Emerging Trends In Data Architecture

Emerging Trends in Data ArchitectureDonna BurbankGlobal Data Strategy Ltd.Tuesday, November 14, 20172:30 PM - 2:50 PM

Who Am I?Donna Burbank is a recognisedindustry expert in informationmanagement with over 20 years ofexperience in data strategy, informationmanagement, data modeling, metadatamanagement, and enterprisearchitecture. Her background is multifaceted across consulting, productdevelopment, product management,brand strategy, marketing, and businessleadership.She is currently the Managing Director atGlobal Data Strategy, Ltd., aninternational information managementconsulting company that specializes inthe alignment of business drivers withdata-centric technology. In past roles,she has served in key brand strategy andproduct management roles at CATechnologies and EmbarcaderoTechnologies for several of the leadingdata management products in themarket.As an active contributor to the datamanagement community, she is a longtime DAMA International member, PastPresident and Advisor to the DAMARocky Mountain chapter, and wasrecently awarded the Excellence in DataManagement Award from DAMAInternational in 2016.She has worked with dozens of Fortune500 companies worldwide in theAmericas, Europe, Asia, and Africa andspeaks regularly at industryconferences. She has co-authored twobooks: Data Modeling for theBusiness and Data Modeling MadeSimple with ERwin Data Modeler and is aregular contributor to industrypublications. She can be reached atdonna.burbank@globaldatastrategy.comDonna is based in Boulder, Colorado.She was on the review committee forthe Object Management Group’s (OMG)Information Management Metamodel(IMM) and the Business ProcessModeling Notation (BPMN). Donna isalso an analyst at the Boulder BI TrainTrust (BBBT) where she provides advicesand gains insight on the latest BI andAnalytics software in the market.Follow on Twitter @donnaburbankGlobal Data Strategy, Ltd. 20172

Who Are You? SurveyHow Would You Describe Your Role?Businessperson or Business AnalystData Architect, Data Modeler, or Data AnalystEnterprise ArchitectBusiness Intelligence Analyst or DeveloperDBA, Programmer, or Technical ITData ScientistA combination of the aboveOtherGlobal Data Strategy, Ltd. 2017

What We’ll Cover Today With technology changing at a faster pace than ever before, it can be challenging to keep up with thelatest innovations in data architecture. If you’re wondering which technologies and trends yourorganization should include as part of your enterprise data architecture, you’re not alone. How do we define Data Architecture in today’s data ecosystem? Which are the hot technologies to adopt? What might be a fad or passing trend? Which are on their way out? How can a data architecture support my business goals? Content is based on research from a 2017 DATAVERSITY survey on “Emerging Trends in DataArchitecture”.Global Data Strategy, Ltd. 20174

What is Data Architecture?DMBOK Definition The DAMA Data Management Body of Knowledge (DMBOK), defines data architecture as thefollowing: “Data Architecture is fundamental to data management. Because most organizations have moredata than individual people can comprehend, it is necessary to represent organizational data atdifferent levels of abstraction so that it can be understood and management can make decisionsabout it. Data Architecture artifacts include specifications used to describe existing state, define datarequirements, guide data integration, and control data assets as put forth in data strategy. An organization’s Data Architecture is described by an integrated collection of master designdocuments at different levels of abstraction, including standards that govern how data iscollected, stored, arranged, used, and removed. It is also classified by descriptions of all thecontainers and paths that data takes through an organization’s systems.”Global Data Strategy, Ltd. 20175

What is Data Architecture?Survey Respondents Provided a Range of Views Survey respondents also provided a range of relevant definitions including:“ the practice of examining the enterprise strategy, and identifying the key Data Integration pointsthat need to be enabled to execute that strategy, and laying out a roadmap for creating the corecapabilities to deliver those integrations, so that companies can leverage data as a strategicasset.”“ the science of assessing where your company data is, and the art of designing the best futureway of storing data across the enterprise.”“ the discipline to help businesses manage their data in the most effective, secure, compliant, andprofitable way.”Global Data Strategy, Ltd. 20176

Business Drivers for Data ArchitectureWhat’s Driving the Need? As more organizations seedata as a strategic asset, andwith the drive towards DigitalBusiness Transformation onthe rise, the need to analyze,understand & govern coredata assets continue to be akey goal.Global Data Strategy, Ltd. 20177

Who is Responsible for Creating a Data Architecture?Wide Range of Responses shows Need for Collaboration With a greater business focus ondata and a wider range oftechnologies associated with DataManagement A Bit moreInteresting Well Duh it is not surprising that there is aconcomitant rise in the diversity ofroles responsible for developing aData Architecture. the role of the data architect, notsurprisingly, continues to play alarge role.Collaboration is Key to aSuccessful Data Architecture.Global Data Strategy, Ltd. 20178

What Data Platforms are Currently in Use?“Which of the following data sources or platforms are you currently using?[Select all that apply] A wide range of technologies arecurrently in use.Spreadsheets areubiquitousMore Legacyplatforms (44.6%)than Big Data (42.2%)Relational Databasesare still clearly theleader.Global Data Strategy, Ltd. 20179

Emerging Technologies“Which of the following do you plan to use in the future that you are not usingcurrently? [Select all that Apply]” For those looking at newtechnologies, there is a wide rangeof responses. Big Data Platforms a leaderMove to Cloud RDMBSGraph DatabaseReal-time StreamingInternet of Things (IoT)Many looking to BigData Platforms Many are still uncertain, indicatingthe vast rate of change and widearray of choices available.Movement to theCloud is popularUncertainty iscommon.Global Data Strategy, Ltd. 201710

Moving to the CloudA Growing Trend As indicated in the previous response re: Future Technologies, adoption of Cloud Technologies ison the rise, with over 75% of respondents currently implementing a Cloud strategy, or planningto in the future.Scalability & Cost Savings are leading reasons for moving to the CloudGlobal Data Strategy, Ltd. 201711

Cloud Migration not without its ConcernsWhat are your Concerns regarding moving data to the Cloud? [Select All that Apply] Security & Privacy areleading concerns for thosemoving to the Cloud.Global Data Strategy, Ltd. 201712

Big Data a Growing TrendAnalysis & Discovery are Key Drivers Over 70% of organizations areeither using Big Data solutions, orplanning to in the future. Analysis & Discovery are leadingtrends including: Data Science & Discovery Reporting & Analytics “Sandbox” ExplorationGlobal Data Strategy, Ltd. 201713

Big Data Concerns Security is a leading concern, and DataGovernance was a top write-in response. The Complexity of current BigData solutions & the SkillsRequired to manage themwere also common issues.Global Data Strategy, Ltd. 201714

Data Lakes There was a mixed responseto Data Lake ImplementationGlobal Data Strategy, Ltd. 201715

Data Warehouse vs. Data LakeA Data Warehouse is a storage repository that holds currentand historical data used for creating analytical reports. Datastructures & requirements are pre-defined, and data isorganized & stored according to these definitions.Data WarehouseGlobal Data Strategy, Ltd. 2017A Data Lake is a storage repository that holds a vastamount of raw data in its native format, includingstructured, semi-structured, and unstructured data.The data structure & requirements are not defined untilthe data is needed.Data Lake16

Master Data Management As more and more organizations struggle with obtaining a single, consistent view of core datasuch as Product, Customer, Vendor, and Location, Master Data Management (MDM) is seeing arise in implementations with over 65% of respondents pursuing a MDM strategy. Master Data is the consistent and uniform set of identifiers andextended attributes that describes the core entities of theenterprise including customers, prospects, citizens, suppliers, sites,hierarchies and chart of accounts (sic). Master data management (MDM) is a technology-enableddiscipline in which business and IT work together to ensure theuniformity, accuracy, stewardship, semantic consistency andaccountability of the enterprise's official shared master data assets.- Source GartnerGlobal Data Strategy, Ltd. 201717

Data Modeling is Hotter than everSexiest job of the 21st Century?In a recent DATAVERSITY survey,over 96% of were engaged in DataModeling in their organizations.Global Data Strategy, Ltd. 201718

Types of Models & Diagrams in Use Business-centric models arepopular: Logical Data Models Conceptual Data Models Business Process Models As well as models that showthe interactions betweensystems in today’s complexdata environment: Data Flow DiagramsGlobal Data Strategy, Ltd. 201719

Data Modeling styles vary, but ER and DFD are leaders Every modeler has his or herown style and methods. Amongthe most common methods inuse are: Entity Relational (E/R) Data Flow Diagrams (DFD) Star Schema Data Warehouse(Kimball-style)Global Data Strategy, Ltd. 201720

Metadata is Hotter than everThe other sexiest job of the 21st Century?In a recent DATAVERSITY survey, over 80% ofrespondents stated that:Metadata is as important, if not moreimportant, than in the past.Global Data Strategy, Ltd. 201721

Metadata Management Use Cases Use Cases were similar acrossthe 2016 & 2017 surveys: 2017 saw growth in: Regulation & Audit Master Data ManagementGlobal Data Strategy, Ltd. 201722

Open SourceOpen Source adoption is on the Rise The Cost Savings and Flexibility with theOpen Source approach is appealing to many. Projects supported by the Apache Foundationsuch as Cassandra, Hadoop, Hive, Spark,Lucene, Pig, Storm, etc. are in production atenterprises worldwide. Open Source often requires more in-housedevelopers to implement and lacks thesupport of commercial products. Some are packed with other applications &sold commercially with support available.Global Data Strategy, Ltd. 201723

Blockchain Blockchain has received much attention in the media as a way to revolutionize financial and otherindustries. According to Harvard Business Review:“With blockchain, we can imagine a world in which contracts are embedded indigital code and stored in transparent, shared databases, where they are protectedfrom deletion, tampering, and revision. In this world, every agreement, everyprocess, every task, and every payment would have a digital record and signaturethat could be identified, validated, stored, and shared. Intermediaries like lawyers,brokers, and bankers might no longer be necessary. Individuals, organizations,machines, and algorithms would freely transact and interact with one another withlittle friction. This is the immense potential of blockchain.”Among survey respondents, however, only 7.7% areactively using or pursuing use of Blockchain technology.Global Data Strategy, Ltd. 201724

Blockchain Use Cases For those using Blockchain technologies, use cases are not limited to financial transactions. These include: Identity Management Regulatory Compliance and Audit Records Management Capital Markets Supply Chain Management International Payments Peer-to-Peer TransactionsGlobal Data Strategy, Ltd. 201725