Leading a Data Team that Supports the Business into a Cohesive Data-driven UnitCanon Data Science & Analytics – An Overview

Apr – 2018

UsmanShahbazSeniorManagerDataScience&Analytics,Canon

Usmanhasmorethan13yearsofexperienceindatascience,datastrategyandadvanceddataanalytics.WorkingasSeniorManagerDataScience&AnalyticsatCanon,heisprogressivelyworkingtowards

developingfull-scaleanalyticalcapabilityacrossmultiplecustomerchannelsthuscreatingstrategicleverageforthebusiness.PriortojoiningCanon,Usmanhadledteamswithintelcoandretailindustriestoeffectivelydeliverondatastrategy,consumerinsights,datavisualisation andadvancedanalyticalmodelling.Usmanis

currentlyenrolledforaPhDinMachineLearning.HealsoholdsanMBAandaBachelor’sdegreeinElectricalEngineering.

Without Data You are just another person with an OpinionW. Edwards Deming “ ”

@rev16

About Canon – Our Journey in Oceania

Howitallbegan

Intheearly1930’s,inasmallroominTokyo,someyoungentrepreneurshadadream.TocreateJapan’sfirst-ever35mmcamerawithfocalplaneshutter.

Thesuccessofthatfirstprototypewasthebeginningofsomethingmore,therestishistory!

Canon Oceania Journey

Tobeadata-drivenorganizationwhere weanalyse andpredictconsumerbehaviour toarticulateorganisational strategyandoptimise ourconsumerinteractions.

LEVEL-1

Identifying Data Sources & Data

Islands

LEVEL-2

Bridging Data sources through

Rapid Prototyping

LEVEL-4

Diagnostic/Predictive Insights fuel

business hypothesis

“Data as a way of work”

Data centered decision making

LEVEL-3

Build Enterprise Data Model/ Production Algorithms

OurAmbition

StrategicPillars

DataRoadmap

DESCRIPTIVE

Gain insights from historical data with reporting & visual

dashboards.

DIAGNOSTIC

Build complex Data Models to evaluate

business hypothesis

PRESCRIPTIVE

Recommend decisions & course of action based

on the predictive algorithms

PREDICTIVE

Data Models able to predict patterns and behaviors through machine learning.

LEVEL-5

Prescriptive analytics drives key strategies

Data Strategy

….But before we move further

Hadoop is not the answer to everything…Not Yet…… Right Data Architecture is

….We need to talk about the Elephant in the room…..

AnalyticalDataProcessing&ModellingCenterofExcellence

TM1

AnalyticPresentation&EnterpriseDistribution

MicrosoftAX- ERP

CDSS– ERP

AD(HR)

MasterDataFiles

SalesForce SFDC

PDWArchivedData

MyCanon

SocialBakers&SocialNetworks

GoogleAnalytics

EnterpriseDWH/DimensionalModel

Cubes/TabularModels

PBIDesktop&PBIServiceportal

DataDiscovery

ReportsandDashboards

SSRSReportPacks

OperationalDataStore

MetricsandotherFacts

DimensionalAttributesandHierarchies

BusinessDataGlossary

ReportTaxonomy

MasterDataHub

DataGovernance/DataStewardship

DataLineageandProcessingControl

Microsoft Analytics Architecture

Microsoft Data Science R Server Architecture

Python Production code in SQL Server - 2017

Rapid Prototyping - Analytical Lifecycle

DecisionMakingonInsightsNewlyestablisheddatareportormodel

empowerbusinesswithactionableinsights.Businessevaluatesdecisionsbasedonthe

insights.

DataVis.OrMLProductionModel

BasedontheDWHschemaandbusinessrequirementsproductionmodelisshared

withthebusinessstakeholders..

ProcessAreaWorkshopRunworkshopswithbusinessstakeholderspertainingtheindividualprocessareas

BuildAnalyticalPrototypeCreateabusinessprototypeoftherequirementsandusethattovalidatewithstakeholders

ScaleatEnterpriseAnalyticalModel

OncevalidatedbythebusinesspasstheprototypeforascalableEnterprise

AnalyticalModel

Putting the Prototyping Structure to Work

RapidPrototyping&BusinesslogicDesignAcceleratedbusinesslogictest,sampledatatestandvalidatebusinessrequirement

SolutionIntegration/Optimisation&testing

Solutionintegrationwithrestoftheanalyticalpieces,comprehensivetesting&required

optimisation.

ProductionDeployment&Presentation

Basedonthesolution,productiondeploymentwithsecuritymatrix

appliedbuildsoutthepresentationlayerwithautomation.

MachineLearningExpert

Developsprimarydatatesting,buildsanddevelopstestingalgorithm.ValidatesBusiness

Hypothesis

SolutionDevelopmentArchitectscalabledevelopmentoftheprototype,DataEngineeringforODS/DWH

VisualisationExpertBuildsdatamodelsafterdatadiscoveryandbuildsrequiredbusinesslogicincloseconsultationwithBusiness

Owners

BusinessOwnerInitializestheprocessbysubmittingthebusinessrequirementandlogic.Evaluatesthefinalreportthathas

beenproduced

Journey that we have covered…. • Usingrapidprototypingwithbusinessunitstovalidatebusiness

hypothesis• Bridgedatasourcesandcreateprototypeswithconsolidatedview• Datagovernanceframeworkacrossgrouptofirmupprocedural

changes• ScalingthesolutionsinSQLserveraftertheprototypevalidation• PowerBIasthetooltoconsumeanalyticalcontentacross

organisation• Democratising notonlythedatabutalsotheabilitytodraw

powerfulinsights

Way Forward….//>>

• InsteadofthemaintainingthewebAPIinPowerBI,pushtheminSSASlayer

• Bettermanagementintermsofthesecuritymodelaswellnomenclaturedefinitionandmaintenance

• UsethepowerBIqueryfunctionalityforrapidprototypingbutSSASlayerforproductionlayer

• UsebaselevelRtestingthroughRvisualsinpowerBIinsteadoflocalmachineRlibraries

• Continuebuildingprototypesusinglocalmachinelibraries• ContinuescalingusingSQLStoredProceduresforproduction

versionofthealgorithms

Crawl

Walk

Run

What we could have done differently…• Insteadofconnectingeachflatfileconnectionin

PowerBI,weshouldhaveusedthefolderconnections

• InstalltwoinstancesofEnterpriseGatewayonefordevelopmentandanotherforproductionreleases

• UsethepowerBIqueryfunctionalityforrapidprototypingbutSSASlayerforproductionlayer

• UsebaselevelRtestingthroughRvisualsinpowerBIinsteadoflocalmachineRlibraries

• Buildingworkspacedoesnotallowwhopublishedwhat….withintheteam

Thank YouQuestions?