Digital Enterprise Architecture: Four Elements Critical To .

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Cognizant 20-20 InsightsDigital OperationsDigital EnterpriseArchitecture: Four ElementsCritical to SolutionEnvisioningToday’s digital organization demands an enterprise architecturethat is guided by its intended business outcome and which caninform strategy embracing a multidimensional approach coveringdigitization, data management, analytics, AI and automation.Here’s how to get started.Executive SummaryThe digital revolution continues to transformour world. Mobility is rapidly ascendingas sensor and actuator capabilities bringintelligence to devices of all varieties viaNovember 2019the aptly named Internet of Things (IoT).According to IDC, our digital world is now“doubling in size every two years, and by2020 the digital universe – the data we create

Cognizant 20-20 Insightsand copy annually – will reach 44 zettabytes,or 44 trillion gigabytes.”1 As data proliferatesexponentially, a new class of applications isemerging, one endowed with the intelligence toredefine the business, operating and technologymodels in place since the onset of the 21st century.Companies such as Google, Amazon, Facebook,LinkedIn and Uber are leading the way inmonetizing big data and disrupting marketsthrough data-driven strategies. In this regard,McKinsey2 has emphasized the importance ofan integrated approach to data sourcing, modelbuilding and organizational transformation.The pivot to digital depends on a suite oftechnology developments in the areas of sensors,actuators (for triggering actions), networking/integration and computing (data management,processing, analytics, etc.). This white paperoutlines the key architectural elements forundertaking an integrated design approach andthereby accelerating the journey to full-scaledigital. The foundational aspects of our approachare guided by TOGAF, 3 the industry-leadingenterprise architecture (EA) framework. Theproposed architecture elements focus on whatwe call the four M’s – materials, machines, modelsand mesh.4The overarching perspective of the digitalenterprise architectureThe business world is increasingly interconnectedvia constituent nodes of networked computers.These nodes pervade business, IT systems andapplications of varying size, individual devices andeven sensors.The digital architecture of a connected environmentforms the foundation of an information ecosystemthat provides business services to customers,business partners and employees. Such businessservices are composed of finer-grained constituentservices and data from other nodes – which maybe either within or outside the business unit’sboundaries. New services are formed by slicing,consolidating and repurposing informationcontained within the extended enterprise and then,The digital architecture of a connectedenvironment forms the foundation ofan information ecosystem that providesbusiness services to customers, businesspartners and employees.2/Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning

Cognizant 20-20 InsightsDigital vehicular traffic system: Illustrates connectedecosystem multiplying the digital enterprise sSupplierSubscriptionsFigure 1by applying analytical and processing intelligence,generating new services of interest.As a result, the functional logic, information used,system and technology involved often cut acrossapplications, application architectures and evenenterprise boundaries, which are transparent to theconsuming user. Therefore, a digital architectureblueprint is vital for successful implementationof a digital enterprise. However, service-levelarchitectural parameters such as uptimes andresponse times remain tied to services in a realtime service integration scenario, and need to befactored in when the architecture is defined.Digital architecture blueprint: Four focus areasENTERPRISE DIGITAL ARCHITECTUREBusinessArchitectureFocus on businessoutcome-drivenmodels and hitectureFocus on identifyingand creating a mesh ofdata and managing thesame.Focus on buildingintelligent machines,embodying the logic towork upon the data todo, learn, think.Technology ArchitectureFocus on the material aspectof the intelligent machinescovering the hardwareand technology choicesof implementation (e.g., AIsoftware).Figure 23/Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning

Enterprises need to be adept in digitizing, analyzing data andautomating smart actions to attain an edge over rivals and reapenhanced operational efficiencies in the hyper-competitive globaleconomy. This demands new business solutions and approaches.Building a business architectureIn our digital age, organizations need new modelsand services to generate greater business value.For this, they need to use the wealth of digital datathat surrounds their organizations – from theirpeople, processes, devices and consumer input.We call these Code Halos,5 unique virtual identitiesproduced by every digital click, swipe, like, buy,comment and search. Enterprises need to beadept in digitizing, analyzing data and automatingsmart actions to attain an edge over rivals and reapenhanced operational efficiencies in the hypercompetitive global economy. This demands newbusiness solutions and approaches.In our book What to Do When Machines DoEverything, the authors from our Center for theFuture of Work outline a data-driven approach (seeFigure 3) for building intelligent machines neededto thrive in the digital economy.Intelligent process automation (IPA) appliesthe machine intelligence (MI) baked into thealgorithms that power today’s software for creatingsophisticated business processes. IPA applicationcan be found in clinical data management for lifesciences, claims adjudication for insurers, loanapplications in banking, logistics optimization,and traditional technology processes such asinfrastructure and information managementservices. Sites recommending items based onprevious purchases are using MI to analyze users’buying patterns and promote other items of likelyinterest to the customer.4/Getting AHEAD with businessarchitectureA utomationHFocus on unleashing the powerof the digital workforce comprised ofsoftware robots (bots) and virtual personal assistants (VPAs). alosEAFocus on the true power of personalized services by cultivating information that surrounds people, theorganization and devices. nhancementFocus on enhancing end-users’perceptions of system aspects suchas utility, ease of use and efficiency. bundanceDFocus on vast new markets bydropping the price point of existingoffers and finding new energysources with the help of machines. iscoveryFocus on conceiving new products,new services and new industries byleveraging machine intelligence.Figure 3Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning

In fact, digital advancement is progressing muchfaster than what many industry watchers expected.Machines can now read, see, listen, write and actuate.Using these core capabilities, machines can nowperform complex tasks and learn new things.Enterprise digital architecture helps ITorganizations to realize diverse business usecases such as the following: Enhancing public safety and optimizing utilitiesthrough sensor data analysis. Assessing patients’ health in real time byanalyzing patient data from wearable devicesand sensors. Identifying fraud and money laundering viabanking cyber-surveillance. Route planning based on crowd-sourced GPSdata. Discovering new energy sources in the oil andgas industry, and streamlining oil distribution.With the advancement of smart meters, deliveryoptimization and algorithmic trading, today’snew machines are progressively leading to thenext generation of inventions. So the underlyingarchitecture needs to provide an extensiblefoundation to support such evolution.Designing an application architectureIn fact, digital advancement is progressing muchfaster than what many industry watchers expected.Machines can now read, see, listen, write andactuate. Using these core capabilities, machinescan now perform complex tasks and learn newthings. Sooner or later, machines will have thecapacity to imitate the other human senses: touch,smell and taste. And with time they will be able towork smarter.The challenge, therefore, for an enterpriseapplication architect is building intelligent systemsthat can fulfill business goals which deliver resultsthat exceed competitors’ efforts and createadditional value for the customer. The architecturemust leverage the core capabilities available in somedistributed models and enable tighter human-5/machine collaboration than ever before.This calls for a focused, methodical approach foridentifying how machines can advance automation,productivity and discovery by creating andanalyzing Code Halos generated through theabundance of data that permeates business. Aretail organization, for example, can leverage fourkey areas under Code Halos where machines canbe a differentiating factor: identification of marketsegmentation, sentiment analysis, campaignmanagement and recommendation engines.Similarly, there are four areas of enhancement:predictive maintenance, predictive planning,cognitive monitoring and fraud detection. (How todo it is a topic of separate deliberation.)Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning

Cognizant 20-20 InsightsDesigning a modern digital applications anceDiscoveryThinkApplication CapabilityFocus on building solutions and frameworks that can perform, learn and discover by themselves andfulfill business objectives identified under the bucket of automation, Code Halos, enhancement,abundance and discovery.Analytical CapabilityFocus on prediction, reporting, planning, optimization and collaboration algorithms and models tobuild an end-to-end analytical layer.Sensory & Actuation CapabilityFocus on creating core capabilities that imitate human senses such as reading, watching and listening todigitize the enterprise and generate data required to inform machine algorithms. Also focus on triggering actions like toggling lights (e.g., of signals), physical movements (e.g., a motor), etc.Figure 4Along with identifying these areas, commonanalytical practices (such as forecasting, optimizingand planning), are required to build machinesin each respective area. Figure 4 illustrates oneapproach. This design customizes machine learningalgorithms and models for common API realization.As organizations shift to digital, forecasting,optimizing and planning activities shift to real-timeprocesses and become part of a continuous cyclerather than the periodic events of yesteryear.Architects need to identify the sources from which they can acquirethe relevant data to model the enterprise as realistically as possible.Along with identifying the sources, they must identify or buildinterfaces that can capture an information object with its attributes.6/Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning

Cognizant 20-20 InsightsIntuiting information architectureInformation is digital’s key substrate, the baseupon which application capabilities and analyticalcapabilities thrive.The challenge to enhanced digitization of thephysical business world starts with generating amodel that properly simulates the business. Manyorganizations have begun to do this with theirIoT deployments and related digital initiatives.Architects need to identify the sources from whichthey can acquire the relevant data to model theenterprise as realistically as possible. Along withidentifying the sources, they must identify or buildinterfaces that can capture an information objectwith its attributes. Thus far, organizations have beenfocusing primarily on objects and their attributes,as success for a digital solution lies inthe ability to capture the holistic behavior of anobject. Moreover, with the proliferation of data,the challenge becomes one of petadatamanagement – how to acquire, store and organizevast quantities of quality data. When there isavailability of data aplenty, categorizing theinformation is key to better management.Finally, siloed data cannot reveal the full story.The dots need to be connected to complete thenarrative. Hence, an integrated view of informationmust be built by correlating the objects andbuilding a comprehensive enterprise informationmodel and associated interfaces. (A detailedapproach to achieve this is a discussion topic forthe future.)Eyeing the right information architectureIntegrated viewFocus on connecting information to build a real-world model that makes the informationlivelier so that it all tells a better story.Data typeFocus on classifying data, whether it is structured, unstructured, audio, image, video, document or sensor data coming from RDBMS, No-SQL, HDFS, CMS, etc., so that it can be bettermanaged when acquiring, storing, organizing and analyzing high volumes of data.InterfaceFocus on data from a diverse set of interfaces (e.g., sensors, telemetry, wearables, audio,video, chat, e-mail) that not only capture attributes of a specific object but also its associatedfunctionalities.SourceFocus on sourcing data from enterprise systems, social media, mobile platforms, sensornetworks, print media, etc. so that anything and everything associated with the organizationcan be digitized.Figure 57/Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning

Cognizant 20-20 InsightsEnvisioning the technology architectureCompanies need to keep digging for digitalfuel (i.e., mining data of all types and formats) tocontinually grow their business intelligence. Specialtechnologies and materials are required to discover,acquire, organize and analyze big data. Traditionaltechnologies, such as CPU-based computing andrelational databases, fall short in managing thevolume, velocity and variety of data.At the same time, technologies that power MI areproliferating. A disciplined approach to finding andapplying the most appropriate tools is critical. Inthis regard, an effective technology architecturecan ensure that this critical element is in place atthe right time in tandem with addressing the keyhuman and organizational issues involved in acultural change. Figure 6 summarizes key technicalareas and their characteristics.IoT devices and platforms are crucial for dataacquisition and real-time ingestion of differenttypes of data. Once acquired, various data storagetechnologies (i.e., RDBMS, No-SQL, CMS, HDFS),along with data warehouse tools, help to organizethe data through transformation, normalization,encoding, generating training sets, etc. Theprocessed data is fed to analysis tools with built-inalgorithms such as clustering, learning, etc., andmodels such as predictive, optimization, planning,etc. Finally, an enterprise should appropriatelyexperiment with emerging industry-specificbusiness solutions6 or MI platforms.Focusing on technology architectureBusiness solutionsFocus on leveraging service providers or commercial-off-the-shelf solutions. As machinelearning gains momentum, more frameworks will be packaged with solutions.Technology for analyzing dataFocus on core technology like data science, machine learning and natural language processing for off-the-shelf algorithms, models and different types of data analysis capabilities.Technology for organizing dataFocus on data capture and enrichment tools to organize data so that it can be ingested intothe analysis framework.Technology for acquiring dataFocus on cognitive IoT sensors or devices for acquiring and disseminating structured andunstructured information.HardwareFocus on compute and storage power required to handle big data; MI algorithms work a lotmore effectively on GPUs. Examples of such hardware are Google TPU, Cirrascale, NVIDIA,DGX-1, Titan X, etc.Figure 68/Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning

Cognizant 20-20 InsightsLooking forwardThe digital world has brought tremendousopportunities and new challenges. The key isto prioritize and convert the opportunities ina systematic and holistic enterprise-focusedapproach to create a digital solution. The solutionneeds to be functionally effective, practical,supportive for a plug-and-play architectureand, importantly, scalable in part and as a whole.The solution design would typically focus onmodernization and hardening of legacy and core ITsupporting multimodal architecture models.A digital-ready, scalable architecture wouldprioritize specific aspects of architecture likecloud adoption and security improvementsfocusing on the four M’s of digital engineeringdiscussed before. It’s essential to avoid looking atit as a narrow data, automation, integration, IoT oranalytics problem.The solution designer needs to examine individualaspects of data procurement, gain business insightand then convert this knowledge into services thatadd business value. Each of these aspects requiresdeep dives and elaboration into architecturalbuilding blocks – starting with technical capability.Dimensions such as security, architecturalgovernance and data stewardship will then needto be addressed. Special attention is required tomanage the sensitivities related to automation’simpact on people. Each issue could be a topic for aseparate in-depth deliberation.Endnotes1“Data Growth, Business Opportunities, and the IT Imperatives,” IDC, April 2014, 4iview/executive-summary.htm.2“Three keys to building a data-driven strategy,” McKinsey, March 2013, ta-driven-strategy.3TOGAF, ogaf.4Malcolm Frank, Paul Roehrig and Ben Pring, What to Do When Machines Do Everything, John Wiley & Sons, February 2017,https://www.wiley.com/en-us/.5To Do When Machines Do Everything%3A How to Get Ahead in a World of AI%2C Algorithms%2C Bots%2C and Big Data-p-9781119278665.6Malcolm Frank, Paul Roehrig and Ben Pring, Code Halos: How the Digital Lives of People, Things, and Organizations Are Changing the Rules of Business, John Wiley & Sons, April 2014, https://www.wiley.com/en-us/Code Halos%3A How the Digital Lives of People%2C Things%2C and Organizations are Changing the Rules of Business-p-9781118862070.9/Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning

Cognizant 20-20 InsightsAbout the authorsAbhik SenguptaIT and Digital Enterprise Architecture Consultant, Cognizant ConsultingAbhik Sengupta is an IT and Digital Enterprise Architecture Consultant within Cognizant Consulting.With nearly three decades of industry experience, Abhik leads a team of enterprise architects and legacytransformation specialists guiding and supporting major enterprises across the world in their IT andenterprise architecture strategy and organization transformation initiatives. He has authored paperspublished in IEEE and other publications. Abhik can be reached at Abhik.Sengupta@cognizant.com.Kamales MandalEnterprise Architect, Cognizant ConsultingKamales Mandal is an Enterprise Architect within Cognizant Consulting. He has over 17 years of experiencein diverse walks of enterprise application development, enterprise integration and IT consulting, andhas worked with major enterprises across North America, Australia, UK and Latin America in the retail,government, healthcare and financial domains. Kamales has authored multiple papers and is the keyarchitect of an emerging semantic technology-based, model-driven consulting platform: ACE (analyze.consult. execute). He can be reached at Kamales.Mandal@co

enterprise architecture (EA) framework. The proposed architecture elements focus on what . Designing an application architecture In fact, digital advancement is progressing much . for an enterprise applica

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