Through your efforts to structure benchmark data sets across infrastructure, real estate, and
operations, you will move on to prioritizing your deliverables. To ensure that your program
can be established and scaled, you will need to assess the value of the many potential deliver-
ables involved in energy management. This chapter provides a basic methodology that can be
used as a guide to establish value speciﬁc to your infrastructure and operations. Consider the
framework laid out in this chapter as a guide that focuses on some common value themes across
facilities and IT departments. However, in all cases you will need to tweak this framework to
the speciﬁc interests of your stakeholder groups. In this chapter, you will learn the following:
How to best structure the data to support your program’s goals and deliverables
How to convert and normalize the data across different operations and disparate infra-•u
How to best present the data to highlight the multiple levels of value your program will
bring to the existing organization
Organizing the Data
Everything that can be counted does not necessarily count; everything that counts cannot
necessarily be counted.
Think of structuring the data that supports your program just as you would build a house. You
build it from the foundation up, and you make sure the foundation is strong enough to sup-
port the weight of the structure. There will be many permutations of the foundational data you
maintain, but they will come later. Because you are building your benchmark, proof of concept,
pilot, and program structure virtually at this point, you will have to make some assumptions.
However, try to limit assumptions where possible and avoid any “deep dives” on the data that
rely too much on theory and not enough on real data. When building a home, you don’t need to
do a structural load test on concrete, but you do need to use a material (concrete) that is a proven
stratum for construction. Power capacities, cooling burden factors, kilowatt-per-hour cost, and
carbon dioxide equivalencies are your concrete. Buildings, square footage, departments, and
system integration are your sides, roof, and trim.
There are several vehicles to consider when organizing your data. We opted to keep it simple
initially and used basic spreadsheets with pivot tables and, in preparation for the pilot setup,
used a Structured Query Language (SQL) database to cube the data sets for modeling purposes.
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