Bridging the Last Mile: Getting Data to the People Who Need It
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Watch full webinar here: https://bit.ly/3cUA0Qi Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it. Attend this session to learn: - The challenges organizations face when trying to get data to the business users in a timely manner - How Data Virtualization can accelerate time-to-value for an organization’s data assets - Examples of leading companies that used data virtualization to get the right data to the users at the right time
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Bridging the Last Mile: Getting Data to the People Who Need It
- 1. DATA VIRTUALIZATION PACKED LUNCH WEBINAR SERIES Sessions Covering Key Data Integration Challenges Solved with Data Virtualization
- 2. Bridging the Last Mile: Getting Data to the People Who Need It Chris Walters Senior Data Solutions Consultant, Denodo Paul Moxon SVP Data Architectures & Chief Evangelist, Denodo
- 3. Agenda 1.What is the ‘Last Mile’? 2.Logical Architectures to the Rescue 3.Data Virtualization as a Data Access Layer 4.Denodo Platform Demo 5.Summary 6.Q&A 7.Next Steps 3
- 4. What is the ‘Last Mile’? Term from Logistics domain • Getting the product from the Distribution Center to the customer • Personalized deliveries rather than bulk shipping Also used in Telecoms • The ‘gap’ from the broadband switch to the home or office • Or the wireless gap between the wireless base station and the user’s mobile device 4
- 5. Why is the Last Mile Important? This is delivering the requested product to the customer • In our case, delivering data to the user • Change from bulk movement of data (ETL/ELT) to delivering data specific to users needs This used to be ‘Data Marts’ – extracted subsets of data for a specific use But, the information ‘landscape’ is getting more complex, more diverse, and more distributed • The old ETL to the Data Warehouse and then ETL to create Data Marts doesn’t cut it anymore… 5
- 7. Why is this a Problem? 7 IT DepartmentBusiness “You’re too slow, too expensive, and never deliver what I want.” “You can’t make up your mind, keep adding features, and never see the big picture.” Casual User: “Just forget it.” Power User: “Just give me a data dump.” BU Leader: “We’ll do it ourselves.” “I’d rather be doing something else than taking your order.” “You’ll come crawling back to us soon.”
- 8. 8 Gartner – The Evolution of Analytical Environments This is a Second Major Cycle of Analytical Consolidation Operational Application Operational Application Operational Application IoT Data Other NewData Operational Application Operational Application Cube Operational Application Cube ? Operational Application Operational Application Operational Application IoT Data Other NewData 1980s Pre EDW 1990s EDW 2010s2000s Post EDW Time LDW Operational Application Operational Application Operational Application Data Warehouse Data Warehouse Data Lake ? Logical Data Warehouse Data Warehouse Data Lake Marts ODS Staging/Ingest Unified analysis › Consolidated data › "Collect the data" › Single server, multiple nodes › More analysis than any one server can provide ©2018 Gartner, Inc. Unified analysis › Logically consolidated view of all data › "Connect and collect" › Multiple servers, of multiple nodes › More analysis than any one system can provide ID: 342254 Fragmented/ nonexistent analysis › Multiple sources › Multiple structured sources Fragmented analysis › "Collect the data" (Into › different repositories) › New data types, › processing, requirements › Uncoordinated views
- 9. 9 Gartner – The Evolution of Analytical Environments This is a Second Major Cycle of Analytical Consolidation Operational Application Operational Application Operational Application IoT Data Other NewData Operational Application Operational Application Cube Operational Application Cube ? Operational Application Operational Application Operational Application IoT Data Other NewData 1980s Pre EDW 1990s EDW 2010s2000s Post EDW Time LDW Operational Application Operational Application Operational Application Data Warehouse Data Warehouse Data Lake ? Unified analysis › Consolidated data › "Collect the data" › Single server, multiple nodes › More analysis than any one server can provide ©2018 Gartner, Inc. Unified analysis › Logically consolidated view of all data › "Connect and collect" › Multiple servers, of multiple nodes › More analysis than any one system can provide ID: 342254 Fragmented/ nonexistent analysis › Multiple sources › Multiple structured sources Fragmented analysis › "Collect the data" (Into › different repositories) › New data types, › processing, requirements › Uncoordinated views Operational Application Operational Application Operational Application IoT Data Other NewData Logical Data Warehouse Data Warehouse Data Lake Marts ODS Staging/Ingest Data Virtualization • Limited flexibility • Data duplication • Limited support for operational BI • Lack of integration with big data technologies • Non-Trivial support for bi-modal BI • Too big to move • Complex transformations moved to users • Security • Weak Metadata √ Improved Time to Market by 50 to 90% √ Improved Report Consistency √ Reduce Duplication of Data √ Improve Transparency √ Reduced development Cost √ Future Proof the architecture against technology changes
- 10. 10 Gartner – Logical Data Warehouse “Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April 2018 DATA VIRTUALIZATION
- 11. Gartner, Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs, May 2018 “When designed properly, Data Virtualization can speed data integration, lower data latency, offer flexibility and reuse, and reduce data sprawl across dispersed data sources. Due to its many benefits, Data Virtualization is often the first step for organizations evolving a traditional, repository-style data warehouse into a Logical Architecture”
- 12. 12 Benefits of a Virtual Data Layer A Virtual Layer improves decision making and shortens development cycles • Surfaces all company data from multiple repositories without the need to replicate all data into a data warehouse or data lake • Eliminates data silos: allows for on-demand combination of data from multiple sources A Virtual Layer broadens usage of data • Improves governance and metadata management to avoid “data swamps” • Decouples data source technology. Access normalized via SQL or web services • Allows controlled access to the data with low grain security controls A Virtual Layer offers performant access • Leverages the processing power of the existing sources controlled by Denodo’s optimizer • Processing of data for sources with no processing capabilities (e.g. files) • Caching and ingestion engine to persist data when needed TTV USAGE PERFORMANCE
- 14. Customer Case Study - FESTO 14 • Founded 1925 • Annual revenues (FY 2018) €3.2 B • Over 21,000 employees • Headquarters in Germany • World´s leading supplier of automation technology and technical education. BUSINESS NEED • Optimize operational efficiency, automate manufacturing processes, and deliver on-demand services to business consumers • Find smarter ways to aggregate and analyze data • An agile solution that enables the monetization of customer-facing data products • Free business users from IT reliance to become self-sufficient with reporting and analysis THE CHALLENGE: Find an agile way to integrate data from existing silos, including data warehouse, machine data, and others, that will reduce dependencies from business users on IT and provides quick turnaround and flexibility.
- 15. Customer Case Study - FESTO 15 SOLUTION: • Festo developed a Big Data Analytics Framework to provide a data marketplace to better support the business • Using the Denodo Platform to integrate data from numerous on- prem and cloud systems in real-time • A unified layer for consistent data access and governance across different data silos
- 16. 16 FESTO – Digital Transformation
- 17. Product Demonstration Bridging the Last Mile: Getting Data to the People Who Need It 17 Chris Walters Senior Data Solutions Consultant, Denodo
- 19. 19 DATA CONSUMERS DISPARATE DATA SOURCES SQL Queries (JDBC, ODBC, ADO.NET) Web Services (SOAP, REST, OData) Web-based catalog & search Secure delivery (SSL/TLS) DATA CONSUMERS MPP Processing Relational Cache Corporate Security Monitoring & Auditing Metadata Repository Execution Engine & Optimizer A Modern Data Virtualization Architecture DATA VIRTUALIZATION
- 20. 20 What’s the demo scenario We have a traditional Data Warehouse in Oracle To offload the warehouse end expand our data sets with IoT data, we have acquired a Hadoop cluster We are big users of SaaS solutions Need to easily build reports using data coming from these sources
- 21. 21 Example What’s the impact of a new marketing campaign for each country? Historical sales data offloaded to Hadoop cluster for cheaper storage Marketing campaigns managed in an external cloud app Country is part of the customer details table, stored in the DW Sources Combine, Transfor m & Integrate Consume Base View Source Abstraction join group by state join Sales Campaign Customer
- 22. Demo 22
- 23. Key Takeaways 23
- 24. 1. Information architectures are getting more complex, more diverse, and more distributed 2. Traditional technologies and data replication don’t cut it anymore 3. Data virtualization makes it quick and easy to expose data from multiple source to your users 4. Data virtualization provides a governance and management infrastructure required for successful data management Key Takeaways
- 26. 26 Next Steps Access Denodo Platform in the Cloud! Take a Test Drive today! www.denodo.com/TestDrive G E T S TA R T E D TO DAY
- 27. Thank you! © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies.