The Power of Data in Uncertain Times

Crises of any kind create near term challenges with some carry-forward implications. A contained crisis such as a fire results, at minimum, in a pause in business activity in the interim, investment in physical restoration and probably a new insurance relationship. A global pandemic such as COVID-19 is the kind of crisis that has the added complications of many possible and seemingly indeterminate timelines and responses. This pandemic is impacting the health of people directly, it has implications for how we as humans occupy space and it would appear to have far-reaching financial ramifications.

Confronting the COVID-19 pandemic cannot be done with tunnel vision. Instead we should rely on trusted information to help us see beyond our own reactions. It is essential to use the data available to us to remain focused and effective, to guide us toward rational decision making. What follows is a framework I have seen succeed at colleges and universities. There is no reason it can’t apply to organizations outside the higher education sphere as well.

Step One: Establish a Baseline

All decision-making starts with establishing a baseline. If this is already the normal process for you, then you have a great jumping-off point.  If not, gathering the information available to you and organizing it can sometimes be a very daunting task. And if you are in the midst of a crisis and this step isn’t already done, it might seem like energy you can’t afford to spend. But it is at this point that you will uncover key elements about who you are as an organization, your strengths and areas of improvement.

With an established baseline, you’ll discover how much space you really have and how much time you spend on different kinds of work. You’ll see where your money is going and gain clarity about the size of the challenge in front of you. You’ll uncover your information gaps. You’ll examine which facilities service which programs and the level of service those programs receive. A baseline helps you put perceived truths under a microscope and square them with facts.

The baseline serves as the point from which you measure what has truly changed, what has stayed the same and what might need to change going forward.  Making decisions without an established baseline means you will be speculating and working from your gut.  Such an approach is unlikely to serve you well.

Step Two: Assess Facilities Performance

Having baseline information allows you to assess how your facilities have performed in the past.  You’ll know when energy use increased or decreased and what service levels have looked like over time. You’ll have troves of data regarding workloads, staffing levels, project delivery and, of course, funding.  You’ll also have the ability to look at data points in the context of each other.  What did staffing changes do to service levels?  How has project funding affected the backlog of needs?  How have maintenance expenditures affected energy consumption?  What impact did changes to capital investment after the Great Recession have on the condition of facilities overall and the amount of space that now requires care and what strains were placed on budgets overall?  Ideally you also know how you look benchmarked to yourself over time as well as to peers.  This data informs how well you are meeting the needs of your customers, but you must also assess how well-positioned you are to handle change today.

An assessment uncovers whether you have a surplus of any resources (space, people, even capital) or if you are operating on a knife’s edge.  It tells whether and where you possess resiliency and where you do not. No organization will be untouched by the future implications of a crisis, but some will be positioned to respond better than others.

Step Three: Compare Models and Make Predictions

Facilities will inevitably need to respond to the business activities that your campus community selects going forward, but they should also inform that activity through modeling of various scenarios. And with solid baseline data and performance information, effective modeling is attainable. It is possible to predict what the costs will be for enhanced cleaning methods, to identify service reductions if furloughs or layoffs are being considered or even to understand the implications of near-term capital expenditure cuts on long-term capital outlay and overall facilities conditions. Some of these costs will be intuitive, while others will be surprising.

More intense cleaning of existing spaces might logically require more people and materials costs. But it might not be intuitively obvious to everyone that even if you reduce the number of people using a space, the level of effort and costs to clean that space may still be higher than it was before the pandemic.  Increasing ventilation rates on spaces would increase costs regardless of the number of occupants.  Decisions to suspend study abroad programs may have unintended housing and food consequences that could represent significant operational and capital implications.

Keeping a college virtual with limited on-campus activity will also have complicated operational costs and demands to continue to keep the property ready for a return to normal, particularly if those conditions are maintained for an extended period of time and carry into parts of the country where winters are harsh. Modeling these kinds of costs is crucial to linking near-term decision making to long-term consequences.

Predictive models based on real data and tied to historical and peer performance provide credible reference points from which to make decisions.

Step Four: Rational Decision-making

Rational decision-making is impacted most by three factors: timing, available information and healthy communication.  It is critical that short and long-term models are communicated while institutional leadership is making choices, not afterward. For any project, costs of changes are always lower when done in the planning phase. Once an institution makes a choice about the direction it wants to move in response to a given crisis, the challenge and cost to shift that decision is greatly increased, and the likelihood for change is greatly reduced.

In this inverse relationship, information received late is usually information unused.  Examining data-driven models while options are being considered is crucial to informing the planning process and reducing preventable consequences.

All of this presumes that there is quality dialogue among people who are interested in hearing each other and speaking a common language. It is critical that the knowledge developed through this process is assembled in language that everyone can understand, language that is free of jargon and connected to issues of important to the entire community, and that there is an opportunity for such language to be heard.

Good Data is the Basis for Good Decisions in Uncertain Times

Utilizing models rooted in trusted data that help drive rational decision-making is particularly important in times of crisis. For even the most experienced teams, the tunnel vision that arises in states of crisis as people look to address the immediate problems can lead to poorly considered and uninformed decisions.  Predictive models based on real data and tied to historical and peer performance provide credible reference points from which to make decisions.  This information moves conversations from crisis reactions to disciplined crisis response plans. Linking short-term decisions to long-term consequences will drive the institution toward recognizing and realizing the best of all possible outcomes.