The CDO Stack: Pouring a Data Foundation in CRE

Last month, we talked about AI and analytics in real estate, and I mentioned that having a data foundation is a critical element in performing analytics. This month we’ll further explore what it takes to stand up a foundation of data and the work typically involved.

John D’Angelo

Apologies if you’re tired of me pointing this out, but there are few places where our industry has bigger shortcomings than in data. The challenge—or opportunity, depending on how you look at it—is threefold:

• First, while there are industry bodies focused on defining data standards for real estate, there are no commonly accepted, widely adopted data standards in real estate.
• Second, data governance isn’t as mature or common in real estate as it needs to be.
• Finally, data is typically found in multiple siloes, captured by function, and very often duplicated across multiple siloes.

Before we go much deeper in how to overcome these challenges, the single biggest key to success in establishing a data foundation is having a leadership team who believes that data infrastructure is important and worth the time, effort and resources required to build and maintain. Without this, odds are terrible that data problems get solved. 

Okay, so let’s say you’re a leader or have a leadership team who is there. Great. Now what?

With respect to data standards, the best place to start is to think about what analytics you want to be able to perform and work backwards to identify the data sets that are required to fuel those analytics. I find that identifying and defining a good set of analytics is best as an iterative process. In other words, don’t expect to sit down in one session and identify a fulsome starter set. 

I also find it’s easiest to start with a question mentality—in other words, what questions would you like to ‘ask’ the data and whose answers would lead to the most valuable and/or potentially valuable actions.

Once you do this, determine how valuable having the answer would be, how actionable that answer is, how much you would trust the answer, and what data is required (pro tip – start with classes / “buckets” of data, rather than individual data elements).

Now sort the questions and analytics you’ve identified according to your priorities, look at what required data sets the high priority questions have in common, and you’ll find you have your list of starter foundational data.

Trust me on this, don’t try to do everything at once. Defining data standards can be tedious detailed work that’ll seriously try your patience. If you start with the data that is most important, and go in phases, you’ll be far less at risk for data project fatigue. 

Now that you have data definitions, a data governance function and further resources will be important to get the data in good shape and keep it that way. Depending on the size of your company and the magnitude of the task, this function can range from a single person who is partially responsible, to a whole team of fully dedicated people. The important thing to remember is that efficient effort by a single person or team to keep data clean can eliminate a tremendous amount of fractional effort hidden across dozens of workstreams.

Finally, data duplication can be a sticky problem that represents real enterprise risk when the wrong set of dated or flawed data is used. It’s fine for data to be duplicated, but it’s not fine to lack clarity about which data set is the “golden source”. In the steps above, once you identify the data that’s critical, it’s equally important to identify who in the organization owns that data and its golden source. Your data governance function should then keep track of who owns what and, ideally, how, where and why that data is duplicated.

John D’Angelo is a managing director with Deloitte and is the Firm’s real estate solutions leader, designing solutions to address client challenges and push the industry forward. With over 30 years of experience as a management consultant to the global real estate industry, John has helped some of the biggest names in real estate leverage technology and use data to optimize and transform their operations.

Read the April 2022 issue of CPE.