It is a truth universally acknowledged that an analyst with a data set must be in want of a protocol to deal with missing data (my apologies to Jane Austen). In our work analyzing data related to buildings and facilities, NIKA’s analysts often find that the facility managers are missing information that we would need to help them better predict equipment failure or develop maintenance schedules or manage inventory. If this sounds familiar, don’t fret – there are several ways to deal with missing data, each with its own advantages and disadvantages.
Option 1: Only Analyze Complete Data Sets
While this would be analytically pure, it is extremely limiting. Perfect data sets are only slightly less rare than unicorn sightings, and only accepting complete data sets may mean that there is questionable data that should be excluded that isn’t, such as when there is an expenditure or cost that’s out of range with the rest of the data.
Option 2: Ignore Incomplete Records
This is a valid option, provided the data set is large enough and the number of incomplete records is proportionally small; statistical literature normally limits this to around 5% of the records. There may be cases, however, where this is a poor fit, such as a building’s energy consumption reported on a monthly basis. For example, if only the hottest months are missing, then your analysis is not going to take into account all the air conditioning that’s used in those months.
Option 3: Use Estimates
This option has the advantage of making the data set complete. However, there are many different ways to make an estimate and a particular philosophy or reasoning must be chosen. In general, you want to treat each instance of missing data the same way. When you choose a method for using an estimate, you will have to defend it. An experienced data analytics expert can help you use proven estimation methodologies and calculations, like regression analysis, to fill in the blanks and get a more accurate picture of your overall facility health.
If you’ve thought about turning to data analytics to help improve facility operations, but were worried that your data isn’t accurate or complete enough, there are options available to you. Data analytics is a complex field requiring diverse expertise, including business, mathematics, and technology. These disciplines all come together to create a holistic view of your organization’s facilities. The Enterprise Technologies team at NIKA has extensive experience turning all kind of data into actionable analytics. Contact us to learn more about how we can help you increase the useful life of buildings and equipment in your facility portfolio.