Addressing the Significance of Data Quality in CRE: Part One

Posted by Chris Goodnight on Aug 30, 2018 4:31:28 PM

Building-data1We’ve all heard the old adage “Garbage in, garbage out,” a term attributed to be coined by George Fuechsel, an IBM programmer and instructor. Simply put, the phrase notes the cause and effect relationship between bad data input and output in the realm of computer science. In other words, software can only process what it is given.

As other industries before it, the digital transformation occurring within the commercial real estate industry has brought with it a heavy significance and reliance upon data, more specifically information that is actionable for decision making purposes. Information is the critical part of the data properties collect—it’s the right “stuff” that drives business intelligence and enables property owners and managers to make decisions that reduce costs, increase revenue, and improve performance.

While the ability of data to drive this type of value is universal across industries, the truth about data quality in the commercial real estate industry is a long and complicated story perpetuated by decades of incorrect data collection as well as the diversity of the industry itself. The prevailing opinion of most CRE professionals assumes bad data mostly consists only of duplicates. Duplicate data is bad data and it is a common mistake, but it is not the only contributing factor to bad data in the industry. 

To better understand CRE's data quality issues and incompleteness, look no further than at the property level. A recent survey indicated less than 30% of all property owners and managers (regardless of multifamily or commercial office markets) are confident their occupant data is accurate. If you're an owner, asset or property manager, ask yourself a few questions related to property-level data collection to put this into perspective:

  • Am I inputting false information such as a random lease expiration when I don't know it?
  • Am I ignoring important fields that I should have completed?
  • Am I not entering data in a streamlined fashion?
  • Am I searching for data (such as a contact) before creating a new record?

Garbage in, garbage out. With bad inputs, you get bad outputs of impaired decision making, inaccurate analytics, increased occupant turn, poor occupant experience, redundant and regressive marketing, and decreased NOI.

CollageBad data causes impaired decision making. Without accurate data, you cannot make reliableethical decisions, and you will end up with inaccurate analysis. With bad contact information, you won’t be able to maintain a successful level of loyalty or increased retention. When your property management system isn’t being utilized properly, perhaps due to duplicates or users not updating contact records, engagement with your occupants and prospective occupants on the other end is disjointed and therefore not reliable. You must avoid this redundant and regressive marketing mistake at all costs. And of course, all of this boils down to decreased NOI.

To put matters into perspective, the Data Warehousing Institute estimates that American companies across industries lose $600 billion dollars annually due to the cost of poor data quality. Additional estimates find 15-20% of data in a typical organization erroneous or otherwise unusable.

Staggering figures like these have given new meaning to the often grouped data quality management buzzwords, quality assurance (QA) and quality control (QC). Overall, although sometimes overlooked or undermined, the QA/QC process is an incredibly valuable aspect of any data analysis. It mitigates the errors and mistakes that can be detrimental in the development of trust and confidence between property ownership, management, and occupants. 

Dashboard_Data_QualityThe importance of providing commercial real estate owners with actionable data starts with implementing a well-organized data quality management solution. Property companies using this approach create a competitive advantage over their competitors. A well-managed data flow turns a property group into an expert in the statistics they deem valuable and enables it to deliver accurate details and interpretations of information to make better decisions at the property level. 

Access to relevant data is vital for succeeding in the commercial real estate market. In the industry’s quest to improve efficiency, firms should consider investing in advanced data solutions and programs to secure information relating to their property performance and occupant behavior. The need for the CRE industry to provide their investors and property owners organized and actionable data continues to grow, as it must have informed stakeholders who use the information from various sources to make sound decisions. 

While Part One of Addressing the Significance of Data Quality in CRE focused on the current state of data collection and quality in the commercial real estate market, Part Two will highlight the appropriate steps to improve data quality at the property level to aid in better decision making.


Topics: Data Quality, Commercial Office, Data Segmentation, Multifamily, CRE tech

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