Let’s take a relatively simple space management example: An organization’s executive team tasks a CRE analyst with finding locations with unused space – to identify potential subletting or consolidation opportunities. Available data for Building A shows that space is allocated at 150 square feet per person and a 90% occupancy rate – both metrics within performance targets for this hypothetical location.
But, what if the underlying data used to provide those metrics is inaccurate? What if several employees are assigned to space in Building A, but in reality, are working from home and not leveraging occupying permanent space in the building? What if some of the building square footage is not reported, thus understating the overall available space? In the absence of data quality monitoring, the analyst may assume that the reported data reflects reality and conclude that the building is properly utilized, when in fact, significant square footage remains unused. This location might have otherwise been a candidate for consolidation, sublet, or other cost-savings initiatives, but the initial screening analysis missed the opportunity, due to unidentified problems with the quality of the data.
CRE executives need to have confidence that the data they rely on for decision making is accurate and complete, and when it’s not, they need to know about it.
Gauging the quality of real estate data
Data is a corporate asset, as real an asset as any piece of equipment or a building, and needs to be valued, assessed and managed as such. There are four primary criteria we typically use to objectively measure the quality of real estate data:
- Data Completeness
Identify where information is missing to ensure that your KPIs and metrics are meaningful. For example, if location square footage is not provided consistently per location, many unit metrics will become unreliable (cost per square foot, square foot per seat, etc.).
- Data Validity
Implement data controls in the system of record to ensure that data describes reality. For example, do lease expenses extend beyond the expiration date of the lease?
- Data Consistency
Identify data outliers to separate data quality problems from performance problems. For example, if one location is reporting occupancy costs that are dramatically greater than the typical costs, it may indicate a data problem rather than a performance problem.
- Data Timeliness
Ensure that the required information is available when the decision makers need it. For example, when performing a Mark to Market analysis, it is useful to understand the date of the last broker-provided market rate information, to ensure the data is still relevant.
Implementing a data governance strategy and a data management program
A data governance strategy can provide a framework to address many of the technical and process issues that cause data quality problems. Real estate data may be compromised due to lack of integration between the many systems and spreadsheets used by the various real estate service lines. Process and training problems or a lack of system adoption can also lead to data quality problems.
Any governance strategy should include the implementation of technology and processes to support a data management program. Automated data quality monitoring tools are critical, not only to identify that a data quality problem exists but also to precisely measure and locate the problem, so that corrective action can be taken. A sustainable data governance strategy should include ongoing data quality monitoring, allowing the CRE team to monitor improvements to the completeness, validity, consistency and timeliness scores.
The critical nature of CRE data, its increasingly vital role in decision-making and the steps organizations can take to maximize its value, is a subject that warrants a focused, extensive examination – much more than we can cover in a single blog. Educating yourself about the value of treating CRE data as a corporate asset and instituting a coherent data governance strategy is a good first step because if you can trust your data you can trust your decisions.
Kevin Garry
Managing Director, Data Analytics, GCS Global Technology