MVP Cloud OS Week: Data Insights Keynote

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Keynote from Anthony Saxby, Microsoft UK, SQL Server Product Manager

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MVP Cloud OS Week: Data Insights Keynote

  1. 1. How do I provision and scale my LOB Apps on demand? How do I optimize my fleet based on weather and traffic patterns? What’s the social sentiment for my brand of products? Can I enable my workforce to work where they want on their device? How do I better predict future outcomes?
  2. 2. CUSTOMER SERVICE PROVIDER WINDOWS AZURE 1CONSISTENT PLATFORM
  3. 3. Business Transformation Integration Portals Business & Clinical Intelligence, Big Data Simplify Processes to Deliver Benefits Ascribe Consulting
  4. 4. Quality and Accreditation • Microsoft Gold Partner – Business Intelligence – Information Worker (SharePoint) – Advanced Infrastructure – Security Solutions • Microsoft Partner of the Year Finalist 2011 – Business Intelligence • Microsoft Partner of the Year Winner 2012 – Healthcare • EHI Award Winner - 'Best use of IT to improve patient safety’ • Microsoft Healthcare Services Ready – The only Healthcare specialist with this accreditation in the UK • ISO9001 Quality Assurance with TickIT certification
  5. 5. All content is commercial in confidence and bound by NDA
  6. 6. All content is commercial in confidence and bound by NDA
  7. 7. All content is commercial in confidence and bound by NDA
  8. 8. All content is commercial in confidence and bound by NDA
  9. 9. All content is commercial in confidence and bound by NDA
  10. 10. All content is commercial in confidence and bound by NDA
  11. 11. USER STORIES Medical and Operational Use Cases All content is commercial in confidence and bound by NDA
  12. 12. what do we stop doing because it doesn’t work? how do we prove safety, quality and cost-effectiveness? what underlying trends in consumption of services do we need to address? where do other agency, personal and social care pressures impact health services? how do I charge for and cost the scale and complexity of services that I provide? All content is commercial in confidence and bound by NDA
  13. 13. 1. Identify alcohol and substance misuse A&E attendances 2. Note the diagnostic intervention sought 3. Note the mode of arrival in advance of recording for statutory reporting purposes 4. Note the incident location relating to the ambulance call 5. Profile the incident using the notes captured by the ambulance crew and on handover to Hospital 6. Link this unstructured data back to structured data 7. Note volume of ECGs and other diagnostic tests ordered and check they have been fulfilled 8. Analyse the blockages in the A&E lean process based upon markers of process blockages in notes 9. Cost activity at point of intervention 10. Identify self-harming patients 11. Assess the impact of support teams and programmes designed to cut cost and improve services 12. Assess suicide risk and take action 13. Explain the likely causes of key performance standards breaches 14. Assess whether my patients were treated with dignity and respect 15. deploy a platform that is configurable to deal with RTAs, Sports injuries, perform MEWS, Glasgow Coma Scores and other things All content is commercial in confidence and bound by NDA
  14. 14. All content is commercial in confidence and bound by NDA
  15. 15. All content is commercial in confidence and bound by NDA
  16. 16. All content is commercial in confidence and bound by NDA
  17. 17. All content is commercial in confidence and bound by NDA
  18. 18. All content is commercial in confidence and bound by NDA
  19. 19. HEALTHPLAZA DEMONSTRATION How does it work? All content is commercial in confidence and bound by NDA
  20. 20. All content is commercial in confidence and bound by NDA
  21. 21. All content is commercial in confidence and bound by NDA
  22. 22. All content is commercial in confidence and bound by NDA
  23. 23. All content is commercial in confidence and bound by NDA
  24. 24. Unstructured Data Dimensions Alcohol Word Analytics Busy Drugs SelfHarm Diagnostic Intervention (Intervention) Sport Absconded Acuity Bariatric Body BP -Blood Pressure Cause Condition Department GCS Keyword -Catch all for history Medication Outcome Third Party (Who attended with) Patient Dignity Pulse RTC/RTC SATS SelfHarm Ward Measures PeriodArrivalToDischarge Is4hrTriageBreach TotalTriageWaitPeriod AttendanceCount TriageWaitPeriodMins Diedwithin30days MRICount XrayCount ECGCount CTCount MRIUnstructuredCount Dimensions Discharge Destination X X X X X X X X X X X X X X X X X X X X X X Disposal Method X X X X X X X X X X X Source OfReferral X X X X X X X X X X X Time (Arrival Time) X X X X X X X X X X X Time (Discharged Time) X X X X X X X X X X X Arrival Mode X X X X X X X X X X X Patient X X X X X X X X X X X Date (Discharged Date) X X X X X X X X X X X Date (Leftdept Date) X X X X X X X X X X X Age Group X X X X X X X X X X X Diagnosis (First Diagnosis) X X X X X X X X X X X Junk X X X X X X X X X X X AELength OfStay (Triage X X X X X X X X X X X Complaint X X X X X X X X X X X Date (Arrival Date) X X X X X X X X X X X Hospital X X X X X X X X X X X Died Days After Leaving X X X X X X X X X X X Structured to Unstructured All content is commercial in confidence and bound by NDA
  25. 25. MEC Global Media investment and management #1 Media investment organisation - GroupM #3 MEC Digital Media Agency - e-consultancy
  26. 26. ONLINE ADVERTISING
  27. 27. INDUSTRY POV “Our experience is that marketing is becoming much more data-driven. Clients want simplified use of the data, and they want it to be brought together in one place. Most digital campaigns are driven by these data and people want dashboards with real-time data – this is critical.” Sir Martin Sorrell
  28. 28. CHANGING LANDSCAPE •US Digital budgets steadily increasing, expected to reach near TV levels by 2016 (Source: eMarketer) •Event level tracking datasets will increase from 4PB to 10PB −(From 12,400,000,000,000 to 31,000,000,000,000 rows of data) * 0 10 20 30 40 50 60 70 80 2012 2013 2014 2015 2016 Spend(billions) Online Newspaper Magazine TV Source: eMarketer National debt of Ukraine
  29. 29. CHANGING LANDSCAPE • Traditional media budgets will remain constant; however the means to serve those ads is no longer “traditional” • Digital magazine ads will be interactive. − Could rotate creative based on device usage patterns • Out of home (OOH) ads could be changed depending on daypart, geo demographics, current events, etc.
  30. 30. CHANGING LANDSCAPE • The tablet/laptop as a second screen will allow for a deeper immersive experience for the consumer − Digital ad could be served allowing viewers additional product information for a recent TV product • The way to achieve this is through data, specifically, real time access to data
  31. 31. CHANGING CONSUMER http://www.youtube.com/watch?v=heSudg-tfIk
  32. 32. PDW SCENARIO - ATTRIBUTION BUSINESS GOAL: • Measure the true impact of media activity on campaign objectives and ROI TECHNICAL NEEDS: • Data from multiple publishers (Google/DoubleClick, MS Advertising/Atlas, etc.) • Daily FTP downloads and in some cases intra-day through APIs. • Typical analysis tools – SAS, R, SSAS, Excel/PowerPivot
  33. 33. CHALLENGES SPEED: • From data acquisition to availability for analytics PEOPLE: • Skills SCALE: • Physical storage
  34. 34. BAKE OFF Dataload PDW SQL PDW SQL Attribution report +35%
  35. 35. Great Cost Skills (operational / Db Great Specialised Ultra Big data Next Gen Data Ownership FastTrack Great Scale PDW2 impact? CONSIDERATIONS
  36. 36. DELIVERED UNDER 12 MONTHS: • Product which is now running on 5 “significant” clients REVENUE: • Incremental revenue, projected ROI on investment 18 months THINKING: • Changed established thinking / supported new • Moved us up a gear in a key strategic area
  37. 37. WHAT NEXT ? Enhanced PDW query engine Data Scientists BI Users DB Admins Regular T-SQL Results Structured data Traditional schema-based DW applications PDW V2 Social Apps Sensor & RFID Mobile Apps Web Apps Unstructured data HadoopPolybase
  38. 38. LESSONS LEARNT EXPERTS: • Saves time and money in the long run REALISTIC: • Start small PLAN: • Vendors, both tin and consultancy - time
  39. 39. • • • • • •

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