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MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

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Big data can drive insight for all companies, large and small. With diverse data sources, healthcare companies can merge patient records with diagnostics, education organizations can better measure and monitor student performance from a variety of inputs, and in the public sector, disparate data can be used to improve operations.

In this session, we will take a whirlwind tour of industry examples with a variety of solutions from SQL to APS, as well as Hadoop, Microsoft Cortana and Power BI. And learn firsthand how North Texas Tollway Authority leverages big data to improve the customer experience on the roadways.

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MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along

  1. 1. MT32 How relational (SQL) and unstructured data (Hadoop) learned to get along David Leibowitz, Dell EMC Stephen Kyriakos, North Texas Tollway Authority Larry Levy, Microsoft
  2. 2. “The computer isn’t the thing. The computer’s the thing that gets us to the thing” - Joe MacMillan
  3. 3. 3 Big Data
  4. 4. 4 What Can I Do About It? When Will it Happen? Why Did it Happen? What Happened? Questions asked of Big Data Innovation Complex implementations Spreadmarts Siloed data Transactional systems Value Enterprise data warehouse OLAPETL Operational Reporting Machine learning Any dataIn-memory Internet of Things Optimization & StimulationHadoop DashboardsAd hoc analysis Predictive Analytics Data mining
  5. 5. Example of the Harmony of Structured & Unstructured Data: Healthcare
  6. 6. Case Study: Fullerton Health
  7. 7. Example of the Harmony of Structured & Unstructured Data: Public
  8. 8. Case Study: North Texas Tollway Authority
  9. 9. 9 BI System – 0 to 70 MPH
  10. 10. 10 Theirs Theirs
  11. 11. 11 Ours
  12. 12. 12 Everyone drives on toll roads $9.5 Billion in new projects
  13. 13. 13 First to use transponder as a method for toll collection First to convert entire system to ETC Paving the way
  14. 14. 14 • Public entity / Private bonds • Multiple / diverse audiences • Blind to 20% of customers • Capture data at 70 MPH It’s complicated
  15. 15. 15 BI solves the complicated 7.4 million customers 2.1 million daily transactions 97% customer satisfaction One of the fastest growing regions in the U.S.
  16. 16. 16 It’s complicated. How we did it Evaluated resources Talked to the business Reviewed infrastructure Made a plan
  17. 17. 17 Why BI?Inventoried data Variety Velocity Venue Volume
  18. 18. 18 Define the “to be” state Manage hybrid workloads Integrate with business partners Self-Service and Full-Service Analytics Extend the solution
  19. 19. 19 Delivered CARS
  20. 20. 20 Become Your Own Architect We can purchase this photo if you like it. It speaks to finding a solution that works – cutting through layers in order to get what you need. Defined our “To Be” state Extend the BI team Empower usersBe your own architect
  21. 21. 21 Our Next Steps
  22. 22. Example of the Harmony of Structured & Unstructured Data: Education
  23. 23. 23 Demo: Student Analytics with Education Data Management
  24. 24. 24 Getting Started
  25. 25. 25 Stakeholders Procurement PMO It takes a village Lessons Learned
  26. 26. Data Modernization & Becoming Future Ready
  27. 27. 27 Key takeaways Complicated is doable Serve the business Build a flexible, scalable solution Plan your work, work your plan

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