Building An Effective & Extensible Data & Analytics .
Cognizant 20-20 InsightsDigital BusinessBuilding an Effective & ExtensibleData & Analytics Operating ModelTo keep pace with ever-present business and technology change and challenges,organizations need operating models built with a strong data and analyticsfoundation. Here’s how your organization can build one incorporating a range ofkey components and best practices to quickly realize your business objectives.Executive SummaryTo succeed in today’s hypercompetitive global economy,organizations must embrace insight-driven decision-making.This enables them to quickly anticipate and enforce businesschange with constant and effective innovation that swiftlyincorporates technological advances where appropriate. Thepivot to digital, consumer-minded new regulations arounddata privacy and the compelling need for greater levels ofdata quality together are forcing organizations to enactbetter controls over how data is created, transformed, storedand consumed across the extended enterprise.Chief data/analytics officers who are directly responsible for thesanctity and security of enterprise data are struggling to bridgeAugust 2018the gap between their data strategies, day-to-day operationsand core processes. This is where an operating model can help.It provides a common view/definition of how an organizationshould operate to convert its business strategy to operationaldesign. While some mature organizations in heavily regulatedsectors (e.g., financial services), and fast-paced sectors (e.g.,retail) are tweaking their existing operating models, youngerorganizations are creating operating models with data andanalytics as the backbone to meet their business objectives.This white paper provides a framework along with a set ofmust-have components for building a data and analyticsoperating model (or customizing an existing model).
Cognizant 20-20 InsightsThe starting point: MethodologyEach organization is unique, with its ownspecific data and analytics needs. Different sets ofcapabilities are often required to fill these needs. Forthis reason, creating an operating model blueprint isan art, and is no trivial matter. The following systematicapproach to building it will ensure the final productworks optimally for your organization.Building the operating model is a three-step processstarting with the business model (focus on data)followed by operating model design and thenarchitecture. However, there is a precursory step,called “the pivots,” to capture the current state andextract data points from the business model prior todesigning the data and analytics operating model.Understanding key elements that can influence theoverall operating model is therefore an importantconsideration from the get-go (as Figure 1 illustrates).The operating model design focuses on integrationand standardization, while the operating modelarchitecture provides a detailed but still abstract viewof organizing logic for business, data and technology.In simple terms, this pertains to the crystallization of thedesign approach for various components, includingthe interaction model and process optimization.Preliminary step: The pivotsNo two organizations are identical, and theoperating model can differ based on a numberof parameters — or pivots — that influence theoperating model design. These parameters fall intothree broad buckets: Design principles: These set the foundationfor target state definition, operation andimplementation. Creating a data visionstatement, therefore, will have a direct impacton the model’s design principles. Keep in mind,effective design principles will leverage allexisting organizational capabilities and resourcesto the extent possible. In addition, they will bereusable despite disruptive technologies andindustrial advancements. So these principlesshould not contain any generic statements, like“enable better visualization,” that are difficult tomeasure or so particular to your organizationthat operating-model evaluation is contingentupon them. The principles can address areassuch as efficiency, cost, satisfaction, governance,technology, performance metrics, etc.Sequence of operating model developmentPreliminaryStep 1BusinessmodelThe pivots(current state)Step 2OperatingmodelStep 3Operating modelarchitectureFigure 12 /Building an Effective & Extensible Data & Analytics Operating Model
Cognizant 20-20 Insights Current state: Gauging the maturity of data andrelated components — which is vital to designingthe right model — demands a two-prongedapproach: top down and bottom up. The reason?Findings will reveal key levers that require attentionand a round of prioritization, which in turn canmove decision-makers to see if intermediateoperating models (IOMs) are required. Influencers: Influencers fall into three broadcategories: internal, external and support.Current-state assessment captures thesedetails, requiring team leaders to be cognizant ofthese parameters prior to the operating-modeldesign (see Figure 2). The “internal” categorycaptures detail at the organization level.”External” highlights the organization’s focusand factors that can affect the organization. And“support factor” provides insights into how muchcomplexity and effort will be required by thetransformation exercise.Operating model influencersInternalExternalSupport factor Data & analytics vision Value proposition Geography, spread& culture Customer/businesssegment & communicationchannelsChange impact index(employee) Technology landscape Competition (monopolyvs. oligopoly)Revenue & headcount Regulatory influenceManagement commitmentand funding Organization setup(flat vs. consensus;product-driven vs.function-driven)Position in value chainFigure 23/Building an Effective & Extensible Data & Analytics Operating Model
Cognizant 20-20 InsightsFirst step: Business modelA business model describes how an enterpriseleverages its products/services to deliver value,as well as generate revenue and profit. Unlike acorporate business model, however, the objectivehere is to identify all core processes that generatedata. In addition, the business model needs tocapture all details from a data lens — anything thatgenerates or touches data across the entire datavalue chain (see Figure 3).We recommend that organizations leverage oneor more of the popular strategy frameworks, suchas the Business Model Canvas1 or the OperatingModel Canvas,2 to convert the informationgathered as part of the pivots into a business model.Other frameworks that add value are Porter’s ValueChain3 and McKinsey’s 7S framework.4 The outputof this step is not a literal model but a collection ofdata points from the corporate business model andcurrent state required to build the operating model.Second step: Operating modelThe operating model is an extension of the businessmodel. It addresses how people, processand technology elements are integrated andstandardized. Integration: This is the most difficult part, as itconnects various business units including thirdparties. The integration of data is primarily atthe process level (both between and acrossprocesses) to enable end-to-end transactionprocessing and a 360-degree view of thecustomer. The objective is to identify the coreprocesses and determine the level/type ofThe data value chainBusiness valueData acquisition/Data generationDataprovisioningData preparation/data synthesisEnterprise data managementData ingestion/data integrationData modeling/reportingEnterprise data analyticsFigure 34 /Analytics &VisualizationBuilding an Effective & Extensible Data & Analytics Operating Model
Cognizant 20-20 InsightsIntegration & standardizationxData map: field servicesLocationxEmployeeInstallation servicesBilling omerProductBusiness process/data domain mappingTransmission & distributionField servicesxxxxxxxxxxServicesCustomer interactionxxBilling & payment processingxxCustomer administrationXxService request managementxxThird-party settlementReceivables managementxxxxxxxxxxxxXxxxxxxData standardization requirementsCustomer attribute level standardization rules & process level standardization requirements Product attribute level standardization rules & process level standardization requirements Equipment attribute level standardization rules & process level standardization requirements Order attribute level standardization rules & process level standardization requirements Work management attribute level standardization rules & process level standardization requirements Billing & invoice attribute level standardization rules & process level standardization requirements Employee attribute level standardization rules & process level standardization requirements Location attribute level standardization rules & process level standardization requirements Cross-functional process mapFigure 4integration required for end-to-end functioningto enable increased efficiency, coordination,transparency and agility (see Figure 4).A good starting point is to create a cross-functionalprocess map, enterprise bus matrix, activitybased map or competency map to understandthe complexity of core processes and data. In ourexperience, tight integration between processes andfunctions can enable various functionalities like selfservice, process automation, data consolidation, etc. Standardization: During process execution,data is being generated. Standardization ensuresthe data is consistent (e.g., format), no matterwhere (the system), who (the trigger), what5/(the process) or how (data generation process)within the enterprise. Determine what elementsin each process need standardization and theextent required. Higher levels of standardizationcan lead to higher costs and lower flexibility, sostriking a balance is key.Creating a reference data & analyticsoperating modelThe reference operating model (see Figure 5) iscustomizable, but will remain largely intact at thislevel. As the nine components are detailed, themodel will change substantially. It is common to seethree to four iterations before the model is elaborateenough for execution.Building an Effective & Extensible Data & Analytics Operating Model
Cognizant 20-20 InsightsFor anyone looking to design a data and analyticsoperating model, Figure 5 is an excellent startingpoint as it has all the key components and areas.objects and relations over time, such tools will helpgreatly in maintaining the operating model.The objective is to blend process and technologyto achieve the end objective. This means usingdocumentation of operational processes aligned toindustry best practices like Six Sigma, ITIL, CMM, etc.for functional areas. At this stage it is also necessaryto define the optimal staffing model with the rightskill sets. In addition, we take a closer look at what theorganization has and what it needs, always keepingvalue and efficiency as the primary goal. Striking theright balance is key as it can become expensive toattain even a small return on investment.Final step: Operating modelarchitectureDiverse stakeholders often require different viewsof the operating model for different reasons. Asthere is no one “correct” view of the operatingmodel, organizations may need to create variantsto fulfill everyone’s needs. A good example iscomparing what a CEO will look for (e.g., strategicinsights) versus what a CIO or COO would lookfor (e.g., an operating model architecture). Toaccommodate these variations, modeling tools like5Archimate will help create those different viewsquickly. Since the architecture can include manyEach of the core components in Figure 5 needsto be detailed at this point, in the form of achecklist, template, process, RACIF, performanceReference data & analytics operating model (Level 1)Business units / functionsCustomer / employeeThird party (vendor/supplier)172ManagesupportRegulatory complianceIndustry value chainManage process8A. Manage demand/requirements b. Manage channelsManagechange3 Manage data3a. Data acquisition/data generation3b. Data provisioning/data storage3c. Data preparation/data synthesis3d. Data ingestion/data integration3e. Data modeling/reporting3f. Data analytics &visualization4 Manage data services4a. Data management services4b. Data analytics servicesBusiness5 Manage project lifecyclePeopleProcess5a.Strategy,Plan & align5b.Analyze terativedevelopment5f.IterativeTesting5g.Rollout &transitionOrganizationItChannelDataTechnology6 Manage technology/platform9 Manage governanceFigure 56/Building an Effective & Extensible Data & Analytics Operating Model5h.Sustain &govern
Cognizant 20-20 Insightsmetrics, etc. as applicable. the detailing of threesubcomponents one level down. Subsequentlevels involve detailing each block in Figure 6 untiltask/activity level granularity is reached.The operating model componentsThe nine components shown in Figure 5 will bepresent in one form or another, regardless of theindustry or the organization of business units.Like any other operating model, the data andanalytics model also involves people, process andtechnology, but from a data lens. Component 1: Manage process: If anenterprise-level business operating model exists,this component would act as the connector/Component 1: Manage Process: If an enterpriselevel business operating model exists, thiscomponent would act as the connector/bridgebetween the data world and the business world.Every business unit has a set of core processesthat generate data through various channels.Operational efficiency and the enablementof capabilities depend on the end-to-endmanagement and control of these processes.For example, the quality of data and reportingcapability depends on the extent of couplingbetween the processes. Component 2: Manage demand/requirements & manage channel: Business units arenormally thirsty for insights and require differenttypes of data from time to time. Effectivelymanaging these demands through a formalprioritization process is mandatory to avoidduplication of effort, enable faster turnaroundand direct dollars to the right initiative.Sampling of subcomponents: An illustrative view2a. Manage demand/requirementsData checklist & templates6Manage technology/platformTechnology, application, platformBusiness/System requirementsBusiness case justificationInfrastructureCapacityRequirements traceability matrixRACI matrixTool/Vendor selection modelBackup & disaster recoveryBusiness IT alignmentWireframes/TemplatesStaffingData center operationsProcess/Data flowsMeta model representationApplication inventoryLicense & renewalsInfrastructureBackup & disaster recoverySupport requirementsSecurity & access managementRegulatory & complianceUsability requirementsEffort & ROI estimationBenefit/Value realizationValue proposition matrixImpact/Dependency analysis9Manage governanceData/Information governancePolicy, process & proceduresFramework & methodologyKPI/MetricsIssue managementRACI/CRUDDG organization modelStandards & controlsCharterFigure 67/Building an Effective & Extensible Data & Analytics Operating Model
Cognizant 20-20 Insights Component 3: Manage data: This componentmanages and controls the data generated bythe processes from cradle to grave. In otherwords, the processes, procedures, controls andstandards around data, required to source, store,synthesize, integrate, secure, model and report it.The complexity of this component depends onthe existing technology landscape and the threev’s of data: volume, velocity and variety. For a fairlycentralized or single stack setup with a limitednumber of complementary tools and technologyproliferation, this is straightforward. For manyorganizations, the people and process elementscan become costly and time-consuming to build.To enable certain advanced capabilities, thearchitect’s design and detail are major parts ofthis component. Each of the five subcomponentsrequires a good deal of due diligence insubsequent levels, especially to enable “as-aservice” and “self-service” capabilities. Component 4a: Data management services:Data management is a broad area, and eachsubcomponent is unique. Given exponentialdata growth and use cases around data, theability to independently trigger and manageeach of the subcomponents is vital. Hence,enabling each subcomponent as a service addsvalue. While detailing the subcomponents,architects get involved to ensure the processcan handle all types of data and scenarios. Eachof the subcomponents will have its set of policy,process, controls, frameworks, service catalogand technology components.Enablement of some of the capabilities as aservice and the extent to which it can operatedepends on the design of Component 3. It iscommon to see a few IOMs in place before thesubcomponents mature. Component 4b: Data analytics services:Deriving trustable insights from datacaptured across the organization is not easy.Every organization and business unit has itsrequirement and priority. Hence, there is noone-size-fits-all method. In addition, withadvanced analytics such as those built aroundmachine-learning (ML) algorithms, naturallanguage processing (NLP) and other forms ofartificial intelligence (AI), a standard model isnot possible. Prior to detailing this component,it is mandatory to understand clearly what thebusiness wants and how your team intends todeliver it. Broadly, the technology stack and datafoundation determine the delivery method andextent of as-a-service capabilities.Similar to Component 4a, IOMs help achieve theend goal in a controlled manner. The interactionmodel will focus more on how the analyticsteam will work with the business to find, analyzeand capture use cases/requirements from theindustry and business units. The decision on thesetup — centralized vs. federated — will influencethe design of subcomponents.Business units are normally thirsty for insights and require differenttypes of data from time to time. Effectively managing thesedemands through a formal prioritization process is mandatory toavoid duplication of effort, enable faster turnaround and directdollars to the right initiative.8/Building an Effective & Extensible Data & Analytics Operating Model
Cognizant 20-20 Insights Component 5: Manage project lifecycle: Theproject lifecycle component accommodatesprojects of Waterfall, Agile and/or hybridnature. Figure 5 depicts a standard projectlifecycle process. However, this is customizableor replaceable with your organization’s existingmodel. In all scenarios, the components requiredetailing from a data standpoint. Organizationsthat have an existing program managementoffice (PMO) can leverage what they alreadyhave (e.g., prioritization, checklist, etc.) andsupplement the remaining requirements.The interaction model design will help supportservicing of as-a-service and on-demand datarequests from the data and analytics side duringthe regular program/project lifecycle. Component 6: Manage technology/platform: This component, which addressesthe technology elements, includes IT servicessuch as shared services, security, privacy and9/risk, architecture, infrastructure, data center andapplications (web, mobile, on-premises).As in the previous component, it is crucial todetail the interaction model with respect to howIT should operate in order to support the as-aservice and/or self-service models. For example,this should include cadence for communicationbetween various teams within IT, handling of liveprojects, issues handling, etc. Component 7: Manage support: No matterhow well the operating model is designed, thehuman dimension plays a crucial role, too. Be itbusiness, IT or corporate function, individuals’buy-in and involvement can make or break theoperating model.The typical support groups involved in theoperating-model effort include BA team(business technology), PMO, architectureboard/group, change management/advisory,Building an Effective & Extensible Data & Analytics Operating Model
Cognizant 20-20 Insight
Building an Effective & Extensible Data & Analytics Operating Model To keep pace with ever-present business and technology change and challenges, organizations need operating models built with a strong data and analytics foundation. Here’s how your organization can build one incorporating a range of
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a convenient, inline visualization solution for data scientists, we created pytri,a Python package that enables visualization of substrate Layers without leaving a Jupyter notebook [18] or other IPython environment (Fig. 4). Jupyter is a standard research platform for many communities. By bringing composable, extensible visu-alization to this .
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