The document discusses how data and analytics are evolving in the built environment. It notes that while data has value, organizations must have an effective data management strategy to unlock that value. Such a strategy includes having a data-driven culture, identifying available data, standardizing data naming, and avoiding "data drowning". Common obstacles to effective use of data are also outlined, as well as traditional and new applications of data analytics. The document advocates performing analytics close to the data source to enable real-time decision making and describes the role of Project Haystack in standardizing device data modeling.

1. 1
THE EVOLUTION OF DATA
AND ANALYTICS IN THE
BUILT ENVIRONMENT
John Petze
Marc Petock
James McHale

2. § Data has changed the way companies in
every industry does business and
manages performance
§ Data is now an irreplaceable asset
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3. DATA, A BLESSING AND CURSE
§ Data has tremendous value – but
requires effort to unlock that
value
§ Can seem overwhelming to
organizations
§ Requires a data management
strategy for optimal results
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4. DATA PLANNING
§ Data-driven culture
§ Defined strategy
§ Identify what data is available
§ Categorize the data
§ Standardize what you call
things
§ Avoid data drowning
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5. COMMON OBSTACLES
§ Lack of sharing
§ Ownership
§ Data Silos
§ Data Quality
§ Barriers to Access
§ Data Reliability and Continuity
§ Data overload-too much data; not relevant
§ Separate systems for storage and analytics
§ Lack of centralized data management system
§ Resources and planning
§ Lack of personnel who can read, understand the
data
§ Improper labeled/identified data
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6. TRADITIONAL WAYS DATA AND
ANALYTICS ARE BEING IMPLEMENTED
Energy efficiency, occupant comfort, reduced
maintenance costs and response time
§ Fault Detection: Identify broken dampers and valves as they break
§ Energy Analysis and Management: Automatically calculate the energy
and cost impact of broken equipment
§ Prioritize current operational issues based on their relative cost impact
§ Identify unnecessary periods of simultaneous heating and cooling
§ Identify sensors drifting out of calibration
§ Compare current facility operation to the “typical” day, week, or month
during similar weather
§ Compare energy use of one building to other buildings across a campus
§ Compare similar equipment, such as chillers or boilers within a plant, to
determine which is most efficient
§ Analyze current energy spend and predict future energy spend
§ Analyze occupant comfort data to pinpoint trouble areas
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7. NEW WAYS DATA AND ANALYTICS ARE
BEING IMPLEMENTED
§ Space Utilization
§ Occupant Engagement
§ Well-being
§ Productivity
§ Preventative/Predictive
§ Financial Performance
§ Asset Value
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8. THE MOVE TO ANALYTICS
AT THE EDGE
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9. 11/6/18 9

10. ANALYTICS AT THE EDGE A DEFINITION:
Performing essential
data acquisition,
storage
computation and
analytic functions as
close to the data
source as possible.
11/6/18 10Image courtesy of SkyFoundry

11. 3 THINGS ANALYTICS SHOULD DO –
WHETHER AT THE EDGE OR CLOUD
§ Increase the lifespan of your building
automation systems and mechanical
equipment
§ Provide direction on top priorities for
maintenance, comfort, and energy and
cost saving
§ Get the info to those who are most suited
to act on it in a format that matches their
needs
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12. THE ROLE OF PROJECT HAYSTACK IN THE
DATA REVOLUTION
§ Equipment systems, control systems, IoT devices
produce vast quantities of data
§ But that data has poor inconsistent descriptors to
define its meaning
§ This results in significant manual effort and cost
when attempting to work with the data
§ The industry needs a standardized methodology to
describe the meaning and relationships of data
§ That’s what Haystack is – a MARKUP LANGUAGE
FOR DEVICE AND EQUIPMENT DATA
§ Haystack enables normalization of IoT data from
systems and devices of all types with a uniform
data modeling methodology – a core need for the
Data Revolution
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13. PROJECT HAYSTACK UPDATES
Haystack Connect 2019 Announced
Haystack Connections Magazine January 2019 Issue In development
Collaboration with ASHRAE BACnet Committee
Membership – More members, more contributors
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14. SUMMARY
§ A data management plan empowers companies to seek
and make good fact-based decisions that drive better
outcomes
§ Connecting to it; collecting it, storing it, ensuring its
integrity; analyzing it, and using it to make business
decisions and develop a strategy
§ Determining who controls and who owns the data and
what is done with it will lead us down some interesting
paths
§ Data velocity is on the rise, companies must be able to
analyze it and get actionable advice instantaneously
§ It is not about more data, but rather asking the right
questions to get the right data, understand it and help
solve specific problems and address specific issues
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15. RESOURCES
§ www.project-haystack.org
§ Haystack Connections Magazine
§ Haystack Connections Magazine #1.pdf
§ Haystack Connections Magazine #3 Fall 2017.pdf
§ Haystack Connections Magazine Issue #2 Jan 2017.pdf
§ Haystack Connections Magazine Issue 4 June 2018.pdf
§ Guide Specifications
§ Guide Spec.docx
§ White Papers
§ CABA White Paper on Project Haystack.pdf
§ Introduction to Project Haystack a Primer.docx
§ Reference Implementation – Applying Haystack Tagging
for a Sample Building.pdf
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