The CDO Stack: Help From Technology

Here are strategies to use when operations are strained by staffing or workload issues.

John D'Angelo
John D’Angelo

Last month, I talked about the great resignation and how I’ve seen it impacting the operations of several companies in our industry. While I understand this isn’t just an issue impacting operations, that’s where I spend a big chunk of my time. And at the time, I was thinking about the ways in which technology might help if your operations are strained or failing because of staffing or workload issues. Specifically, if you don’t have any available staff and your existing folks are at their breaking point.

In talking with several companies over the last few weeks and having had the opportunity to speak with many of you at recent industry events (in person, joyously), I realized some of the technology opportunities that have obvious applications to me might not be equally obvious to everyone else. So, apologies if this is basic information, but I figured it could be helpful to make sure you’re aware of what’s possible.

For our purposes, I’m going to focus on robotic process automation (RPA) and machine learning (ML) because I think these two technologies are friends that can help automate some of the person-intensive, but rule-based, repetitive tasks that make up so much of the work happening in our industry.

Just so we’re clear, I’m not keen to see people displaced, but I am keen to see the work that people perform shift to more valuable applications. Applying RPA and ML promises to reduce immediate issues caused by the great resignation, and in the mid and long-term, it promises to free up resources to do better or more valuable work that process robotics (bots) and artificial intelligence either cannot or should not perform.

The RPA and ML experience

Starting with RPA or bots, think of this as a machine that follows instructions, doesn’t get tired, doesn’t get bored and is willing and often able to work around the clock, seven days a week. If you’ve programmed macros in a spreadsheet, you’re very close to having had an RPA experience.

RPA is basically one step up the evolutionary or skills ladder from macros. One big thing that sets RPA apart from macros is that it can cross systems and websites, taking input from one and moving it to another. Bots can log into systems, be triggered by events, follow basic logic and complete their task over and over, relentlessly.

How is this useful? Think of the tasks that people are completing in your processes today, which are necessary to run the business, but which are repetitive and rules-based. In other words, the tasks must be done with care, but they don’t require judgment… just following the rules.

If you’re wondering where to start, while you’re thinking of tasks or processes that fit this description, take the next step to write them down, prioritize them according to volume, complexity and value, and use that evaluation to build a business case.

Next, pick out one or more high-priority opportunities to build, test and deploy. Keep in mind that this simple set of activities sometimes requires patience and tenacity. You’re not likely to get it exactly right on the first try, so sticking with it is important.

I hate to leave you hanging, but we’ll cover machine learning or cognitive automation (one step up the ladder of capability and sophistication from RPA) next month. In the meantime, if you have people re-keying data from one system or spreadsheet to another (and I know just how common this is in our industry), please spend some time looking at how bots can take over that task.

I’ve barely scratched the surface of RPA in this month’s column but stay tuned and we’ll talk about how ML and cognitive automation can help extract data from documents next.

John D’Angelo is a managing director with Deloitte Consulting and leads the real estate industry sector for Deloitte Consulting in the US. With over 33 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.

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