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DAS Slides: Emerging Trends in Data Architecture – What’s the Next Big Thing?

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DAS Slides: Emerging Trends in Data Architecture – What’s the Next Big Thing?

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With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in data architecture, along with practical commentary and advice from industry expert Donna Burbank.

With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in data architecture, along with practical commentary and advice from industry expert Donna Burbank.

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DAS Slides: Emerging Trends in Data Architecture – What’s the Next Big Thing?

  1. 1. Copyright Global Data Strategy, Ltd. 2020 Emerging Trends in Data Architecture – What’s the Next Big Thing? Donna Burbank Global Data Strategy, Ltd. January 23rd, 2020 Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  2. 2. Simplifying Advanced Data Workloads with NoSQL Data Management for Modern Data Demands Jennifer Yonemitsu Director, Product Marketing @DataStax
  3. 3. Modern Database Foundation – Apache Cassandra #1 DATABASE for scale, availability, and fault tolerance ZERO DOWNTIME the only masterless architecture among leading DBMS platforms PROVEN AT MASSIVE SCALE #1 Contributor to Apache Cassandra and Apache TinkerPop projects Develop and contribute all open source Cassandra drivers Core Products Developed from open source Cassandra, TinkerPop Best distribution and support of Cassandra for production, fully integrated with TinkerPop
  4. 4. 3 Modern Data Diversity and Complexity LEGACY DATA INTEGRATION REAL-TIME, STREAMING, EVENTS DISPARATE, SILO’D DATA DATA SECURITY / SOVEREIGNTY UNPREDICTABLE SCALE HYBRID, MULTI, INTER-CLOUD
  5. 5. Modern Data Brings Workload Complexity Today’s Related Data and Complex Workloads Traditional Siloed Data and Workload Management >
  6. 6. Data Management Evolution { : } 2020’s2000’s1980’s
  7. 7. Application Challenges with Advanced Data Workloads Data Ingest • Fast bulk and individual queries, and graph entity ingest/mutability • Need atomicity guarantees Data Model Flexibility • Schema for easy and obvious data management and optimal performance API Flexibility • Ability to query data or traverse a graph quickly • e.g. traverse a graph from any object, or access an individual graph object Intelligent Indexing • Support global traversing with forgiving search • Leverage indexing for optimized performance Connected Data • Related disparate data transformation • Analytics/algorithm, and graph execution Intelligent Scaling • Scale easily to meet workload demands • e.g. Bind queries, traversals to local datasets, collocate neighborhoods Security • Authorization for data objects and individual data entities
  8. 8. So, how do we solve for mixed workloads? 7 How complex is your query? • Simple - Single Partition/Single Index Lookup, Single Iteration • Complex - Full Scan, Large Aggregation, Unknown Iterations • In between - Multiple Partition/Indexes, Aggregations, or Multiple Iterations How fast do you need it? • Machine Time < the time it takes to interrupt a user process • Human Time < time a user will wait • Offline Time is Everything else
  9. 9. Mixed Workload Coverage – Customer 360 Queries Offline fast Human fast Machine fast CQL Search Analytics Responsetime Simple Complex 1. Find me Dave 2. Find me all people with similar names to ‘Dave’ 3. Tell me if there are duplicate Dave’s 4. Find how Dave and Jenn are connected 5. Find how influential Dave is in my application 6. Show Dave what items are trending for anyone with the same profile while he looks for a gift to purchase for Jenn in his mobile app 1 DSE Graph 4 5 3 2 Stream Processing 6
  10. 10. 9 Fraud Anomaly detection and connected components IoT Act on sensors and analyze the network Recommendation Systems Your preferences and your network’s preferences Law Enforcement Bad actor identification and criminal network activity Fleet Management Vehicle tracking and path optimization New Opportunities BLENDED WORKLOADS AT SCALE WITH DATASTAX Manage Seamlessly with one Database Graph, Analytics, Search, Advanced Security, Stream Processing, In-memory Engine
  11. 11. All of Your Workloads Seamlessly Handled by One Database MIXED-WORKLOAD SUPPORT WITH DATASTAX Native Graph Database 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 – a requirement for most modern applications today. Enterprise Search Functionality provides indexing support for Cassandra; functionality for geospatial, full-text, and advanced search operations. In-memory Engine delivers the fastest possible response times for data that is constantly accessed. Stream Processing with Apache Kafka and Cassandra fully integrated for streaming event data.
  12. 12. 11 Simplified Data Complexity A Single Data Platform Mixed Workloads at Scale Scale apps for complex data & workloads with ease Real-time Intelligence Access the full value of your ever-changing data Multi-DC, Multi-Platform Deployment and operations wherever you choose to deploy SINGLE CLOUD MULTI- / INTER-CLOUD HYBRID CLOUD ON PREMISES
  13. 13. Learn about Cassandra and TinkerPop today! 12 academy.datastax.com @DataStaxDevs datastax.com/downloads @DataStax
  14. 14. Thank you
  15. 15. Copyright Global Data Strategy, Ltd. 2020 Emerging Trends in Data Architecture – What’s the Next Big Thing? Donna Burbank Global Data Strategy, Ltd. January 23rd, 2020 Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  16. 16. Global Data Strategy, Ltd. 2020 Donna Burbank 2 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 awarded the Excellence in Data Management Award from DAMA International. 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
  17. 17. Global Data Strategy, Ltd. 2020 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 3 This Year’s Lineup
  18. 18. Global Data Strategy, Ltd. 2020 What We’ll Cover Today • With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. • This webinar will discuss the results of a recent DATAVERSITY survey on emerging trends in data architecture, along with practical commentary and advice. 4 Content is based on research from a 2019 DATAVERSITY survey on “Trends in Data Management”.
  19. 19. Global Data Strategy, Ltd. 2020 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.” 5 DMBOK Definition
  20. 20. Global Data Strategy, Ltd. 2020 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.” 6 Survey Respondents Provided a Range of Views
  21. 21. Global Data Strategy, Ltd. 2020 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 2020 Global Data Strategy, Ltd Data Management Supports a Wider Data Strategy www.globaldatastrategy.com
  22. 22. Global Data Strategy, Ltd. 2020 Data-Driven Business Data-Driven Business is an impetus for data management • 70% of respondents feel that their organization sees data as a strategic asset. • 68% are looking to save costs and increase efficiency • 53% see digital transformation as a key driver for data management 8 Data Management is the foundation of the Data-Driven Business
  23. 23. Global Data Strategy, Ltd. 2020 Business Optimization vs. Business Transformation 9 Digital Transformation is transforming business Business Optimization Becoming a Data-Driven Company • Improving Efficiency • Reduce Redundancy • Eliminate Manual Effort • Growing Revenue • Improved Marketing Campaigns • Data-driven Product Development • Etc. Business Transformation Becoming a Data Company • New Business Models • Data is the product • Monetization of information • Digital Transformation • Improved Marketing Campaigns • Data-driven Product Development • Etc. How do we do what we do better? How do we do something different?
  24. 24. Global Data Strategy, Ltd. 2020 Data is Driving the Future of the Global Economy • “For most of the history of business, the world’s leading companies have been industrially-focused… • …But today’s business reality is very different. We live in a world of bytes – and for the first time technology and commerce have collided in a way that makes data far more valuable than physical, tangible objects. • The best place to see this is in how the market values businesses.”1 10 Product Focus Data Focus The World Economic Forum sees today’s economy as driven by Data, not Goods & Services 1 Oct 15, 2018, World Economic Forum, “These are the 8 major forces shaping the future of the global economy”
  25. 25. Global Data Strategy, Ltd. 2020 Democratization of Data Management An analysis of Global Data Strategy, Ltd’s customers shows a wide range of industries and sectors. 11 Not Just for the Big Players Anymore Nonprofit Finance & Insurance UtilitiesHealth Care Education & Universities Government Manufacturing Media & Entertainment Retail Restaurant
  26. 26. Global Data Strategy, Ltd. 2020 Business Intelligence & Analytics Business Intelligence & Analytics are key to gaining business insight. • 80% of respondents indicated that reporting and analytics were key drivers for data management. • 87% are implementing business intelligence • 87% have a data warehouse in place • 22% are using a data lake in conjunction with a data warehouse 12 Business Intelligence & Analytics provide Business Insight
  27. 27. Global Data Strategy, Ltd. 2020 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% 13 Gaining Business Insight through Analytics and Reporting continues to be a main business driver for today’s organizations.
  28. 28. Global Data Strategy, Ltd. 2020 Data Governance Data Governance is critical in supporting the data- driven business • 76% have a current data governance initiative in place or are planning one in the near future • 86% consider data security a priority • >50% identified improved collaboration through using a defined data architecture 14 Data Governance improves collaboration and increases data accountability & protection
  29. 29. Global Data Strategy, Ltd. 2020 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. 15 A number of respondents mentioned Data Governance as a way to align various stakeholders around common goals
  30. 30. Global Data Strategy, Ltd. 2020 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 16
  31. 31. Global Data Strategy, Ltd. 2020 Ethics in Data Management 17 1 United Nations Global Sustainability Goals How can we use data for greater good?We can do this, but should we do this? • Anecdotally in our practice, a notable change in 2019 is the increase in the number of clients asking to include ethics as a formal part of there data governance and data management initiatives: • Empathetic Customer Journey Mapping • Analytics to support “Data for Good” -- community health and support initiatives • Ethics as part of data governance principles and guidelines
  32. 32. Global Data Strategy, Ltd. 2020 Data Platform Evolution Data Technology & Platforms continue to evolve • 81% are using relational databases on-premises • 71% are using spreadsheets as a data platform • Future plans include a wide range of technologies: • Cloud-based relational databases • Graph databases • NoSQL databases • Big Data platforms 18 While relational databases remain the leading platform, new technologies are being added to the mix.
  33. 33. Global Data Strategy, Ltd. 2020 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. 19 Relational database still dominate the market, both on premises and Cloud-based
  34. 34. Global Data Strategy, Ltd. 2020 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. 20 Future plans still feature relational databases, with a higher focus on Cloud Adoption, and a wider mix of technologies.
  35. 35. Global Data Strategy, Ltd. 2020 Future Technologies • Currently implemented: • Containerized technologies: 55.17% • Kubernetes: 53.57% • Serverless Computing (PaaS, FaaS, etc.): 45.45% • Future Plans: • Deep learning: 17.65% • Industry 4.0: 33.33% • Digital Twins: 8.33% 21 Future plans expand analytics focus to Deep Learning and Industry 4.0 .
  36. 36. Global Data Strategy, Ltd. 2020 Data Management 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% 22
  37. 37. Global Data Strategy, Ltd. 2020 Prioritizing Efforts for 2020 23 So… What’s the next Big Thing?
  38. 38. Global Data Strategy, Ltd. 2020 Top 5 Predictions for 2020 24 1. The blurring of “Business” and “IT” roles will continue 2. The blurring of “Data Management” and “Business” will continue (e.g. Digital Transformation, Industry 4.0) 3. Organizations will rely on a matrixed set of data-centric tools and technologies (e.g. relational, NoSQL, graph, etc.) 4. Data governance and ethics will have an increased role in business operations 5. Analytics and BI will continue to be a strong driver, with an evolving focus more towards AI and predictive analytics, rather than simple descriptive analytics/reporting.
  39. 39. Global Data Strategy, Ltd. 2020 White Paper: Trends in Data Management • Download from www.globaldatastrategy.com • Under ‘Whitepapers’ • Also available on Dataversity.net 25 Free Download
  40. 40. Global Data Strategy, Ltd. 2020 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 26 Join us next month
  41. 41. Global Data Strategy, Ltd. 2020 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. 27 Data-Driven Business Transformation Business Strategy Aligned With Data Strategy Visit www.globaldatastrategy.com for more information
  42. 42. Global Data Strategy, Ltd. 2020 Questions? 28 • Thoughts? Ideas? www.globaldatastrategy.com

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