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Building a Future-State Data Architecture Plan - Where to Begin?

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With technology changing at an ever more rapid pace and business requirements ever-evolving to meet the needs of the market, building a future-state Data Architecture plan can be a challenge. Join this webinar to learn practical ways to balance technology and business needs as you develop your future-state architecture for the coming years.

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Building a Future-State Data Architecture Plan - Where to Begin?

  1. 1. Copyright Global Data Strategy, Ltd. 2019 Designing a Future State Data Architecture: Where to Begin? Donna Burbank Global Data Strategy, Ltd. December 3rd 2019 Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  2. 2. DataStax + Apache Cassandra™ Building a Foundation for Modern Data Architecture Louise Westoby Senior Director, Product Marketing @lwestoby
  3. 3. Data Architecture Has Evolved 3 Today CLOUD 1990s1970s CLIENT-SERVERMAINFRAME
  4. 4. Modern Apps Require Modern Data Management 4 What’s needed is a modern database ALWAYS-ON DISTRIBUTED REAL TIME CAN YOUR LEGACY DATA SYSTEMS KEEP UP?
  5. 5. Database Architecture Matters 5 High Availability No more scheduled outages for upgrades. Always available. Key Design Principle: Design with the understanding that system/hardware failures can and will occur Flexible Deployment Options Can run in any data center – on premises, hybrid cloud, multi cloud. Data Sovereignty and Security Globally distribute data without compromising security. No Single Point of Failure Out of box data replication for fault tolerance and global distribution. Scalability Continuously available at all time zones, at all times. Cost of Deployment and Management Control rising and unpredictability of costs.
  6. 6. 6 Modern Database Foundation – Apache Cassandra #1 DATABASE for scale, uptime, and performance ONLY MASTERLESS ARCHITECTURE among leading DBMS vendors #1 Contributor to Apache Cassandra Develop and contribute all open source Cassandra drivers Developed and updated from the open source Cassandra project Best distribution of Cassandra for production
  7. 7. DataStax is Uniquely Suited for Modern Apps DataStax Masterless Architecture No single point of failure for 100% uptime Predictable performance with linear scalability and low latency data access Single toolset to manage cloud and on premise developments Automatic global data distribution for hybrid and multi-cloud deployments DataStax customer deployments are in the public cloud >60% 7
  8. 8. All of Your Workloads Seamlessly Handled by One Database 8 MULTI-WORKLOAD DATABASE SUPPORT Native graph database capabilities allow you to unlock the value behind your data and all the relationships that make them meaningful. Integrated Spark analytics allows for hybrid analytical transaction processing and Spark streaming, which is a requirement for most modern applications today. Enterprise search functionality provides indexing support for Cassandra as well as functionality for geospatial, full-text, and advanced search operations. In-memory engine delivers the fastest possible response times for data that is constantly accessed.
  9. 9. 9 Macy’s needed to invest in a positive and engaging customer experience across all channels in an effort to attract and retain customers online and in-store for customer retention and business revenue. STRATEGY Adopt a multi-cloud strategy with IBM with the option to add other CSPs Leverage DataStax for omnichannel catalog service OUTCOME Service scales up to millions of universal product codes and million requests per second, with a 100 ms response time. A seamless online and mobile customer experience across multi-cloud infrastructure Easily adding new CSPs to existing cloud infrastructure leveraging DataStax’s masterless architecture. Macy’s needed a flawless data management platform to power its omnichannel catalog – especially during the busy holiday season. The Challenge
  10. 10. Learn About Cassandra for Free! academy.datastax.com 10
  11. 11. Thank you
  12. 12. Global Data Strategy, Ltd. 2019 Donna Burbank 12 Donna is a recognised industry expert in information management with over 20 years of experience in data strategy, information management, data modeling, metadata management, and enterprise architecture. Her background is multi-faceted across consulting, product development, product management, brand strategy, marketing, and business leadership. She is currently the Managing Director at Global Data Strategy, Ltd., an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. In past roles, she has served in key brand strategy and product management roles at CA Technologies and Embarcadero Technologies for several of the leading data management products in the market. As an active contributor to the data management community, she is a long time DAMA International member, Past President and Advisor to the DAMA Rocky Mountain chapter, and was recently awarded the Excellence in Data Management Award from DAMA International in 2016. Donna is also an analyst at the Boulder BI Train Trust (BBBT) where she provides advice and gains insight on the latest BI and Analytics software in the market. She was on several review committees for the Object Management Group’s for key information management and process modeling notations. She has worked with dozens of Fortune 500 companies worldwide in the Americas, Europe, Asia, and Africa and speaks regularly at industry conferences. She has co- authored two books: Data Modeling for the Business and Data Modeling Made Simple with ERwin Data Modeler and is a regular contributor to industry publications. She can be reached at donna.burbank@globaldatastrategy.com Donna is based in Boulder, Colorado, USA. Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  13. 13. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 24 - on demand Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 18 - on demand Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 28 - on demand Data Modeling at the Environment Agency of England - Case Study • April 25 - on demand Data Governance - Combining Data Management with Organizational Change • May 23 - on demand Master Data Management - Aligning Data, Process, and Governance • June 27 - on demand Enterprise Architecture vs. Data Architecture • July 25 - on demand Metadata Management: Technical Architecture & Business Techniques • August 22 - on demand Data Quality Best Practices (w/ guest Nigel Turner) • Sept 26 - on demand Data Catalogues: Architecting for Collaboration & Self-Service • October 24 - on demand Data Modeling Best Practices: Business and Technical Approaches • December 3 Building a Future-State Data Architecture Plan: Where to Begin? 13 This Year’s Lineup
  14. 14. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same • April 23 Master Data Management – Aligning Data, Process, and Governance • May 28 Data Governance and Data Architecture – Alignment and Synergies • June 25 Enterprise Architecture vs. Data Architecture • July 22 Best Practices in Metadata Management • August 27 Data Quality Best Practices • September 24 Data Virtualization – Separating Myth from Reality • October 22 Data Architect vs. Data Engineer vs. Data Modeler • December 1 Graph Databases: Practical Use Cases 14 Join us in 2020
  15. 15. Global Data Strategy, Ltd. 2019 What We’ll Cover Today • With technology changing at an ever more rapid pace and business requirements ever-evolving to meet the needs of the market, building a future-state Data Architecture plan can be a challenge. • This webinar focuses on practical ways to balance technology and business needs as you develop your future-state architecture for the coming years. • How do we define Data Management 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 2019 DATAVERSITY survey on “Trends in Data Management”. 15
  16. 16. Global Data Strategy, Ltd. 2019 What is Data Management? The DAMA Data Management Body of Knowledge (DMBOK), defines data architecture as the following: “Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.” 16 DMBOK Definition
  17. 17. Global Data Strategy, Ltd. 2019 What is Data Management? Survey respondents also provided a range of relevant definitions including: “Data Management describes people, process, and technology to optimize, protect, and leverage data as an asset.” “Data Management is an organization capability supported by tools, processes, standards, and people.” “Data Management makes enterprise data effective and efficient by supporting business activities.” 17 Survey Respondents Provided a Range of Views
  18. 18. Global Data Strategy, Ltd. 2019 A Successful Data Strategy links Business Goals with Technology Solutions Level 1 “Top-Down” alignment with business priorities Level 5 “Bottom-Up” management & inventory of data sources Level 2 Managing the people, process, policies & culture around data Level 4 Coordinating & integrating disparate data sources Level 3 Leveraging data for strategic advantage Copyright 2019 Global Data Strategy, Ltd Data Management Supports a Wider Data Strategy www.globaldatastrategy.com
  19. 19. Global Data Strategy, Ltd. 2019 Implementation Now & In the Future • The Top Data Management components currently implemented are : • Business Intelligence and Reporting: 87.02% • Data Warehouse: 86.55% • Data Security: 85.95% • Data Integration: 70.37% • Document Management: 70.33% • Data Governance: 61.11% • Data Quality: 61.29% • Those planned in the next 1-2 years include: • Semantic Web Technologies: 76.00% • Data Virtualization: 63.24% • Data Science (Including AI or Machine Learning): 54.74% • Big Data Ecosystems: 53.42% • Self-service Analytics: 52.63% • Metadata Management: 52.43% • Data Governance: 38.89% 19
  20. 20. Global Data Strategy, Ltd. 2019 a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around common goals Business Goals & Drivers • Analytics and Reporting continue to lead the business drivers for data management. • Top drivers include: • Gaining insights through reporting and analytics: 79.70% • Saving cost and increasing efficiency: 68.42% • Reducing risk: 66.92% • Improving customer satisfaction: 58.65% • Driving revenue and growth: 57.14% • Supporting digital transformations: 53.38% 20 Gaining Business Insight through Analytics and Reporting continues to be a main business driver for today’s organizations.
  21. 21. Global Data Strategy, Ltd. 2019 Data is an Asset, but Communication & Quality Remain an Issue • While the majority of organizations see data as an essential asset, and manage security and compliance: • All stakeholders across the organizations do not take part in data management • Communication is an issue • Data Quality continues to be a challenge • Formal data management metrics are not tracked 21
  22. 22. Global Data Strategy, Ltd. 2019 a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around common goals Who is Driving Data Management in an Organization? • While Technical Roles still lead Data Management activities, Business Stakeholders are playing a larger part. • From those who listed “Other”, Data Governance Lead was a common response. 22 Notably, a number of respondents mentioned Data Governance as a way to align various stakeholders around common goals
  23. 23. Global Data Strategy, Ltd. 2019 a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around common goals Current Platform Adoption • Relational Database still dominate the data management landscape • Majority is on-premises • Some Cloud Adoption • Spreadsheets still ubiquitous, partly due to the large interest from business users. 23 a
  24. 24. Global Data Strategy, Ltd. 2019 a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around common goals Future Platform Adoption • Future Plans still include a high percentage of relational databases, with a higher percentage of Cloud-based systems. • A wider distribution of platform usage indicates the variety of options and fit-for- purpose solution – one size doesn’t fit all. 24 Relational Databases still dominate the landscape, with a higher focus on Cloud Adoption.
  25. 25. Global Data Strategy, Ltd. 2019 Moving to the Cloud: Pros and Cons 25 While organizations are moving to the Cloud for better scalability, concerns regarding security & privacy remain.
  26. 26. Global Data Strategy, Ltd. 2019 Models & Architecture Help Manage Disparate Data Platforms 26 What Types of Models/Diagrams do you use in your Data/Enterprise Architecture?
  27. 27. Global Data Strategy, Ltd. 2019 Prioritizing Efforts for 2020 27 What does this mean for your Data Architecture Plans for 2020 and Beyond?
  28. 28. Global Data Strategy, Ltd. 2019 28 Key Steps to Creating a Data Management Program • The following steps should be included when creating a data management program. The order is less important than ensuring that they are completed. Steps to Success Secure Senior Executive Support Identify a Data Champion among senior leadership. Define Vision, Drivers & Motivations Define business-driven vision for the program. Build the Business Case Outline key benefits of data program & risks of not doing so Deliver “Quick” Wins Short, iterative, business-driven projects deliver short-term value, building towards long-term gain. Identify Business-Critical Data Focus on the data that has the highest impact on the business. Identify & Interview Stakeholders Elicit feedback from key stakeholders – listen & communicate. Create Organization Define an organizational structure that aligns with your way of working. Communicate Build a communication plan from initial feedback phase throughout all phases of the program. Assess Data Maturity Assess the data maturity of the organization across all aspects of data management. Map Priorities to Capabilities Create a realistic “heat map” aligning business goals with data management capabilities.
  29. 29. Global Data Strategy, Ltd. 2019 Defining an Actionable Roadmap • Develop a detailed roadmap that is both actionable and realistic • Show quick-wins, while building to a longer-term goal • Balance Business Priorities with Data Management Maturity • Focus on projects that benefit multiple stakeholders • Mix core architecture with “new shiny things” 29 Maximize the Benefit to the Organization Initiatives H1 '17 H2 '17 H2 '18 H2 '18 Strategy Development Governance Lineage for Privacy Rules Business Glossary Population & Publication Data Warehouse Metadata Customer Analytics Pilot – Social Media integration Open Data Publication IoT Integration Ongoing Communication & Collaboration Customer Product Location Integrated Customer View Marketing Sales Customer Support Executive Team
  30. 30. Global Data Strategy, Ltd. 2019 Building Blocks to an Effective Roadmap 30 Why? Who? How? What? When? • What are the key business drivers? • Think both “Offense” & “Defense” • What KPIs can be monitored to show ROI? • Who are the key stakeholders who will benefit? • Who is an executive champion? • Who are the Data Owners & Stewards helping to support data governance? • How will you organize the Data Governance team(s)? • How are business capabilities aligned with data management priorities? • What data needs to be managed & prioritized? • In which platforms or systems? • When will you roll this out? • What is the timing and cadence or actions and deliverables? • Are there other key initiatives it’s important to align with? • What are some potential “quick wins”?
  31. 31. Global Data Strategy, Ltd. 2019 Summary • A Robust Data Architecture supports managing Data as a Strategic Asset in order to gain business insights • Reporting and Analytics are key business initiatives • With growing interest from business users, more roles than ever are involved in Data Architecture decisions, driving the need for collaboration. • Organizations are faced with the challenge of making sense of a diverse data technology landscape • Relational Databases are by far the leader in use by most enterprises • The move to the Cloud lends to both Cost Savings & Scalability as well as Security & Privacy concerns. • With a wide variety of options available, fit-for-purpose solutions are key • Models and metadata are more important than ever in gaining an understanding of both business requirements & technical implementation. • A successful data management program and architecture requires a balanced mix of people, process, technology, and governance
  32. 32. Global Data Strategy, Ltd. 2019 About Global Data Strategy, Ltd • Global Data Strategy is an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. • Our passion is data, and helping organizations enrich their business opportunities through data and information. • Our core values center around providing solutions that are: • Business-Driven: We put the needs of your business first, before we look at any technology solution. • Clear & Relevant: We provide clear explanations using real-world examples. • Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s size, corporate culture, and geography. • High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of technical expertise in the industry. 32 Data-Driven Business Transformation Business Strategy Aligned With Data Strategy Visit www.globaldatastrategy.com for more information
  33. 33. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same • April 23 Master Data Management – Aligning Data, Process, and Governance • May 28 Data Governance and Data Architecture – Alignment and Synergies • June 25 Enterprise Architecture vs. Data Architecture • July 22 Best Practices in Metadata Management • August 27 Data Quality Best Practices • September 24 Data Virtualization – Separating Myth from Reality • October 22 Data Architect vs. Data Engineer vs. Data Modeler • December 1 Graph Databases: Practical Use Cases 33 Join us in 2020
  34. 34. Global Data Strategy, Ltd. 2019 White Paper: Trends in Data Management • Download from www.globaldatastrategy.com • Under ‘Whitepapers’ • Also available on Dataversity.net 34 Free Download
  35. 35. Global Data Strategy, Ltd. 2019 Questions? 35 • Thoughts? Ideas? www.globaldatastrategy.com

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