SlideShare a Scribd company logo
1 of 1
My Initiatives at EWS
Data Analytics / Big Data Eco System (October2015 - Ongoing)
ExtendedEWS’backgroundinData AnalyticsandBI/DWby introducingBigData AnalyticsEcosystem.
Raisedawarenessandinterestthroughintracompanyseminarsinvarious typesof analyticsincluding
Descriptive, Exploratory,Inferential,Predictive,Causal,andMechanisticanalytics. Reviewed 4V’s,
Volume,Variety,Velocity,andVeracityof BigDataand the significance of suchanalysisingenerating
BusinessValue. Tappedinternalandhiredexternal talentwithskillsinMathematics,Statistics,
Economics,andBusinessinadditiontoComputerScience. AddedtoolsandtechnologiestoBigData
ToolkitwithHadoop,MapReduce,andR forAnalyticsatthe topof the stack. Setuptrainingaccessto
teamsfor a multitude of toolsincludingApache Spark,Scala,Python,HBase,Oracle NoSQL,Apache
Hive/Pig/Ambari,Tableau,etc. This,byfar,is the mostextensiveundertakingatEWSsimplydue tothe
sheersize andmagnitude of requirementsincludinghighlyspecializedskillsets,diverse areasof
analytics,anda wide array of relevanttoolsandtechnologies. The Ecosystem;however,hasbeensetup
to evolve andadaptto markettrends.
MobilityServices- Native,Hybrid, HTML5, RMAD, AWSMobile (October2013 - Ongoing)
IntroducedMobilityServicesinitiative forfasterTime-to-Marketwhileexercisingandpromoting
attentiontodetail. Existingtalentfromwebandbackenddevelopmentwasleveragedforcross
platformmobile implementationsusingHTML5. Skillsof Native andCross-platformdeveloperswere
strengthenedthroughformal trainingsessions. Low-code andNo-codetools,suchasAlphaAnywhere,
for RapidMobile ApplicationDevelopment(RMAD) were reviewedandratedforpossible use. AWS
Mobile wasintroducedtoleverage cloudbaseddevelopment,testing,deployment,andmonitoring. All
processes,trainingmaterial,toolsandtechnologiesweredocumentedforcontinuousimprovement.
EWS MobilityServiceswereincubatedinside Agile andContinuousDeliveryModel fromthe getgo.
Agile / CI / CD Initiative (October2012 - Ongoing)
Introducedinitiativetobringthe entire organizationintothe foldof TestDrivenAgile Developmentwith
ContinuousIntegrationandDelivery. Strategywasformulatedtoimplementchange andpromote
acceptance. Trainingsessionswere conductedstartingwithpilotteamstoavoidthe shockgoing
throughthistransformationall atonce. Once the propertrainingandtoolswere inplace and the
cultural impactwas accountedfor,the processof transitioningadditional teamswasinitiated. All
proceduresandtrainingmaterialswere documentedunderaversioncontrol documentrepository.
Procedureswere putinplace forswifton-boardingof new teammembers. Trainthe trainersessions
were conductedtomitigate risksassociatedwiththe turnovers. Overtime,the entire processwasmade
self-sustainablebyintroductionof continuousimprovementtechniquesincludingadditionof new tools
and technologies.

More Related Content

What's hot

AllanKote-Resume-2016
AllanKote-Resume-2016AllanKote-Resume-2016
AllanKote-Resume-2016
Allan Kote
 
Haddop in Business Intelligence
Haddop in Business IntelligenceHaddop in Business Intelligence
Haddop in Business Intelligence
HGanesh
 
Gartner peer forum sept 2011 orbitz
Gartner peer forum sept 2011   orbitzGartner peer forum sept 2011   orbitz
Gartner peer forum sept 2011 orbitz
Raghu Kashyap
 
Key desire in the new assignment
Key desire in the new assignmentKey desire in the new assignment
Key desire in the new assignment
Prabhakar Sastri
 

What's hot (12)

AllanKote-Resume-2016
AllanKote-Resume-2016AllanKote-Resume-2016
AllanKote-Resume-2016
 
Data science life cycle
Data science life cycleData science life cycle
Data science life cycle
 
Earley Executive Roundtable Summary - Data Analytics
Earley Executive Roundtable Summary - Data AnalyticsEarley Executive Roundtable Summary - Data Analytics
Earley Executive Roundtable Summary - Data Analytics
 
Big Data Analytics and Artifical Intelligence
Big Data Analytics and Artifical IntelligenceBig Data Analytics and Artifical Intelligence
Big Data Analytics and Artifical Intelligence
 
ILRI Research Methods Group (RMG): Unified research computing platform
ILRI Research Methods Group (RMG): Unified research computing platformILRI Research Methods Group (RMG): Unified research computing platform
ILRI Research Methods Group (RMG): Unified research computing platform
 
Data scientist versus big data
Data scientist versus big dataData scientist versus big data
Data scientist versus big data
 
What is big data
What is big data What is big data
What is big data
 
Oracle big data publix sector 1
Oracle big data publix sector 1Oracle big data publix sector 1
Oracle big data publix sector 1
 
Haddop in Business Intelligence
Haddop in Business IntelligenceHaddop in Business Intelligence
Haddop in Business Intelligence
 
Gartner peer forum sept 2011 orbitz
Gartner peer forum sept 2011   orbitzGartner peer forum sept 2011   orbitz
Gartner peer forum sept 2011 orbitz
 
Key desire in the new assignment
Key desire in the new assignmentKey desire in the new assignment
Key desire in the new assignment
 
Alteryx and Tableau: Iron Mountain's Sherpa to business insight
Alteryx and Tableau: Iron Mountain's Sherpa to business insightAlteryx and Tableau: Iron Mountain's Sherpa to business insight
Alteryx and Tableau: Iron Mountain's Sherpa to business insight
 

Similar to Director IT

JohnParedesResumeLinkedin
JohnParedesResumeLinkedinJohnParedesResumeLinkedin
JohnParedesResumeLinkedin
John Paredes
 
Keyrus US Information
Keyrus US InformationKeyrus US Information
Keyrus US Information
Julian Tong
 
Memory Management in BigData: A Perpective View
Memory Management in BigData: A Perpective ViewMemory Management in BigData: A Perpective View
Memory Management in BigData: A Perpective View
ijtsrd
 

Similar to Director IT (20)

Data science Nagarajan and madhav.pptx
Data science Nagarajan and madhav.pptxData science Nagarajan and madhav.pptx
Data science Nagarajan and madhav.pptx
 
Efficient Data Labelling for Ocular Imaging
Efficient Data Labelling for Ocular ImagingEfficient Data Labelling for Ocular Imaging
Efficient Data Labelling for Ocular Imaging
 
The Study of the Large Scale Twitter on Machine Learning
The Study of the Large Scale Twitter on Machine LearningThe Study of the Large Scale Twitter on Machine Learning
The Study of the Large Scale Twitter on Machine Learning
 
JohnParedesResumeLinkedin
JohnParedesResumeLinkedinJohnParedesResumeLinkedin
JohnParedesResumeLinkedin
 
What is business analytics
What is business analyticsWhat is business analytics
What is business analytics
 
Keyrus US Information
Keyrus US InformationKeyrus US Information
Keyrus US Information
 
Keyrus US Information
Keyrus US InformationKeyrus US Information
Keyrus US Information
 
Memory Management in BigData: A Perpective View
Memory Management in BigData: A Perpective ViewMemory Management in BigData: A Perpective View
Memory Management in BigData: A Perpective View
 
IRJET- Business Intelligence using Hadoop
IRJET-  	  Business Intelligence using HadoopIRJET-  	  Business Intelligence using Hadoop
IRJET- Business Intelligence using Hadoop
 
Ibisa platform EN
Ibisa platform ENIbisa platform EN
Ibisa platform EN
 
How to Optimize Sales Analytics Using 10x the Data at 1/10th the Cost
How to Optimize Sales Analytics Using 10x the Data at 1/10th the CostHow to Optimize Sales Analytics Using 10x the Data at 1/10th the Cost
How to Optimize Sales Analytics Using 10x the Data at 1/10th the Cost
 
Surendra Resume
Surendra  ResumeSurendra  Resume
Surendra Resume
 
Steve Woolege Of Aster Data Gives Lightning Talk At BigDataCamp
Steve Woolege Of Aster Data Gives Lightning Talk At BigDataCampSteve Woolege Of Aster Data Gives Lightning Talk At BigDataCamp
Steve Woolege Of Aster Data Gives Lightning Talk At BigDataCamp
 
Agile Testing Days 2017 Intoducing AgileBI Sustainably - Excercises
Agile Testing Days 2017 Intoducing AgileBI Sustainably - ExcercisesAgile Testing Days 2017 Intoducing AgileBI Sustainably - Excercises
Agile Testing Days 2017 Intoducing AgileBI Sustainably - Excercises
 
Business analytics
Business analyticsBusiness analytics
Business analytics
 
Deeper Insights with Alteryx
Deeper Insights with AlteryxDeeper Insights with Alteryx
Deeper Insights with Alteryx
 
Top Big data Analytics tools: Emerging trends and Best practices
Top Big data Analytics tools: Emerging trends and Best practicesTop Big data Analytics tools: Emerging trends and Best practices
Top Big data Analytics tools: Emerging trends and Best practices
 
OVERVIEW OF DATA SCIENCE (3).pdf
OVERVIEW OF DATA SCIENCE (3).pdfOVERVIEW OF DATA SCIENCE (3).pdf
OVERVIEW OF DATA SCIENCE (3).pdf
 
BAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, SydneyBAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, Sydney
 
Steve gregory resume bi
Steve gregory resume biSteve gregory resume bi
Steve gregory resume bi
 

Director IT

  • 1. My Initiatives at EWS Data Analytics / Big Data Eco System (October2015 - Ongoing) ExtendedEWS’backgroundinData AnalyticsandBI/DWby introducingBigData AnalyticsEcosystem. Raisedawarenessandinterestthroughintracompanyseminarsinvarious typesof analyticsincluding Descriptive, Exploratory,Inferential,Predictive,Causal,andMechanisticanalytics. Reviewed 4V’s, Volume,Variety,Velocity,andVeracityof BigDataand the significance of suchanalysisingenerating BusinessValue. Tappedinternalandhiredexternal talentwithskillsinMathematics,Statistics, Economics,andBusinessinadditiontoComputerScience. AddedtoolsandtechnologiestoBigData ToolkitwithHadoop,MapReduce,andR forAnalyticsatthe topof the stack. Setuptrainingaccessto teamsfor a multitude of toolsincludingApache Spark,Scala,Python,HBase,Oracle NoSQL,Apache Hive/Pig/Ambari,Tableau,etc. This,byfar,is the mostextensiveundertakingatEWSsimplydue tothe sheersize andmagnitude of requirementsincludinghighlyspecializedskillsets,diverse areasof analytics,anda wide array of relevanttoolsandtechnologies. The Ecosystem;however,hasbeensetup to evolve andadaptto markettrends. MobilityServices- Native,Hybrid, HTML5, RMAD, AWSMobile (October2013 - Ongoing) IntroducedMobilityServicesinitiative forfasterTime-to-Marketwhileexercisingandpromoting attentiontodetail. Existingtalentfromwebandbackenddevelopmentwasleveragedforcross platformmobile implementationsusingHTML5. Skillsof Native andCross-platformdeveloperswere strengthenedthroughformal trainingsessions. Low-code andNo-codetools,suchasAlphaAnywhere, for RapidMobile ApplicationDevelopment(RMAD) were reviewedandratedforpossible use. AWS Mobile wasintroducedtoleverage cloudbaseddevelopment,testing,deployment,andmonitoring. All processes,trainingmaterial,toolsandtechnologiesweredocumentedforcontinuousimprovement. EWS MobilityServiceswereincubatedinside Agile andContinuousDeliveryModel fromthe getgo. Agile / CI / CD Initiative (October2012 - Ongoing) Introducedinitiativetobringthe entire organizationintothe foldof TestDrivenAgile Developmentwith ContinuousIntegrationandDelivery. Strategywasformulatedtoimplementchange andpromote acceptance. Trainingsessionswere conductedstartingwithpilotteamstoavoidthe shockgoing throughthistransformationall atonce. Once the propertrainingandtoolswere inplace and the cultural impactwas accountedfor,the processof transitioningadditional teamswasinitiated. All proceduresandtrainingmaterialswere documentedunderaversioncontrol documentrepository. Procedureswere putinplace forswifton-boardingof new teammembers. Trainthe trainersessions were conductedtomitigate risksassociatedwiththe turnovers. Overtime,the entire processwasmade self-sustainablebyintroductionof continuousimprovementtechniquesincludingadditionof new tools and technologies.