Seeing assets clearly: Data visualization helps master planning
February 15, 2022
February 15, 2022
Technology can revolutionize management of complex real estate portfolios
Coming out of the pandemic, many large institutions are taking a new look at their assets. New technology and safety measures as well as trends in remote work, distance education, and telehealth are likely changing the volume and quality of space they require. They’re thinking, “We have all this space. What are we doing with it now? What’s our plan going forward?” They’re reassessing their master plans.
They need to choose a path for the next 10 or 20 years, a new master plan. They will need to make decisions about projects such as rehabbing the floor of an existing building, relocating a department, building a new facility, acquiring more land, and so on.
First, these institutions need an accurate picture of what they have and how they’re using it. However, few have such a complete picture. They may have data but it’s not organized in a way they can easily understand. Worse, it’s often siloed in different departments that don’t speak the same language or share similar metrics.
The real estate group can share what they were leasing, the billing department has revenue information, the facilities managers may have plans for buildings under construction and so on. The information varies in detail. For example, an institution may have more detailed info about the space it leases (which must be justified in annual budgets) than the property it owns. Decision makers don’t have a full picture.
Clients at large institutions need the ability to see relevant data quickly to allow for effective decision making on their long-term (10 to 20 years) capital plans. Their planning departments are not equipped to effectively gather and synthesize this data for their leadership.
As designers and master planners, we encounter this dilemma when we are hired to devise a master plan. The information we need often hasn’t been gathered or organized to be useful. Therefore, we’re developing our own digital services around data visualization for institutional planning, for use in master plans, capital forecasting, and much more.
As designers, we evaluate data to understand it, then synthesize it so we can come up with solutions and illustrate or diagram those solutions. The tools for processing and illustrating the data are rapidly advancing in sophistication and power. Where once we might have drawn architectural diagrams by hand from an Excel document, we are now using applications like Power BI and dynamic programs that can read our data and auto generate shapes from scripts we control.
Today, it’s possible to organize the data in such a way that makes the tasks of evaluating, synthesizing, and illustrating the data more powerful, accurate, and useful than what we’ve done in the past.
There's an increasing appreciation of the value of data among our clients. But data needs to be shaped, analyzed, and represented to tell a story that’s meaningful.
Clients at large institutions need the ability to see relevant data quickly to allow for effective decision making on their long-term capital plans.
The tools are changing but data visualization for planning is a process, an approach—not an app. We use a series of data and visualization tools. Our approach is to organize, evaluate, and illustrate the data. While we’ve automated aspects of the visualization, designers are still at the center of it. We start with the data available to us, take it as far as we can, perhaps asking for more along the way or showing the client where the gaps are.
In this way, we find better data to tell a richer story.
We also must tune the visual storytelling to the audience whether it be healthcare decision makers, higher education campus planners, owners, developers, or an architecture and landscape design team. We often want to show the data using the client’s hierarchy and organizational language so that the design team can communicate with the client. In addition, we can imbue categories of data—say for areas of inpatient, outpatient, diagnosis, treatment, and support in a healthcare campus—as another dimension in our data model.
As designers/planners we further enrich the data with our own dimensions, which is valuable to us in our design process but ultimately to the client, too. It’s all about making the conversation with the client easier.
A recent case study revealed that this approach has value. We can take a complex data set and represent it visually to facilitate the conversation about planning. In this case, we started with spreadsheets, PDFs, and plans. We started to develop a data model, which we then shared with our client (an institution with numerous owned and leased real estate assets and current building projects). Our client helped us identify the gaps. Finding and choosing which data to use is a key aspect of this work. As we created new versions of the data model, we acquired better data (finishing with three times the volume from where we began) and built a clear picture.
Then we told the story in different ways, most notably as buildings with colored three-dimensional portions representing usage. We used Dynamo, a scripting product to generate the architectural diagrams from the data model. But the diagrams aren’t meant to be literal. They are useful because they represent large, diverse portfolios of buildings and space. We added a level of abstraction and aggregation of the data to make the visual easy to understand and to move forward the planning conversation.
Today, Stantec’s data visualization teams are using this method to help clients organize, evaluate, and illustrate data and create long-term plans for their healthcare and higher-ed facilities.
Planners for large and complex portfolios of space in education, healthcare, government, industrial, and other markets need to understand their spaces. Data has value, we know this based on the market capitalizations of companies that amass vast quantities of data. Soon, the design industry’s offerings will not be counted from the hours we put into executing a project but in the data we generate and make useful for our clients.