A Systematic Approach to Determine the Amount of Data Required for Asset Management Decisions

EasyChair Preprint 1534

Abstract

Facility, infrastructure, and asset related data is being generated at an unprecedented rate, usually without specific purposes or goals. Data is typically collected in large amounts for exploratory science, achieving significant statistical power, and because of the relatively cheap cost of storing data in the cloud. In many cases however, organizations do not consider the negative issues with indiscriminate data collection to include diminishing returns to reduce uncertainty in asset management decisions and the cumulative costs of the data. This paper proposes a novel 4-step (IEVA) framework for determining the amount of data required for asset management decisions. The framework is built upon the following steps: 1) identify the problem, 2) establish context, 3) verify/collect data, and 4) analyze/decide. The IEVA framework helps to establish a baseline that orientates asset managers to collect decision-focused data and make data-informed decisions. The IEVA framework was tested with energy data collected from seventy-two facilities and the results are presented.

Keyphrases: Asset Management, Big Data, Data-Informed Decisions, Decision-Focused Data

@booklet{EasyChair:1534,
  author    = {Brendan Maestas and Sean Stuntz and Joey Applebee and William Bentley and Jared Breuker and Andrew Davenport and Andrew Hoisington},
  title     = {A Systematic Approach to Determine the Amount of Data Required for Asset Management Decisions},
  howpublished = {EasyChair Preprint 1534},
  year      = {EasyChair, 2019}}