Services....................................7
MAALBS Style for Big Data Services.........................8
Manifesto of MAALBS................................................8
Principles of MAALBS................................................9
MphasiS Mind Maps on Big Data Projects................9
Phases of MAALBS .................................................10
High Level Architecture of MAALBS
Big Data Process..........................................................15
MAALBS for Operational challenges.................................. 15
MAALBS LEAN Adoption.........................................16
MphasiS MAALBS Big Data Team...........................17
Conclusion...............................................................17

Executive Summary
Today, the volume and complexity of market data required by diverse
industries such as BFSI, Retail, Healthcare, Communication Media,
Energy Utilities etc. have become immense and is growing at a rapid
phase. Ongoing market changes have accelerated the demand for
larger volumes of data, thus forcing industries to address this so-called
challenge “Big Data”. This demand is fueled as firms develop and deploy
new, sophisticated strategies. At the same time regulatory changes are
also forcing firms to source and report increasingly larger volumes of trade
data, as well as to adopt higher quality – and usually data-hungry – risk
and pricing models.
Social network data is also adding to this superabundance of data.
The micro-blogging site Twitter serves more than 200 million users
who produce more than 90 million “tweets” per day i.e. 800 per second.
Each of these posts is approximately 200 bytes in size. On an average,
this traffic equals more than 12 gigabytes, a day and, throughout the Twitter
ecosystem, the company produces a total of 8 terabytes of data per day.
Facebook announced they had surpassed the 750 million active-user
mark, making the social networking site the largest consumer-driven data
source in the world. Facebook users spend more than 700 billion minutes
per month on the service, and the average user creates 90 pieces of
content every 30 days. Each month, the community creates more than 30
billion pieces of content ranging from web links, news, stories, blog posts
and notes, to videos and photos.
Everywhere you look, the quantity of information in the world is soaring.
The term “Big Data” has emerged to describe this monstrous growth in data.
“Big Data” represents data sets whose characteristics are comprised of high
volume, high velocity, and a variety of data structures.
What is “Big Data”
“Big Data technologies describe a new generation of technologies
and architectures designed to economically extract value from very large
volumes of wide variety of Data, by enabling high-velocity capture, discovery
and / or analysis.”
“Extremely scalable analytics – analyzing petabytes of structured
and unstructured data at high velocity.”
“Big Data is data that exceeds the processing capacity of conventional
database systems.”
“Big Data is a technology that helps extract value from digital universe.”
Technology vendors in the fields of Legacy Database or Data Warehouse
say “Big Data” simply refers to a traditional data warehousing scenario
involving volumes of data that are available either in single or multi-terabyte
range. Others disagree: that “Big Data” is not limited to traditional Data
Warehouse situations, but includes real-time or operational data stores
used as the primary data foundation for online applications that power key
external or internal business systems. It used to be that these transactional/
real-time databases were typically “pruned” so they could be manageable
from a data volume standpoint. Their most recent or “hot” data stayed in
the database, and older information was archived to a Data Warehouse via
extract-transform-load (ETL) routines.

Business and Process Drivers
for Big Data
Business Drivers
Volume
Potential of terabytes to petabytes of data.
Data volume is the primary attribute of “Big Data.” Volume is often quantified
in terms of terabytes of data. Anything between 3 to 10 terabytes of data
falls within the realm of “Big Data”. In addition, data volume can also be
quantified by counting records, transactions, tables, and files. A large
number of records, transactions, tables, or files can be categorized as
“Big Data”. Though the volume of data is one of the defining characteristics
of “Big Data”, data velocity and data variety (highlighted below) constitute
the other key characteristics/ingredients of “Big Data”.
Variety
All types of data are now being captured such as structured, semi-structured,
unstructured, streaming data, video, audio, Radio Frequency Distribution and
Sensors (RFID) etc.
A significant factor that makes “Big Data” considerably immense is that
it is coming from a greater variety of sources than ever before. Data from
web sources (i.e., web logs, clickstreams) and social media is remarkably
diverse. RFID data from supply chain applications, text data from call center
applications, semi-structured data from various business-to-business
processes, and geospatial data in logistics make up an eclectic mix of data
types. Variety and diversity have therefore become an important attribute
characterizing “Big Data”.
Velocity
How fast does the data come in? Speed or velocity of data is another
defining characteristic of “Big Data”. Data velocity encompasses the
frequency of data generation and the frequency of data delivery. In today’s
hyper-connected and networked society, there is a continuous stream
of information coming from a range of devices ranging from sensors and
robotics manufacturing machines, to video cameras and mobile gadgets.
This ever-increasing amount of data relentlessly flying from devices
in real-time is causing data volumes to grow and do so in a hurry.
Big Data Drivers
Verification
Validation
Value
Process DriversBusiness Drivers
Volume
Variety
Velocity

Process Drivers
Verification
It is a process where data is checked for any inaccurate and inconsistent
information after migration. It helps to determine whether
(i) data is accurately translated while it is transported from one source
to another,
(ii) is complete, and
(iii) supports processes in the new system.
During data verification, there may be a need for a parallel processing of
both systems to identify areas of disparity and forestall erroneous data loss.
Validation
It is a process of ensuring that a program operates on clean, correct and
useful data. It uses routines, often called “validation rules” or “check routines”,
that check for correctness, meaningfulness, and security of data that are input
to the system. The rules may be implemented through the automated facilities
of a data dictionary, or by the inclusion of explicit application program
validation logic.
For business applications, data validation can be defined through declarative
data integrity rules, or procedure-based business rules. Data that does not
conform to these rules will negatively affect business process execution.
Therefore, data validation should start with business-process definition and
set of business rules within this process. Rules can be collected through the
requirements capture exercise. The simplest data validation verifies that the
characters provided come from a valid set. For example, telephone numbers
should include the digits and possibly the characters +, –, and () (plus, minus,
and brackets). A more sophisticated data validation routine would check to
see whether a user had entered a valid country code, i.e., that the number
of digits entered matched the convention for the country or area specified.
Incorrect data validation can lead to data corruption or a security vulnerability.
Data validation checks that data is valid, sensible, reasonable, and secure
before they are processed.
A validation process involves two distinct steps:
(a) Validation Check and
(b) Post-Check Action. The check step uses one or more computational
rules (see section below) to determine if the data is valid.
The post-validation action sends feedback to help enforce validation.
Value
With all the Volume, Variety and Velocity existing in the business, processing
of Big Data helps in deriving value and insight from it to be able to tie it with
business plan that can drive business outcome, ROI and profitability.

MAALBS for BAAS – A Road Map
for Big Data Services
MAALBS for Big Data Services
Every IT organization wants to accelerate innovation, lower costs,
and ensure the high quality of its services. Yet, each of these goals
presents challenges.
Companies need to discover and evaluate the implications that business
innovations may have on their system landscapes — and IT must work
to minimize any system downtime these innovations may confront.
Companies have to ensure ongoing quality in terms of functionality,
performance, availability, and security as the business is dependent
on all these parameters.
The system development and support process is complicated and complex.
Therefore, maximum flexibility and appropriate control is required. Evolution
favors those who operate with maximum exposure to environmental
change and are optimized for flexible adaptation to change. Evolution
deselects those who have insulated themselves from environmental change
and have minimized chaos and complexity in their environment.
The term “Big Data” has become a buzz in both the business and the
technology world. There are numerous conferences, seminars, webinars
and forums on the topic of Big Data and Cloud Computing and the subject
seems like an overused word today. There is still some ambiguity about what
comprises Big Data – Is it just the sheer volume or is it mix of volume, variety,
velocity regardless of the size of data or is it the voluminous unstructured
data coming from social media and machine logs?
Now the scope of the Big Data drivers has expanded from three dimensions
to six dimensions such as volume, velocity, variety to verification, validation
and value. The first three Vs fall into features of Big Data while the last three
Vs come under process and business outcomes.
Therequiredapproachshouldenabledevelopmentteamstooperateadaptively
within a complex environment using imprecise processes. Complex system
development occurs under rapidly changing circumstances. To overcome
these challenges, MphasiS an HP company’s Analytics team has come up with
a robust methodology for their Big Data Roadmap called MAALBS process
which tailors the combination of AGILE SCRUM, ITIL framework and Lean
to efficiently manage and support the entire life cycle of their applications
right from Discover, Design, Develop, Deploy Support. (4DS) = (4A) (Acquire,
Analyze, Assemble and Act).
Discover
Design
Develop
Deploy
Support
Acquire
Analyze
Assemble
Act
MAALBS = BAAS

MAALBS for Big Data Services
Envision and Explore
Process
Institutionalization
Defect
Prevention
MAALBS is an Hybrid AGILE framework which blends SCRUM + ITIL + LEAN
ContinuousServiceImprovement
KnowledgeManagement
Operational Management
Product
Owner
Product
Backlog
Item 1
Item 2
Item 3
Item 4
Item 5
Sprint Backlog
Item 1
Item 2
Item 3
Release Backlog
Item 1
Item 2
Item 3
ACT=DEPLOYMENT
Phase Gate
IN – Approved
BRD
Phase Gate
OUT – Approved
TDD
Phase Gate
IN – Report in
running condition
Phase Gate
Out – Demo
Phase Gate
IN – Approved
TDD
Phase Gate
OUT – Developed
Report
Phase Gate
IN – UTC
Phase Gate
OUT – UTR
Sprint
Meeting with
PO/SM/Team
Retrospective
R D phase and
Promoting the product to
Support Team to include
in GO LIVE schedule List
NO
Business
Acceptance
REVIEW
TESTINGASSEMBLE=DEVELOP
3-4 Week Sprint
YES
1
4 5
6
7
X
X
ANALYZE = DESIGN
3
ACQUIRE = DISCOVER
2
Envision
Speculate
Explore
Adapt
Close
Manifesto of MAALBS
We are uncovering better ways of developing software by doing it
and helping others do it. Through this work we have come to value:
• Team collaboration over processes and tools
• Quality deliverable inline with intelligence over comprehensive
documentation
• Stakeholder’s collaboration over contract negotiation
• Rooms for innovations and welcoming changes over following a plan
At a higher level, MAALBS tailors and adapts the AGILE Project
Management framework introduced by the expert Jim High Smith.
The framework is as follows:
• Envision: Determine the product vision and project scope, the project
community, and how the team will work together
• Speculate: Develop a feature-based release, milestone, and iteration
plan to deliver on the vision
• Explore: Deliver tested features in a short timeframe, constantly
seeking to reduce the risk and uncertainty of the project
• Adapt: Review the delivered results, the current situation, and the
team’s performance, and adapt as necessary
• Close: Conclude the project, pass along key learnings, and celebrate

Principles of MAALBS
• Our highest priority is to satisfy the customer through early and continuous
delivery of valuable software
• Welcome changing requirements, even late in development
• Providing rooms for innovation across project, process and technology
• Deliver working software frequently, from three weeks to six weeks,
with a preference to the shorter timescale
• Business people and developers must work together daily throughout
the project
• Build projects around motivated individuals. Give them the environment
and support they need, and trust them to get the job done
• Face-to-face conversation is the most efficient and effective method
of conveying information to and within a development team
• Working software is the primary measure of progress
• MAALBS processes promote sustainable development. The sponsors,
developers, and users should be able to maintain a constant pace indefinitely
• Continuous attention to technical excellence and good design enhances agility
• Simplicity – the art of maximizing the amount of work not done – is essential
• The best architectures, requirements, and designs emerge from self-organizing
teams
• At regular intervals, the team reflects on how to become more effective, then
tunes and adjusts its behavior accordingly
MphasiS Mind Maps on
Big Data Projects
1. Identifying the line
of Business10. Embed statistics
Analytics for effective
Decision Making
Visualization
MAALBS Big Data Mind Map
2. Data Collection
5. Tailoring and
adhering the standards
and governance to ease
the skills
9. Augmenting Hadoop
with Enterprise
Data Warehouse
6. Collaborating with
COE participating in
the tech forums
3. Proﬁling the
Business Data
4. Adopting MAALBS —
AGILE Process
8. Align Cloud
Operating Model
7. San Box Prototype
and performance
MAALBS
Big Data
Mind Map

Phases of MAALBS
The phases of the MAALBS has been classified into technical and AGIL
process, a blend of AGILE SCRUM, ITIL and LEAN
Technical Process: ACQUIRE, ANALYZE, ASSEMBLE AND ACT
Acquire
• Variety of data are collected in the aspects of heterogeneity, scale,
timeliness, complexity, in all phases of the pipeline that can create
value from data.
• When the data tsunami requires us to make decisions, currently in an
ad-hoc manner, about what data to keep and what to discard, and
how to store what we keep reliably with the right metadata.
• The value of data explodes when it can be linked with other data,
thus data integration is a major creator of value. Since most data is
directly generated in digital format today, we have the opportunity and
the challenge both to influence the creation to facilitate later linkage
and to automatically link previously created data.
• Much data today is not available in structured format; for example, tweets
and blogs are weakly structured pieces of text, while images and video
are structured for storage and display, but not for semantic content
and search: transforming such content into a structured format
for later analysis is a major challenge.
• Big data does not arise out of a vacuum: it is recorded from some data-
generating source. For example, consider our ability to sense and observe the
world around us, from the heart rate of an elderly citizen, and presence of toxins
in the air we breathe, to the planned square kilometer array telescope, which
will produce up to 1 million terabytes of raw data per day. Similarly, scientific
experiments and simulations can easily produce petabytes of data today.
AGIL Process: AGILE SCRUM , ITIL and LEAN
Discover – Story Gathering
Phase Gate IN Process Phase Gate OUT
High level requirements
will be shared in brief
through Power Point,
Excel or Documents.
The product owner
will prioritize the
product from the
product backlog
and provide the details
to the MAALBS team.
Approved Business/
Functional requirement
Documents
Image
Video, Audio
E-mails
Feed Back Forms
Twitter
Facebook
Linkedin
My Space
RSS Feed
Structured
Semi-structured
Un-structured
SocialMedia
XML
DWH DB
RDBMS
ORDBMS
Master-Detail
Structured Files
Web Logs
Contact Logs
Device Logs
Click Stream
Machine Generated
Session Logs

In SPRINT planning
meeting the team
will analyze the
requirements like
business requirement
document, functional
requirement document
for all the prioritized
product and scope it for
the upcoming Sprint.
The products which
cannot be agreed to
complete in the sprint
will be directly pushed
back to the product
backlog with proper
justification provided
to the product owner
like requirements (BR/
FR) not signed-off, huge
estimates due to report
complexity, resource
capacity, etc.
Document
Analyze
Frequently, the information collected will not be in a format ready for
analysis. For example, consider the collection of electronic health records
in a hospital, comprising transcribed dictations from several physicians,
structured data from sensors and measurements (possibly with some
associated uncertainty), and image data such as x-rays. We cannot
leave the data in this form and still effectively analyze it.
Rather we require an information extraction process that pulls out the required
information from the underlying sources and express it in a structured form
suitable for analysis. Doing this correctly and completely is a continuing
technical challenge. Note that this data also includes images and will in the
future include video; such extraction is often highly application dependent
(e.g., what you want to pull out of an MRI is very different from what you would
pull out of a picture of the stars, or a surveillance photo).
In addition, due to the ubiquity of surveillance cameras and popularity of
GPS-enabled mobile phones, cameras, and other portable devices, rich
and high fidelity location and trajectory (i.e., movement in space) data
can also be extracted.
We are used to thinking of Big Data as always telling us the truth,
but this is actually far from reality.
For example, patients may choose to hide risky behavior and caregivers
may sometimes mis-diagnose a condition; patients may also inaccurately
recall the name of a drug or even that they ever took it, leading to missing
information in (the history portion of) their medical record. Existing work
on data cleaning assumes well-recognized constraints on valid data
or well-understood error models; for many emerging Big Data domains
these do not exist.

Design
Phase Gate IN Process Phase Gate
OUT
Approved
Business/
Functional
requirement
document
MAALBS team starts work toward the
initial design of the product/interface
if more than one approach has been
suggested to Design the interface,
all the approach options are properly
documented in Technical design
document (TDD) and the approach
which will be followed get it singed-off
in order to avoid the confusion
at later stage
Approved
Technical
design
document
Assemble
Given the heterogeneity of the flood of data, it is not enough merely to record
and throw it into a repository. Consider, for example, data from a range of
scientific experiments. If we just have a bunch of data sets in a repository, it
is unlikely anyone will ever be able to find, let alone reuse, any of this data.
With adequate metadata, there is some hope, but even so, challenges will
remain due to differences in experimental details and in data record structure.
Data analysis is considerably more challenging than simply locating, identifying,
understanding, and citing data. For effective large-scale analysis all of this has
to happen in a completely automated manner.
This requires differences in data structure and semantics to be expressed in
forms that are computer understandable, and then “robotically” resolvable.
There is a strong body of work in data integration that can provide some of
the answers. However, considerable additional work is required to achieve
automated error-free difference resolution.
Mining requires integrated, cleaned, trustworthy, and efficiently accessible
data, declarative query and mining interfaces, scalable mining algorithms,
and big-data computing environments. At the same time, data mining itself
can also be used to help improve the quality and trustworthiness of the data,
understand its semantics, and provide intelligent querying functions.

Development
Approved Technical
Design Document
(TDD)
The development activities
are sub-categorized into
multiple task/steps as per
the level of estimates (LOEs)
shared to the product owner
and each task/steps are
carried out sequentially like
Code development, Report
development, application/
interface development etc.
Each task/step will have to go
through verification, validation
and review before the start of
the next task.
Develop verify validate
review Develop
The product owner gets
a frequent update on the
progress of development
activities in “Daily breakfast
meeting” from the SCRUM
master. The status on every
day’s development activities
are discussed in “Daily SCRUM
meeting” among the team
members and Scrum master
Workable Product
Act
By studying how best to capture, store, and query provenance, in conjunction
with techniques to capture adequate metadata, we can create an infrastructure
to provide users with the ability both to interpret analytical results obtained and
to repeat the analysis with different assumptions, parameters, or data sets.
Systems with a rich palette of visualizations become important in conveying
to the users the results of the queries in a way that is best understood in
the particular domain. Whereas early business intelligence systems’ users
were content with tabular presentations, today’s analysts need to pack
and present results in powerful visualizations that assist interpretation,
and support user collaboration.
Furthermore, with a few clicks the user should be able to drill down into each
piece of data that the user sees and understand its provenance, which is a key
feature in understanding the data. That is, users need to be able to see not just
the results, but also understand why they are seeing those results.
However, raw provenance, particularly regarding the phases in the analytics
pipeline, is likely to be too technical for many users to grasp completely.

DASHBOARD TREND MOBILE BI

One alternative is to enable the users to “play” with the steps in the
analysis – make small changes to the pipeline, for example, or modify
values for some parameters. The users can then view the results of these
incremental changes.
By these means, users can develop an intuitive feeling for the analysis and
also verify that it performs as expected in corner cases. Accomplishing this
requires the system to provide convenient facilities for the user to specify
analyses. Declarative specification, is one component of such a system.
Testing
Workable Product MAALBS team takes the sole
responsibility of constructing
the test cases and test
plan in line with business
requirements. The product
is tested for each and every
functional clauses the expected
and actual results are captured.
Test Case Results
Deployment
Deployable
Document
The Deployment phase
bridges the gap between the
MAALBS Development team
and MAALBS support. The
MAALBS Development team
constructs the deployable
document pertaining to the
particular product/interface
and checks all the entries
in the deployable document
manually with respect to the
particular environment. The
Support uses the deployable
document shared by the
MAALBS and deploys to the
respective environment say
PRODUCTION.
Workable Product

High Level Architecture of MAALBS –
Big Data Process
MAALBS for Operational Challenges
MAALBS service operation related activities is carried out by the MAALBS
support team. The MAALBS support team is responsible for following
service operations:
Service Desk Function
• Serves as a First Point of contact
• Owns the logged request and ensures it is getting in line
with the user acceptance
• Does a first level fix and first level diagnosis
• Serves as liaison between the end user and IT services provision team
• Supports other IT provisions activities on need basis
• Escalates to the appropriate team when things go out of control
• Plays a vital role in achieving the customer satisfaction BIGSHEETS,
DATA
VISUVALIZATION
BUSINESS
INTELLIGENCE,
REPORTS
MAP
PROGRAM
AUDIO,
VIDEO
DOCS,
TXT
WEB,
LOGS
SOCIAL,
GRAPH
SENSORS,
DEVICES
SPATIAL,
GPS
EVENTS,
OTHERS
FLUME
LUCENE
SOLR
OTHERS
API’s
DB
CASSANDRA
VERTICA
HBASE
HIVE
MAHOUT
MONGODB
STORAGES
HDFS

Incident Management
• The MAALBS support team is responsible for restoring the service of the
application in line with the agreed SLA on Interrupted services
• The incident is acknowledged and the events of the incidents are recorded on
a timely basis in the Incident Management Tool used by the MAALBS team
• The MAALBS team tracks and updates the progress of the incident until
it gets closed in line with the user acceptance
• The MAALBS team executes a professional approach on identifying root
cause of the incident
• The MAALBS support team ensures that problems are identified and resolved
• The MAALBS support team eliminates the recurring incidents
• The MAALBS support team minimizes impact of incidents or problems that
cannot be prevented
• The MAALBS team employs a strategic approach to execute a permanent
fix or a work around
MAALBS Knowledge Management
• The MAALBS team adopts a professional approach by gathering,
analyzing, storing and sharing the knowledge throughout the MAALBS
Life Cycle approach
• The MAALBS support as well as MAALBS development team cross trains
themselves across process, project and technology to build a strong team
MAALBS LEAN Adoption
• Optimal usage of resources by eliminating the waste
• Amplify learning through retrospectives (create knowledge)
• Decide as late as possible (defer commitment)
• Deliver as fast as possible (deliver fast)
• Work collaboratively by empowering the teams (respect people)
• Deliver quality work products in line with the internal and external
stakeholders expectations
• See the whole (optimize the whole)

MphasiS MAALBS Big Data Team
MAALBS is a cross-functional team that adopts AGILE SCRUM, ITIL and
LEAN for their POCs and Projects which will be implemented in an iterative
and incremental way in SPRINTS, The team has got the hyper-specialization
skills in Business Intelligence, Analytics and Hadoop Ecosystems.
Conclusion
MAALBS – An AGILE approach has helped projects on a value-driven delivery
model and also accelerated BI/DW development in a cost-benefit manner with
increased quality of deliverables. MAALBS also augments the incremental
delivery through SPRINTS by emphasizing continuous, incremental, and
evolutionary growth-and-improvement.
I would like to express my appreciation and thanks to all my leaders who
encouraged me in articulating this framework and I would also like to thank
Ganesh Jegannathan, Sampath Kumar Sundaramurthy, Senthil Nathan and
Saravanan Mohan who have helped me a lot by sharing their thoughts.

MS Balaje Viswanaathan
Big Data and BIDW Practitioner, Analytics
MphasiSAbout Author
MS Balaje Viswanaathan also known as “MS” has got 15 years of rich
cross cultural experience in the IT sector. He is currently engaged with
MphasiS – as a Delivery Group Manager – Business Intelligence and
Data Warehousing practices and is a Big Data practitioner. He has an
MCA degree from University of Madras and MBA in Systems and Project
Management. He is the author for LETZ DO PMP and LETZ DO ITILV3 [F]
which primarily focuses on Project Management Service Management
practices. He has got wide range of expertise in diversified fields of
Information Technology services which includes Data Warehousing and
Business Intelligence, Software Development, Maintenance and Testing,
Operations and Project Management. He has also implemented AGILE-
SCRUM Methodology in his recent BI assignment and has come up with BI
initiatives for Process Innovation Framework say MAALBS. MS has taken
training sessions on PMP, ITIL, AGILE SCRUM and DW ETL Informatica.
He is certified in the following disciplines:
• PMP from PMI, USA [PMI Member id: 728277]
• PRINCE2 [practitioner] from APMG UK
• AGILE SCRUM Master from SCRUM Alliance
• ITILV3 [F] from APMG UK
• Certified Six Sigma Green Belt
• Cloud Computing from EXIN
• IBM Mastery BIG Insights – IBM Big Data

About MphasiS
MphasiS an HP Company is a USD 1 billion global service provider, delivering technology based solutions across industries, including Banking
Capital Markets, Insurance, Manufacturing, Media Entertainment, Telecom, Healthcare, Life Sciences, Travel Transportation, Hospitality, Retail
Consumer Goods, Energy Utilities, and Governments around the world. MphasiS’ integrated service offerings in Applications, Infrastructure
Services,andBusinessProcessOutsourcinghelporganizationsadapttochangingmarketconditionsandderivemaximumvaluefromITinvestments.
For more information about MphasiS, log on to www.mphasis.com
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