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Business Forecasting & Analytics ForumSeptember 18-19, 2017  •  Chicago, IL

Executing & ImplementingAnalytics for Success

September 19, 11:00 am

View presentation online at: https://jpkgroupsummits.com/attendee1/

Presenter: “Sammy” Amirghodsi – Options Clearing Corporation

Lessons learned in rolling out organization analytics and intelligence in an organization

I am a Senior Enterprise Technology Executive delivering the strategic planning, technology leadership, and innovative platforms that create revolutionary insights with Big Data, substantial cost savings, and improve productivity. I harness technology to

create end-to-end massive change. Key advisor to executive management and architect beyond multi-million dollar projects, I deliver the insight and actions that shape and

grow organizations. I break down complexity and technological barriers into language that leaders can unite behind. Leveraging my expertise and business acumen, I court buy-in on a technology roadmap that is perfectly aligned to corporate goals and long-

term goals. Having worked across many departments and industries from financial services, healthcare, eCommerce, and consumer product development, I develop the

strategic relationships built on value and integrity that get everyone on board with complex projects.

Siamak Amirghodsi (Sammy)Data Intelligence and Analytic Summit – Chicago

Executing&ImplementingAnalyticsforSuccessstoriesandlessons’learnedinrollingoutorganizationanalyticsandintelligenceinanorganization

Agenda

• Introduction• SuccessfulDataAnalyticsrequirestherightfoundations• DeliveringSuccessfulAnalyticsacrosscomplexenterprises• TeamsandCultures• Takeawayandclosing

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Siamak Amirghodsi (Sammy) :

Vice President - Head of Data & Analytics

Financial Industry

• Over 20+ years of designing, building, managing and executing large scale distributed systems in Fortune 20 companies. Lead the build out of a cutting edge FX payments data platform and analytics solution for a tier-1 investment bank in the US. Currently leading the data platform and real-time analytics build out for a tier-1 exchange related institution in United States.

• Certified on Cloudera Big Data Platform (Developer, Admin and HBase).

• Actively follow Hadoop (MapReduce, HDFS, YARN), Spark (Streaming / SQL / MLlib / GraphX / SparkR), Hive, Pig, Zookeeper, Amazon AWS, Cassandra, HBase, Neo4j, BlockChain, KDB+, RedShift and MongoDB while being fully grounded in traditional IBM/Oracle/Microsoft technology stacks for business continuity and integration.

• Key interests include cognitive models, big data, Hadoop, Spark, streaming systems, deep machine learning, google brain project, swarm algorithms, quantum computing, trading signal discovery, long term commodity cycles, cryptography, digital / crypto currencies, BlockChain, probabilistic graphical models and NLP.

• Contact Info:

• https://www.linkedin.com/in/siamakamirghodsi

Profile

OCC’s Role - Issues and guarantees U.S. Listed contracts - Provides clearing and settlement services- Provides a risk management to ensure marketplace is not

disrupted in the event of a clearing member default

- Options listed on more than 3,600 stocks and more than 600 indices and ETFs

- Cleared more than 4.2 billion contracts in 2015 - Year-to-date average daily options volume :16.4 M- Highest volume trading day on 8/8/11: 41.5 million

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IntroductionHowdidwegethere?

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A Paradigm Shift– Democratization of Data & Rise of Data Scientist

DataCommitteeYes we have the data, but NO You can not have the data!!

Data is free to roam and available on demand – within reason.

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ValueCreationDataPipelineManagingbothtraditionalandmoderndatapipelinesinlargeenterprises

7Source:http://www.toadworld.com/platforms/oracle/w/wiki/11576.modern-data-pipeline-architectures

Data&Analyticrolesarebeingblurred

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• Strategic• CDO,CAA,CTO,CMO,CFO• EA

• Execution• BigDataAnalytic

• Architect• Developer• Engineering• DataWarehouseRoles

• ETL• DataMining• BI

• TraditionalBIteam• DataWarehouseRoles

• ETL• DataMining• BI

• DataTeams• DataScientist• DataEngineer• DataArchitects• DataArtisan• DataManagement

• Enterprise• Regulatory&Compliance• Communication

Understand

Collect

Explore

Clean&Transform

Model

Validate

Socialize

Deploy

Predict

LifeCycle

Scalable Analytics Platform - Big Data

• Low Latency• High Throughput• Data Source

Volume – Data at restVelocity – Data in motionVariety – Any formatVeracity – Uncertainty

• Development & Deployment • Integration• Dynamic Models• Time Series• Data Visualization• Actionable Recommendations• Deep Learning• Data Governance & Security

Data at restData in motion

TypicalHighVelocityBigDataAnalyticsPlatform

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Are we asking the right questions?

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DigitalEconomymanifestitselfasAPIeconomy

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CustomerBehavior

Technology

Enterprise

DeepPersonalization

SocialMedia&Mobile

NativelyDigital

AdvanceAnalytics

CognitiveSystems

DistributedCloudComputing

Regulatorylandscape

Millennialworkforce

DigitalDisruption

APIEconomy

SuccessfulDataAnalyticRequirestherightfoundation

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Program Management of a Big Data Analytics Platform at Scale – Before & After …….

OfCourse – WecandoBigDataanalytics!…themorethebetter

……

Oh!No!BuildingBigDataAnalyticsisHard…thelessthebetter……

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Whatisasuccessfulanalyticprogramabout?

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• Allaboutdata….• AllaboutModels….• Allaboutlookandfeel….• Allaboutpeople….• AllAbouttechnology….• Allaboutgovernance….• AllaboutAgility….• Allaboutengagement….• Allaboutreal-timeanalytics….• Allaboutengagement….• Allaboutyourcustomers….• Allaboutprojectmanagement….• Allaboutgovernanceandqualityofdata....

Source:https://davidparker9.wordpress.com/category/reflection/

Analytics– Amirrorintocorporatedatastrategy&painpoints

15Source:http://dilbert.com

FivePillarofSuccess

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DataStrategy

AnalyticStrategy

Team

s&Culture

Governance

Inno

vatio

n

SuccessfulAnalyticProgramExecutionandAdaption

BuildingAnalyticStrategyforthefirm

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Business

Outcome

Analyticstrategy

DataStrategy

Technology

EnterpriseFunctions

LOB LOB LOB LOB

• Roadmap&Plantoidentifywhatdecisionyouaregoingtoassistwith

• Howareyougoingtoshareandcollaborate

• Howisthecompanydataisaccessedandtransformedandultimatelypresentedattheend-points

• Howareyoumanagingthecontentsandmodels

• Whatclassoftechnologiestouse

• Whataretherelevantregulatoryissues

• Governanceandauditstrategy

Identify&

Describe

Provision&

Share

Model&store

Integrate&

Move

Govern&Manage Steps

Thechampionsofsuccessfulanalyticorganization

18Source:https://www2.deloitte.com/content/dam/Deloitte/uy/Documents/strategy/gx-fsi-evolving-role-of-chief-data-officer.pdf

DeliveringSuccessfulAnalyticsprogramsacrosscomplexenterprises

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FintechdoesitwithInnovation&Culture

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§ Brand§ Capital§ Marketing§ Customer§ History§ Management§ Legacytechnology§ Integration

§ Innovation§ Culture§ Sheerwill§ CoolTechnology

Theapproach– excellenceindataanalytics…

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MonetizationRoadmap

GovernanceRegulatory

SingleSourceOfTRUTH

DataInfrastructure

BuildDataAnalytictalentpipeline

Buildrelationwithyourcustomersbothinternal&external

MinimumViableProduct(MVP)isakey…

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GovernedData

ReliablysourcedData

Datafoundations

Dothis…

Notthis…

AnalyticMVPEngagementstartshere!!!

MVPinaction

HighQuality

LowQuality

SuccessfulAnalyticProductrequireaenterprisewideawareness.

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Analyticsproductinitiation

o ExecutiveBuy-Inandsponsorshipo Dataownershipandboundarieso Complianceo Dataqualitymetricso Knowyourusersatcore

o Reportso Dashboard

o Relevantstandards

Analyticsproduct execution

o Highvaluehighpriorityfirsto Don’tunderestimatedataintegrationo Currenttechnologycapabilitieso Complianceissueso Deploymentstrategymatterso Speedmattersforearlyiterationso Understandthedatastrategyo Iterate&communicatewithusers

Onespeeddoesnotfitall…

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UltraFast(DriveBy)1-2week

Fast2-3weeks

Medium1-4Month

Slow6-12+Month

High

Low

EnterpriseView

TechnicalDebt

Quality

SingleView

Wrangling

Integration

Low

High

TechnicalD

ebt

Integration

Speed

High

Low

Sharedserviceschangesrequirescontinuousengagementwithbusiness

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SharedServices

Project(1),Lob(1)

Project(2),Lob(1)

Project(3),Lob(2)

Project(n),LOB(n)

…

Engage&Harmon

ize

Engage&Harmon

ize

……

Group7Group3

Group5

Group4Group6

Group2

Group1

VerticalUnitsStreamlinedService

Outcome

TheoutcomeofsuccessfulAnalytics

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Technology

People

Process

Ingridients

AnalyticValueCreationPipeline

CostReduction

Regulatory

LowerWorkingCapital

RiskReduction

Measurable

Innovation

BusinessOutcomes

Teams&Culturematters

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FastDataAnalyticRequiresFastCulture

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• TalentRoadmap• Contractorvsemployeemix• Strengthofthebench• Multi-generationworkforceisareality

• Globalteams• Rotation• Connectivity• Culture

DataTeams– SuccessfulInternshipprogramsforthelongrun

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• Smart&ImageDriven• Appealtotheirneedforself-expression• Agilitycanbeingrainedearly• Happinessmattersforretention• JobTitle– beingcoolmatters• Bootcampvs.freestyle• Connectivity• SocialResponsibility• Invisibleattractors

• Pets• Lookandfeel• Appearanceoffreedom• Individualism

Collaborate,connect,co-create,andcontrol,mostlywiththeirpeers

DataTeam- Deployment

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• Stand-Alone• Embedded• Intrinsic

Siamak(Sammy)AmirghodsiPostIsdatascienceaonedimentional technologyproblem?

https://www.linkedin.com/in/siamakamirghodsi/

Basedonavisitwith:• DanielTunkelan• HilaryMason&FFlabsl

Program Management of a Big Data Analytics Platform at Scale

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Sources:

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https://smallbiztrends.com/2011/11/what-millennial-employees-want.htmlhttps://www.theodysseyonline.com/millennial-buzz-word-quithttp://cordial.io/blog/marketing-to-millennials/

http://www.toadworld.com/platforms/oracle/w/wiki/11576.modern-data-pipeline-architecturesDanielTunkelanhttps://www.slideshare.net/dtunkelang/where-should-you-put-your-data-scientists?qid=3cec9b26-6e69-4a18-9040-0a05b983933c&v=&b=&from_search=32

https://www.walkaboutflorence.com/articles/meet-medici

Champions:https://www2.deloitte.com/content/dam/Deloitte/uy/Documents/strategy/gx-fsi-evolving-role-of-chief-data-officer.pdf

https://davidparker9.wordpress.com/category/reflection/

Thank You

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