1. Leveraging Big Data to Drive Big Changes in a Big Way Don Guckert, P.E., APPA Fellow Associate Vice President & Director of Facilities Management The University of Iowa

2. What is Big Data? Merriam-Webster's Collegiate Dictionary added Big Data earlier this year…. Big Data noun Definition of BIG DATA : an accumulation of data that is too large and complex for processing by traditional database management tools

3. What is Big Data? Industry Definition: High volume, high variety and high velocity of information assets that demand cost effective and innovative forms of information process- ing for enhanced insight and decision making.

5. Velocity Moore’s Law: an axiom of microprocessor development usually holding that processing power doubles about every 18 months.

6. Volume Capacity to store data is also growing exponentially …doubling every 40 months since 1980.

7. Volume What is a Zettabyte?

8. Volume What is a Zettabyte? A Zettabyte is a billion Terabytes!

9. Volume There will be 35 zettabytes of data generated annually by 2020, up from 2.7 zettabytes in 2013.

10. Variety Companies world-wide invested $5.5 billion in intelligent building systems in 2012 and the number is expected to rise to $18.1billion by 2017. The campus built environment is mirroring the overall national trends in both big data growth and the investment in intelligent buildings.

11. Variety

13. Air Handler performance Fan Runtime Fan Power VFD Control Static pressure Heating control Cooling control Ventilation Air control Economizer Control CO2 control Outside Air Temperature Return Air Temperature Make-up Air Temperature Supply Air Temperatures Freeze Stat alarms Smoke/Fire alarm Pressure Drop Alarm

14. So What?

15. Benefits of Leveraging Big Data

16. Business Intelligence: Understand what has happened in the past. In-memory Processing: Understand what is happening now. Analytics: Understand what will probably happen.

17. Benefits of Leveraging Big Data Business Intelligence: Understand what has happened in the past. In-memory Processing: Understand what is happening now. Analytics: Understand what will probably happen. All three processes require intense data collection in order to make accurate predictions based on the past, present and presumably the future

18. Big Data & Predictive Maintenance Understanding what has happened in the past, happening now, and what will probably happen is foundational for predictive maintenance.

19. Big Data & Predictive Maintenance Understanding what has happened in the past, happening now, and what will probably happen is foundational for predictive maintenance.

20. Big Data & Predictive Maintenance Hot/cold calls often lag by hours or days of a system failure. With Big Data, we’ll see it and fix it, or avoid the failure altogether, before the occupant feels it.

21. Our Value Proposition - Managing Risk Predicting impending failure, and preventing that failure, mitigates risks to business continuity and financial risk.

22. Investing versus Spending The dollar outlay shifts from productivity losses, repair costs and wasted energy to planned investments in infrastructure and technology.

23. Investing versus Spending The dollar outlay shifts from productivity losses, repair costs and wasted energy to planned investments in infrastructure and technology.

24. The University of Iowa’s Journey into Big Data

25. 2007 Energy Management Plan Strategy #5: Central Building Control & Monitoring The University will pursue a course of action to invest in a centralized building controls system with the goal of achieving control and monitoring of the space inventory representing 90% of the GEF energy expense by July 1, 2013.

26. 2007 Energy Management Plan The Energy Control Center will facilitate efforts in monitoring and determining trends in energy usage and system efficiencies. The investment in central control and monitoring will have the additional major advantages of improving occupant comfort and monitoring security.

27. Foundational Building Blocks

28. Staffing in house expertise

29. Foundational Building Blocks Staffing in house expertise Converting all building controls systems to DDC

30. Foundational Building Blocks Staffing in house expertise Converting all building controls systems to DDC Metering of all major university buildings

31. Foundational Building Blocks Staffing in house expertise Converting all building controls systems to DDC Metering of all major university buildings Integrating control systems

32. Foundational Building Blocks Staffing in house expertise Converting all building controls systems to DDC Metering of all major university buildings Integrating control systems Leveraging data historian for optimization

33. Foundational Building Blocks Staffing in house expertise Converting all building controls systems to DDC Metering of all major university buildings Integrating control systems Leveraging data historian for optimization Developing Design Standards

34. Foundational Building Blocks Staffing in house expertise Converting all building controls systems to DDC Metering of all major university buildings Integrating control systems Leveraging data historian for optimization Developing Design Standards Implementing commissioning programs

35. Foundational Building Blocks Staffing in house expertise Converting all building controls systems to DDC Metering of all major university buildings Integrating control systems Leveraging data historian for optimization Developing Design Standards Implementing commissioning programs Creating the Energy Control Center

36. Energy Control Center

37. In 2009 the Energy Control Center was established, serving as a central information center to view all that was happening in energy production, distribution and consumption.

38. Energy Control Center Early successes in the Energy Control Center’s focused on connecting and integrating all of the process control technology for our utilities production plants.

39. Optimizing Production & Distribution Soon it was realized that we were positioned to implement energy supply and distribution optimization software.

40. Building Systems

41. Monitoring Building Systems Central monitoring of more than 400,000 points of connection.

42. Monitoring Building Systems More than 23,000 connection points for the Pappajohn Biomedical Discovery Building.

43. Monitoring Building Systems Original vision was to use the central center to monitor and analyze building systems in support of field operations.

44. Monitoring Building Systems Original vision was to use the central center to monitor and analyze building systems in support of field operations. Realized the system to do much more.

45. Fault Detection & Diagnostics Operational parameters can be established and then alarmed when out-of-variance conditions (faults) are experienced.

46. Fault Detection & Diagnostics Operational parameters can be established and then alarmed when out-of-variance conditions (faults) are experienced.

49. Big Data Challenges

50. Connected infrastructure

51. Big Data Challenges Connected infrastructure Building the artificial intelligence

52. Big Data Challenges Connected infrastructure Building the artificial intelligence Overcoming cultural entrenchments

53. Big Data Challenges Connected infrastructure Building the artificial intelligence Overcoming cultural entrenchments Workforce readiness

57. Big Data Driven Megatrends

58. From employing mechanics to employing technicians

59. Big Data Driven Megatrends From employing mechanics to employing technicians From operations expenses to capital investments

60. Big Data Driven Megatrends From employing mechanics to employing technicians From operations expenses to capital investments From component diagnostics to systems diagnostics

61. Big Data Driven Megatrends From employing mechanics to employing technicians From operations expenses to capital investments From component diagnostics to systems diagnostics From systems drift to systems hold

62. Big Data Driven Megatrends From employing mechanics to employing technicians From operations expenses to capital investments From component diagnostics to systems diagnostics From systems drift to systems hold From reactive response to predictive response

63. Big Data Driven Megatrends From employing mechanics to employing technicians From operations expenses to capital investments From component diagnostics to systems diagnostics From systems drift to systems hold From reactive response to predictive response From super heroes to unsung heroes

64. Big Data Driven Megatrends From employing mechanics to employing technicians From operations expenses to capital investments From component diagnostics to systems diagnostics From systems drift to systems hold From reactive response to predictive response From super heroes to unsung heroes From managing facilities to managing risk

65. Big Data Driven Megatrends From employing mechanics to employing technicians From operations expenses to capital investments From component diagnostics to systems diagnostics From systems drift to systems hold From reactive response to predictive response From super heroes to unsung heroes From managing facilities to managing risk From institutional knowledge to shared knowledge

66. Big Data Driven Megatrends From employing mechanics to employing technicians From operations expenses to capital investments From component diagnostics to systems diagnostics From systems drift to systems hold From reactive response to predictive response From super heroes to unsung heroes From managing facilities to managing risk From institutional knowledge to shared knowledge From valuing expertise to valuing collaboration

67. The Road Ahead….