Enabling a Bimodal IT Framework for Advanced Analytics with Data Virtualization
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The document details a webinar series on data virtualization, focusing on addressing key data integration challenges within a bimodal IT framework. It contrasts Mode 1, which is predictable and structured, with Mode 2, which is exploratory and adaptive, while highlighting the implementation challenges of a bimodal organization. It also emphasizes the importance of data virtualization in integrating disparate data sources for real-time analytics and the role of advanced analytics in this context.
Enabling a Bimodal IT Framework for Advanced Analytics with Data Virtualization
- 1. DATA VIRTUALIZATION PACKED LUNCH WEBINAR SERIES Sessions Covering Key Data Integration Challenges Solved with Data Virtualization
- 2. Enabling a BiModal IT Framework with Data Virtualization Emma Stein Sales Engineer, Denodo Paul Fearon Senior Solutions Consultant, Denodo
- 3. Agenda1. Bimodal IT – the Pro’s and the Con’s • The challenge for Advanced Analytics 2. Virtualization and the Bimodal approach. • Demonstration 3. Q/A
- 4. Bimodal IT – the Pro’s and the Con’s
- 5. 5 Source : Gartner Kick-Start Bimodal by Launching Mode 2 “Bimodal recognizes that there are areas of the enterprise that have more certainty, objectives and clear cause and effect is understood, we can predict and plan – Mode 1. In other areas, requirements are unclear and changing, the relationship between action and outcome is uncertain, and things are less well understood at the start – Mode 2” Why Bimodal?
- 6. 6 What is Bimodal? Predictable vs Experimental Agility Revenue, Brand, Customer Experience Agile, Kanban, low-ceremony IID Empirical, continuous, process-based Small, new vendors, short- term deals Good at new and uncertain projects Business-centric, close to customer Short (days, weeks) Goal Value Approach Governance Sourcing Talent Culture Cycle Times Reliability Price for Performance Waterfall, V- model, high- ceremony IID Plan driven, approval based. Enterprise suppliers, long term deals. Good at conventional process, projects IT centric, removed from customer. Long (months) Mode 2 Mode 1 “Mode 1 is predictable, improving and renovating in more well-understood areas.” “Mode 2 is exploratory, experimenting to solve new problems. “
- 7. 7 Great idea but a challenge to implement Not popular with leadership “ In the digital era, CIOs not buying ‘this bimodal crap’ ” – CIO magazine* Just make everything AGILE (i.e. lose waterfall and everything is delivered in sprints). New roles, new processes, the setup of a Bimodal org can be prohibitive Highly integrated systems can cause ownership issues. Splitting teams can cause staff challenges (morale, resignations, etc.). Budgetary challenges (who gets what?). * Ref – Clint Boulton – CIO Magazine May 2, 2017 https://www.cio.com/article/3193793/in-the-digital-era-cios-not-buying-this-bimodal-crap.html
- 8. 8 Challenges still exist for Advanced Analytics TDWI Best Practices Report – Data Management for Advanced Analytics
- 9. Bimodal approach with Data Virtualization Crossover with Advanced Analytics challenges
- 10. 10 What is Data Virtualization? Consume in business applications Combine related data into views Connect to disparate data sources 2 3 1 DATA CONSUMERS DISPARATE DATA SOURCES Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word... Analytical Operational Less StructuredMore Structured CONNECT COMBINE PUBLISH Multiple Protocols, Formats Query, Search, Browse Request/Reply, Event Driven Secure Delivery SQL, MDX Web Services Big Data APIs Web Automation and Indexing CONNECT COMBINE CONSUME Share, Deliver, Publish, Govern, Collaborate Discover, Transform, Prepare, Improve Quality, Integrate Normalized views of disparate data “Data virtualization integrates disparate data sources in real time or near-real time to meet demands for analytics and transactional data.” – Create a Road Map For A Real-time, Agile, Self- Service Data Platform, Forrester Research, Dec 16, 2015
- 11. 11 BI and Analytics Reference Architecture
- 12. 12 Data Virtualization scope of responsibility? Business consumer IT Provisioned
- 14. 14 Business consumer IT Provisioned IT Provisioned views Model Prototyping Model Operationalization From prototype to production
- 15. 15 Sophisticated tools for Sophisticated users AA Users/Developers Data Virtualization Developers Data Scientists/Analysts
- 16. 16 Empower the user • Users are much more sophisticated • Tools are much more intuitive and user friendly • Easy Discovery/Collaboration via Data Catalogs • Users can curate their own views of data • Data Scientists & Analysts create new models & views for general consumption • AA is ubiquitous (All types of consumers use productionized AI/ML algorithms) • IT provides views to source data and manage security/governance • IT manages “production” process
- 17. Product Demonstration 17 Sales Engineer, Denodo Emma Stein
- 18. 18 Data Scientist Flow Identify useful data Modify data into a useful format Analyze data Execute data science algorithms (ML, AI, etc.) Share with business users Prepare for ML algorithm
- 19. 19 https://flic.kr/p/x8HgrF Can we predict the usage of the NYC bike system based on data from previous years?
- 20. 20 Data Sources – Citibike
- 21. 21 There are external factors to consider. Which ones? https://flic.kr/p/CYT7SS
- 22. 22 What We’re Going To Do… 1. Search through the Data Catalog to identify useful data sets 2. Prepare the data in the Design Studio 3. Analyze our datasets using Apache Zeppelin 4. Using Python, read the 2017 data and run it through our ML algorithm for training 5. Use 2018 data to test the algorithm 6. Save the results and productionize our findings for other users to explore join join Citi Bike Weather Date Apache Zeppelin
- 23. Demonstration 23
- 24. 24 Work as advisor as well as provider • Bimodal organization may be a stretch but a bimodal approach to information sharing is possible. • Start with “Island” Projects. Use them to polish processes and methodologies, before expanding to more broader projects that have dependencies etc. • Connect business team with IT ambassadors (you already have them) and define workable communication methodologies. • Data is a complicated asset, use the tools and education to give consumers important insight (a hammer is useless until you learn how and when to use it). • Define a change control process that makes it easy for AA users to productionize insight
- 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. Thanks! www.denodo.com info@denodo.com © 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.