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Solution Brief: erwin Data Modeler (DM)The Value of DataModelng for theData-Driven EnterpriseDesigning, documenting, standardizing and aligning any datafrom anywhere produces an enterprise data model to drivebusiness value and underpin governance

Data Modeling: Why It MattersAs it has been for decades, datamodeling remains the optimalway to design and deploy newrelational databases with highquality data sources and supportapplication development. But itprovides even greater value fortoday’s enterprises, where criticaldata exists in both structured andunstructured formats and lives bothon premise and in the cloud.That value includes providingless costly and more efficientmulti-source data discovery andanalysis—through the reverseengineering and documenting ofdatabases—to underpin and quicklyenable large-scale integration,master data management andother initiatives. The ability thatdata modeling offers to synthesize,standardize and store datasources, deriving a single designthat collects historical data frommany disparate designs, also nowcontributes to business intelligenceand analytics consistency andclarity, as well as to reusinganalytics artifacts across projects.Most organizations also havemore than one database platform,and data often is stored in nonrelational formats. Big Data needsto be incorporated into the datamodeling landscape. That’s possiblewhen non-relational data sourcescan be natively designed, deployed,depicted and documented and alsointegrated with the larger world ofrelational data. Applied at this level,data modeling accommodates thecritical need to support realtimeanalytics applications at scale aswell as consistency for the purposeof data source analysis.Finally, one of the most compellinguse cases for a strong datamodeling approach lies in itsconnection to the increasinglyimportant and evolving realm ofdata governance. Today’s enterpriseembraces data governance todrive data opportunities, includinggrowing revenue, and limit datarisks, including regulatory andcompliance gaffes. Data modelingsolutions that incorporate acontrolled central repositoryof data definitions and providevisibility into where and howproperly defined data is to be usedare the foundation of solid datagovernance.DATA MODELING DEFINEDData modeling and design is the process of discovering, analyzing, representing andcommunicating data requirements in a precise form called the data model.Source: DAMAInternational DataManagement Bookof Knowledge V2DATA MODELING’S PLACE IN THE DATA MANAGEMENT UNIVERSEData modeling is part of an integrated and holistic approach to data management that helpsorganizations: Effectively manage and govern massive volumes of data Consolidate and build applications with hybrid architectures, including traditional, Big Data, cloud and on premise Support expanding regulatory requirements, such as GDPR Simplify collaboration across key roles and improve information alignment Improve business processes for operational efficiency and compliance Empower employees with self-service access for enterprise data capability, fluency and accountability.Solution Brief: erwin Data Modeler (DM) 2

Data Modeling: How to Do Iterwin Data Modeler (DM) has always been at the forefront of helping enterprises realizenew advantages from data modeling, originally bringing to the PC an easy and lightweightmanner the database design capabilities of heavyweight, mainframe-based CASE tools.According to IDC, it has a 30% share of the modelingarchitecture and tools market, and clearly continues toplay a critical role as companies transform themselvesinto agile, data-driven organizations where data isfoundational to doing business.erwin DM’s capabilities include:Business and technical database structurescan be visualized through an integrated,graphical model.erwin DM has built-in interfaces for more thanthree dozen database platforms on the markettoday, and it regularly increases its support forthese systems’ extended capabilities. It can readthe technical formats of each of these platformsand translate them into highly graphical modelsrich in metadata. Schema can be deployed frommodels in an automated fashion and iterativelyupdated so that new development can takeplace via model-driven design.End-user BI/analytics are empowered by datasource discovery, analysis and integration.erwin DM gives business users confidence inthe information they use to make decisions:providing a common, contextual, easilyaccessible source of data element definitionsensures they are able to draw upon the correctdata; understand what it represents, includingwhere it comes from; and know how it’sconnected to other entities.They also can rely on basic orchestration to pullin data sources via self-service BI and analyticsdashboards to get the answers they’re lookingfor. In the background, the solution has theability to integrate its models into whateverformat is required for downstream consumption.Business definitions and data-centric businessrules can be stored in the model along withtechnical database schemas, procedures andother information.With business definitions and rules on board,technical implementations can be better alignedwith the needs of the organization. Using anadvanced Design Layer Architecture, model “layers”can be created-with one or more models focusedon the business requirements that then can belinked to one or more database implementations.Design-layer metadata can be connected fromconceptual through logical to physical data models.A data model and associated metadatarepository is a central source for coredefinitions and data structures that can bestandardized and reused across projects.Not only can this help reduce redundancy and“reinvention of the wheel,” saving time andmoney, but it also can help increase quality, as allprojects use this common erwin DM foundationfor data definition and metadata analysis.By storing models in a central repository in theerwin DM Workgroup Edition, conflict resolution,versioning, security, standardization, andmodel organization and hierarchies all will beaddressed, and modeling teams can collaborateto create common objects that can be reusedto help create data quality and consistency.erwin Web Portal provides a simple way forboth non-technical and technical roles to viewthe information stored in the central modelrepository, providing tools such as web-basedinternet search and drill-down, model diagramvisualization, and “where used” report generationthat lets users see how objects interrelate.Solution Brief: erwin Data Modeler (DM) 3

Data Modeling: How to Do It (continued)Any2 (Any Data, Anywhere) strategy enablesorganizations to rationalize platform inconsistenciesand deliver a single source of truth for all enterprisebusiness data.Seamless integration witherwin Business Process (BP) anderwin Enterprise Architecture (EA)improves workflows and planning.Our Any2 approach includes on-demand modelingfor non-relational databases that offer speed,horizontal scalability and other realtime applicationadvantages. With erwin DM NoSQL, modelstructures from Couchbase and MongoDB can becreated automatically. Existing Couchbase andMongoDB data sources can be easily discovered,understood and documented through modeling andvisualization. Existing entity-relationship diagramsand SQL databases can be migrated to Couchbaseand MongoDB too. Relational schema also will betransformed to query optimized NoSQL constructs.erwin’s Any2 approach to designing,documenting, standardizing and aligning anytype of data no matter where it is deployed(on premise or in the cloud) drives quality,value and efficiency in all data managementinitiatives. Well-formed data models importedinto erwin BP, for example, provide thestructure that’s needed for putting data intoaction as part of system interactions andworkflows.Another piece of the strategy is delivered via erwinSafyr Option for ERP, which makes it possible topull out real and highly defined data models frompackaged applications’ data dictionaries and breakdown data management silos. Companies can useits metadata extraction capabilities to discover andbrowse complex data structures inherent in ERPapplications using business-friendly terms, as well asload sets of metadata into erwin DM or export logicalmodel information to the platform. erwinerwin EARNS DATAHONORSerwin Data Modeler: Five-TimeDatabase Trends and ApplicationsReaders’ Choice Awards Winnerfor Best Data Modeling SolutionSimilarly, data models imported intoerwin EA wrap data in the context ofbusiness capabilities, processes and services,applications, organizational structure andsupporting infrastructure to depict and enablean easily understood enterprise model.erwin CloudCore is erwin’s cloud-basedplatform-as-a-service suite, providing unrivaledspeed and ease of use in an on-demandenvironment for data modelers and enterprisearchitects.erwin DM underliesthe transformationof businessesinto data-drivenorganizations.Solution Brief: erwin Data Modeler (DM) 4

Data Modeling: Intersection with Data GovernanceLINEAGEAgileInnovationArprise chitectuterreEnARYIONCTDIBUSINESSGLOSSAge li ATRYADataGovernanceessThe entire data governance lifecycle is covered,across all data producer/consumer roles. ThisALYSIST ANPACIMData-DrivenInsightsB u si nIn conjunction with erwin BP, which reveals all theworkflows, business capabilities and applicationsthat use a data element; erwin EA, which showswhere the element resides within the infrastructure;and erwin Data Governance (DG), which providesdata visibility and control across the enterpriseto both IT and business stakeholders, theinfrastructure is in place to enable the workforceand processes that support actively governing dataassets and their alignment to the business.object is, in fact, a critical aspect of data governance.It puts directly in front of the organization thepotential risks, advantages and costs of a changeas well as empowers it to appropriately plan for achange determined to be worth making. It’s the agileway to get things right the first time.INEACH G&MINAI EARNLData governance initiatives ultimately depend onthe rigors of a solution like erwin DM WorkgroupEdition, where a controlled central repository isthe means of delivering the standardizationrequired to ensure that nothing un-vetted getsinto the data landscape.This includes data impact analysis. Delivering avisualization of the chain of objects, projects androles that may be impacted by a change to a singleSOCIALIZATIONData governance is about understanding criticalenterprise data within a business context, trackingits physical existence and lineage, and maximizingits security, quality and value. Data models bring tothe fore entity relationship diagrams that visualizeterms and their attributes and relationships Theyact as guides to these terms’ data elements in datadictionaries, point to glossaries of data definitions,and manage reference data codes related to terms.They curate where and how these properly defineddata elements are being used, so that others canaccess them for their own projects.persona-based approach, with everyone—fromexecutives on down—invested in and accountablefor data use, creates an enterprise data governanceexperience (EDGE) as opposed to an isolatedprogram managed by IT alone. As embodied in theerwin EDGE portfolio of products, IT and businessfunctions are joined to ensure organizationalobjectives around managing risks and maximizingopportunities are met.&LOWRKF ATIONWO ABORLLCOThe capabilities of the erwin DM productsand associated components reveal a circleof technologies and tools that underpinswhat truly matters to an agile and adaptableenterprise: data anceThe erwin EDGE is an approach and technology platformfor Data Governance 2.0.Solution Brief: erwin Data Modeler (DM) 5