mphasi s agil_analytics_life_cycle_business_style_for_big_data_services[1]

A White Paperby MS Balaje ViswanaathanBig Data and BIDW Practitioner, Analytics

MphasiS

[email protected]

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 SummaryToday, 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 DriversVolumePotential 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 DriversVerificationIt 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.

ValidationIt 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 ServicesMAALBS 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.

The required approach should enable development teams to operate adaptively 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

ProcessInstitutionalization

DefectPrevention

MAALBS is an Hybrid AGILE framework which blends SCRUM + ITIL + LEAN

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Sprint BacklogItem 1Item 2Item 3

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ACT=DEPLOYMENT

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BRD

Phase Gate OUT – Approved

TDD

Phase Gate IN – Report in

running conditionPhase GateOut – Demo

Phase Gate IN – Approved

TDD

Phase Gate OUT – Developed

ReportPhase Gate

IN – UTCPhase GateOUT – UTR

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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 ProjectManagement 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 lineof Business10. Embed statistics &

Analytics for effectiveDecision Making &

Visualization

MAALBS Big Data Mind Map

2. Data Collection

5. Tailoring and adhering the standardsand governance to ease

the skills

9. Augmenting Hadoopwith Enterprise Data Warehouse

6. Collaborating withCOE & participating in

the tech forums

3. Profiling the Business Data

4. Adopting MAALBS —AGILE Process

8. Align CloudOperating Model

7. San Box Prototypeand performance

MAALBSBig Data

Mind Map


Phases of MAALBSThe 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

DocumentsImageVideo, AudioE-mailsFeed Back Forms

TwitterFacebookLinkedinMy SpaceRSS Feed

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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


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 ChallengesMAALBS service operation related activities is carried out by the MAALBS support team. The MAALBS support team is responsible for followingservice 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 fi rst level fi x and fi rst 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 satisfactionBI

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DBCASSANDRA

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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.

ConclusionMAALBS – 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 ViswanaathanBig 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:• PMPfromPMI,USA[PMIMemberid:728277]

• PRINCE2[practitioner]fromAPMGUK

• AGILESCRUMMasterfromSCRUMAlliance

• ITILV3[F]fromAPMGUK

• CertifiedSixSigmaGreenBelt

• CloudComputingfromEXIN

• IBMMasteryBIGInsights–IBMBigData

About MphasiSMphasiS 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, and Business Process Outsourcing help organizations adapt to changing market conditions and derive maximum value from IT investments. For more information about MphasiS, log on to www.mphasis.com

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mphasi s agil_analytics_life_cycle_business_style_for_big_data_services[1]

Data & Analytics