MAALBS Big Data agile framwork

1 MphasiS BIG Data Analytics practice
The use of Big Data is becoming a crucial way for leading companies to outperform their peers. In most industries, established competitors and new entrants alike
will leverage data-driven strategies to innovate, compete, and capture value. Indeed, we found early examples of such use of data in every sector we examined.
In healthcare, data pioneers are analyzing the health outcomes of pharmaceuticals when they were widely prescribed, and discovering benefits and risks that
were not evident during necessarily more limited clinical trials. Other early adopters of Big Data are using data from sensors embedded in products from children’s
toys to industrial goods to determine how these products are actually used in the real world. Such knowledge then informs the creation of new service offerings
and the design of future products
Big Data will help to create new growth opportunities and entirely new categories of companies, such as those that aggregate and analyze industry data. Many of
these will be companies that sit in the middle of large information flows where data about products and services, buyers and suppliers, consumer preferences and
intent can be captured and analyzed. Forward-thinking leaders across sectors should begin aggressively to build their organization’s Big Data capabilities.
In addition to the sheer scale of Big Data, the real-time and high-frequency nature of the data are also important. For example, ‘now casting,’ the ability to
estimate metrics such as consumer confidence, immediately, something which previously could only be done retrospectively, is becoming more extensively used,
adding considerable power to prediction. Similarly, the high frequency of data allows users to test theories in near real-time and to a level never before
possible.
Why Big Data a competitive advantage?

Success Stories of Big Data Business Values of Big Data
 Data centers can reduce storage costs and improve
utilization rates by as much as 30%.
 Financial services firms can store and provide access
to granular transaction records for the 5–7 year
timeframes mandated by the regulatory demands of
Dodd-Frank, Sarbanes-Oxley, etc.
 Transportation companies can save up to USD
0.5million per annum by retaining its truck drivers.
 Enterprise Information Technology departments can
cut production support costs by 30%..
 Companies can move from batch processing of MRO
data to event driven processing.
 Statistical models can be executed up to 50 times
faster improving response times for business
decisions.
 Insurance companies can increase profitability by 30%
by identifying 5% more fraudulent claims
 Increased customer satisfaction
 Improving Patient Care
 Identifying and Reducing Fraud
 Optimizing Network
 Geo-Location marketing
 Assessing & Monitoring Risk
 Preventive Maintenance
 Unlimited scale of storage and processing
provides new flexibility.
 Scalable real-time processing
 New data accessibility
 Clinical trials
 Genomics
 Spatial Science
 Weather Predictions
 Disease patterns
 Patients behaviors
 ……………………………………………………
MphasiS Big Data Technology Partners
 IBM
 Vertica

MphasiS Future plans on Hadoop BigData
 Building Big Data COE
 Partnering with Hadoop Vendors like Cloudera, IBM, Hortonworks,
Map R
 Working currently to build the Big Data team of 10-12 team size
with Hadoop and other Big Data skills in next 2 quarter of time
 Working on setting up the infrastructure with Hadoop technology
for the POCs, trainings and POCs
 Next Steps - Mphasis is willing to partner with HP to define the use
case for Big Data and take it forward as POC at 50% of Mphasis
cost investment
 GTM Strategy with HP
 To start with, Mphasis can provide the Hadoop resources for
the confirmed Big Data projects/POCs for HP and work as an
extended team for HP.
 Once we have couple of implementation cycles completed in
next 2 quarters, we can own and delivery of the Big Data
projects
3 Dimensions of Big Data COE
 Trained talents on Hadoop and its Ecosystems
 Doing POC’s on Sentimental analysis using Semi Structured Data on AMAZON
Cloud using EC2 platform
 Working currently to build the Big Data team of 10-12 team size with Hadoop and
other Big Data skills in next 2 quarter of time
 In the process of evaluating the Big Data use cases across our customer base in
Insurance and BCM market units and plan for 2-3 POC’s in next 2 quarters
 Working on setting up the infrastructure with Hadoop technology for the POCs,
trainings and POCs
MphasiS ASIS state on Hadoop Bigdata MphasiS current capability on Hadoop and Ecosystems
 10+ trained resources on Hadoop and its Eco systems out of
which 4 have been trained on IBM Big Insights.
 2 certified talents on Cloudera
 4 certified talents on UBM Big Insights

MphasiS AGIL Application Life cycle Business Service for BigData 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 require. Companies need to ensure ongoing quality in terms of functionality, performance, availability, and security; the
business simply depends on it.
The system development and support process is complicated and complex. Therefore maximum flexibility and appropriate control is required. Evolution favors
those that operate with maximum exposure to environmental change and have 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.
Inline with the agility existing in the current business and technology challenges , MphasiS has adopted a framework called MAALBS -MphasiS AGIL
application Life Cycle Business Services which is a blend of AGILE SCRUM + ITIL+ LEAN for effective delivery model for BIG Data Services which is a proven
for short term wins which is of iterative and incremental and adopts the effective change management process for responding to the change which depends on
the degree of Change request and also adopts the LEAN methodology for eliminating the waste , Amplify Learning, Deliver as Fast as Possible, Empower
Teams and Build Integrity In on the deliver quality workable product right from DISCOVER, DESIGN, DEVELOP.DEPLOY & SUPPORT. (4D) = (4A) (Acquire ,
Analyze , Assemble and Act ).
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 the processes and tools
 Quality deliverable over comprehensive documentation
 Stakeholder’s collaboration over contract negotiation
 Rooms for innovations and welcoming Changes over following a plan
Manifesto of MAALBS - MphasiS AGIL Application Life cycle Business Services

 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.
 The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.
 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.
Principles of MAALBS - MphasiS AGIL Application Life cycle Business Services

MphasiS Mind maps on Big Data Projects

Phases of MAALBS -MphasiS AGIL Application Life cycle Business Services
MAALBS has the following group of phases
Pre-game: Story Gathering and Design
Discover:
This phase involves “what to do”, it’s a story or the requirement gathering phase on a currently known Product backlogs which will be shared by the Product
owner holds high level information. Each and every member of the team individually interacts with the Product owner, get the confirmed requirements or
approved requirements by continuous interactions by verification validations and review process. Once the requirement gets frozen it is estimated in terms of 8
hours chunks on each activity, which gives a consolidated effort estimate for a particular Product Backlog. For a new story board i.e. the requirement this
discovery consists of both conceptualization and analysis and for an existing story or requirement is being enhanced this phase consist of limited analysis.
The Discover phase includes the Design, on how the backlogs items will be implemented. This phase includes the architectural design of a particular backlog
which is subjected to documentation, verification validation and review of the design document.
Game: Code/Interface development, Testing and Deployment
Development Sprints:
This phase is followed on the approval of the Design document where the team starts developing the Interface. Once the code development of the product
backlog has been completed it will be subjected to verification, validation and review process with stakeholders from the client side. This code development is
accomplished by thorough unit test by individuals responsible for that Product/Interface. The Deployment of Product backlog will be executed based on
documentation of an appropriate build case followed by the manual verification of the entries in the document constructed by the individual. There are multiple,
iterative development sprints/cycles that are used to evolve the system.

Post-Game: Supporting Functions
Post-Game activity of Development & Deployment phase is supporting functions where the MAALBS employs the ITIL process which comprises of Operations,
Knowledge & Continuous service improvement.
Discover – Story Gathering
Phase Gate IN Process Phase Gate OUT
High level requirements will be
shared in brief through
PowerPoint, Excel or
Documents.
The product owner will prioritize the product from the product backlog and
provide the details to the MAALBS team.
In sprint planning meeting the team will analyze the requirements like business
requirement document, functional requirement document for all the prioritize
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.
Approved Business/Functional
requirement
Document
Design
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

Development
Approved Technical design
document
The development activities are sub-categorized into multiple task/steps
as per the level of estimates (LOE) 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 get 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
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 construct the deployable document pertains to the particular
product / Interface and checks manually checks all the entries in the
Deployable document 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
MAALBS -MphasiS AGIL Application Life cycle Business Services for Operational challenges
MAALBS service operation related activities is carried out by the MAALBS support team. The MAALBS support team is responsible le for following service operation
Service Desk Function
 The MAALBS team is responsible for the following Service operations
 Serves as a First Point of contact
 Owns the logged request and ensure it is getting in line with user acceptance
 Do a First level fix and First level Diagnosis
 Serve as liaison between the end user and IT service provision teams
 Support other IT provisions activities on need basis
 Escalate to the appropriate team when things goes out of control
 Plays a vital role in achieving the customer satisfaction

Incident Management
 The MAALBS support team is responsible for restoring the service of the application in line with agreed SLA on the 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.
Problem Management
 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.
 These records are documented in the Knowledge management records
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. Technology to build a strong team.
MAALBS Continual Service improvement
 MAALBS team adopts the continual service improvement model to efficiently manage their development as well as Service related activities

MAALBS LEAN Adoption
 Optimal usage of resources by eliminate 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 inline with the internal and external stakeholders expectations (Build Quality In)
 See the Whole (Optimize the Whole)

MphasiS Future Service offerings on Big Data
Mphasis provides a range of prioritized offerings to help you harness the power of Big Data. From pure technical solutions to store and process Big
Data, to blended or industry-specific offerings that combine business and technology components, we can help you overcome your biggest challenges
.
MphasiS Offering Solution Details
Analytics on Unstructured Data MphasiS offers solutions in the areas of Unstructured Content Analysis, Text Analytics and Social Media Analytics. We enable organizations to make stronger data driven
decisions to improve Operations and better respond to customers. .
. MphasiS offers analysis on human .sentiment from social media posts to convert them into domain specific insights
MphasiS also offers solutions that are based on analysis of other unstructured content like Log files, Machine Logs, Images, etc
Integration of Structured &
Unstructured Data
Integrating unstructured types of data with other structured forms of data can lead to new insights for your business.
MphasiS offers Analytics and Visualization frameworks that blend business / industry specific knowledge with technology solutions to help clients integrate structured and
unstructured data and gain 360o
insight in an accelerated manner.
.
Advanced Analytics Solutions MphasiS has applied Data Mining and Predictive Models to solve a variety of problems including Optimization, Employee Retention, Fraud prevention, and more.
Migration to a Big Data platform
( Future offerings)
MphasiS has expertise in all popular Big Data platforms, viz :
a) Hadoop and its commercial distributions offered by Cloudera and IBM b) Hadoop ecosystem databases like Hive and Hbase c) Column oriented analytics databases
like Vertica d) Appliances like SAP HANA, Teradata, Netezza, Oracle Exalytics, and more
MphasiS offers consulting and technical offerings to enable migration to a Big Data platform :
• Migration to an Appliance
• Migration of entire technology platform to a Hadoop ecosystem
MphasiS leverages its proprietary technical solution accelerators to provide value-added services to clients.
Hosted Offerings
( Future offerings)
MphasiS offers hosted services in the Big Data space. Big data labs have been set up on leading technologies including :
Cloudera Hadoop distribution • IBM Big Insights • HP Vertica •
Big Data Consulting Framework
( Future offerings)
From Business Case definition to Technology evaluation and Architecture recommendations, MphasiS consulting services facilitate adoption of a Big Data platform. We can
also assess unstructured data generated as part of regular business processes, and define the business value and insight they can deliver. The consulting framework
leverages a comprehensive library of artifacts which have been created by MphasiS Big Data Center of Excellence. These artifacts include :
• Business use cases
• Point of view collaterals on various Big Data tools and their application
• Questionnaires to assess existing business and technology landscape.

SMAC POC Implementation
Business Objective :
The objective of this POC is to identify the Positive and
Negative comments tweaked on a specific event and
suggest or recommend the business to come up with a
effective decision based on the outcome of the tweak
exchanged over on the .social media.
MphasiS Approach : Inline with the agility existing in the
current business and technology challenges , MphasiS has
adopted a framework called MAALBS -MphasiS AGIL
application Life Cycle Business Services which is a blend of
AGILE SCRUM + ITIL+ LEAN for effective delivery model for
BIG Data Services which is a proven for short term wins
which is of iterative and incremental and adopts the
effective change management process for responding to the
change which depends on the degree of Change request
and also adopts the LEAN methodology for eliminating the
waste , Amplify Learning, Deliver as Fast as Possible,
Empower Teams and Build Integrity In on the deliver quality
workable product..
MphasiS BIG Data technology approach :
MphasiS adopts 4A model say Acquire , Analyze ,Assemble
and Act to accomplish the business and technical
requirements inline with the stakeholders expectations .
Technology Stack :
Ubuntu 12.1.0 , Hadoop Version : 1.0.3 , MR Language :
PIG v 0.8 , DW Database : HIVE : V0.9.0 , RDBMS :
MYSQL : 5.5 , Algorithm TPE , SWN –SentiWordNet , Spell-
check-GNU Aspel Library V 0.5.0.1 Cloud services : AWS ,
EC2 ,Clusters tested : 1 Master && 2 Slaves

MphasiS Global offices

MAALBS Big Data agile framwork

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