Distributed Data Across Cloud and On-Premises: Opportunities and Challenges
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The document discusses the challenges and opportunities associated with managing distributed data across cloud and on-premises environments. It emphasizes the need for effective data management strategies, such as logical data architectures and data virtualization, to enhance business value and decision-making. Additionally, the document highlights the importance of addressing data silos and implementing agile solutions to optimize data access and use.
Distributed Data Across Cloud and On-Premises: Opportunities and Challenges
- 1. Transforming Data Management for the Cloud
- 2. Distributed Data Across Cloud and On-Premises – Opportunities and Challenges Philip Russom, Ph.D. Independent Industry Analyst for Data Management and Analytics
- 3. Distributed Data Across Cloud and On-Premises: Opportunities and Challenges PHILIP RUSSOM INDEPENDENT INDUSTRY ANALYST
- 4. What you will learn Business Opportunities for Today’s Data The Challenge of Distributed Data Strategies for getting Business Value from Distributed Data ◦Logical data architectures, Data fabric, and Data virtualization
- 5. Business Opportunities based on Leveraging Enterprise-wide Data ▪ Many organizations are positioned to get greater business value from data – Data management skills and infrastructure are mature – They have plenty of data ▪ If they could access the full wealth of their data, they could leverage the data for: – More impactful decisions, advanced analytics, operational efficiency, customer service, cost reductions
- 6. Challenges to Leveraging and Reusing Enterprise Data ▪ Valuable enterprise data is distributed across many IT systems – Each with its own data models, semantics, and interfaces ▪ Makes data resources hard to find and slow to process for multiple business use cases and user types – Users need data mgt solutions and strategies that address distributed data ▪ Data on cloud(s) exacerbates the problems of distributed data – Hybrid (on prem/cloud), Multi-Cloud, Silos migrated to
- 7. SOURCE: Philip Russom 2023 Leave Data Silos, as is Re-Enginee r Most Data Virtual Views Logical Data Architecture s Business Value from Distributed Data Time and Cost of Effort Minimal Effort Maximum Effort Little or No Value Added Greatest Value Consolidate Data into Fewer Datasets Collocate Data onto Fewer Platforms Data Strategies for Getting Business Value from Distributed Data YOUR GOAL – Look for data strategies that provide the most business value, but from minimal technical effort That’s the upper right-hand corner of this chart Migrate Data to Cloud Data Fabric
- 8. Logical Data Architecture, with Fabric & Virtualizatio n Multiple Data Strategies can Work Together, to get Business Value from Distributed Data Data Consumption = Rpts, Analytics, Data Science Distributed Data Sources • ERP, CRM, SFA, Mktg • Financials, Operations • Procurement, Shipping • Many more sources Aggregated Data • Data Warehouse • Data Lake • Data Lakehouse • ODSs, marts… Logical Data Architecture Metadata and Other Data Semantics Data Integration, Quality, Streams, CDC, APIs Data Virtualization = Logical Layer. Central Sec/Gov. RT Delivery. Data Fabric SOURCE: Philip Russom 2023
- 9. Business Value gained from Virtual and Logical Data Strategies ▪ Quick and cost-effective strategies – As compared to data collocation, consolidation, and migration ▪ Agile development methods yield shorter time-to-use ▪ Logical methods can reduce operating costs – And enable creative operating models for development ▪ Simplified, standardized, and controlled access to distributed data ▪ Views of data specific to data domains, user types, and use cases
- 10. High-Value Use Cases enabled by Virtual and Logical Data Strategies ▪ Business-friendly virtual views enable self-service data access – For self-service data exploration, prep, visualization, and analytics ▪ Single point of entry to distributed data – For better security, governance, data standards, and consistency ▪ Abstraction layer easily adapts to source system changes – Simply adjust the layer, not the many tools that access the layer ▪ Virtual data often means fresher data for reports and dashboards
- 11. Conclusion: Leveraging Distributed Data is best done with Logical/Virtual Strategies ▪ Distributed data is a business opportunity to be seized – Enables more impactful decisions, agile analytics, operational efficiency, faster time-to-insight ▪ Distributed data is a tech challenge to be addressed – Makes more enterprise data accessible for leverage, and embraces new technologies and data design methods ▪ Logical/Virtual Strategies excel with distributed data – Logical data architectures, data fabric, and
- 12. Distributed Data Across Cloud and On-Premises – Opportunities and Challenges SVP Data Architecture and Chief Evangelist, Denodo Paul Moxon Independent Industry Analyst for Data Management and Analytics Philip Russom, Ph.D. FIRESIDE CHAT
- 13. Thank you!