Using data as a career accelerator

By Jess Purcell

Everywhere you turn these days, people are talking about data. More and more, our clients expect data to inform every aspect of their business decisions, so speaking that language can help build trust and lead to better collaborations. If you’ve been in a client meeting, you’ve likely witnessed a senior designer quickly and confidently offer design solutions that lead to swift decisions. It feels like magic — sage wisdom and gut feelings based on decades of experience. For a young designer, with limited industry experience, data can enable you to make confident, informed decisions and identify the best solutions quickly without the directly acquired expertise. 

My Transition Into a Data-Focused Career

I started in a traditional architecture role right out of school, but after a couple of years, I was offered the opportunity to become a Design Technology Specialist at Shepley Bulfinch. During my first week, my boss, our CIO, wanted me to try out a new software, Power BI, and gave me an Excel export of every post from our social intranet, Finch. He had a lot of questions: Who was asking or answering questions on the platform? Was there consistent engagement across offices? Who were our experts? Whose voice wasn’t being heard? As someone who was new there, I normally wouldn’t be able to answer such in-depth questions about the culture of sharing in our company. But because I had the data to work with and group, I could arrive at the answers in my first month, without even knowing everyone.

How We Are Using Data

These days, I use data to answer my own questions and track the performance and adoption of new processes and tools we roll out.

One of the challenges we have as a large firm with multiple offices is appropriately staffing projects. Instead of having project managers request specific people for a project, we have them identify the skills they are looking for through an online form. And through a corresponding form we lovingly call a “baseball card,” we have all design staff self-identify their experience level for each skill. By pairing these two data sets, we not only identify everyone in the firm who meets the needs of a project, we also give new hires the opportunity to have their skills utilized to their full potential.

We’ve recently acquired an add-in that will pull machine, user, and model information anytime someone opens or syncs a Revit model. While we had worked with data at a project team scale, using Dynamo to extract and track model metrics, collecting data at the firm level gave us the chance to ask larger questions. As an IT department, we can now identify machines that are performing more slowly than the rest of the team on any given model and replace them. We can identify model health red flags before they become a problem, corrupting the file and losing the project team’s time and work. Recently, we’ve begun exploring how to automate adding that data back into the model via a Dynamo script that runs every night, displaying up-to-date metrics on the model message board when users open their files again.

What Data Do You Have Right Now? 

Architecture, as an industry, has yet to fully embrace leveraging its data, though not for lack of access to it. There are likely reams of data that you have access to at your firm and may have yet to consider using in decision-making, including: 

• Business-related data like project fees, construction costs, and billed hours, which can inform your future contract negotiations.
• Marketing information like projects won and lost, projects by market, repeat vs. new clients, which can give you clarity regarding what projects to go after next.
• Project information like time to completion and number of change orders can answer questions about realistic project scheduling.
• BIM information ranges from exploring your proposed vs. final program, tracking manufacturer information and quantity takeoffs, which allow you to watch the health and performance of your models.

Analyzing these metrics can allow you to confidently contribute to business discussions earlier in your career.

What Questions Are We Trying to Answer Now?

BIM, at its core, is data. And BIM automation is essentially the manipulation of that data. Using tools like Dynamo, PyRevit, and scripts we’ve developed has enabled us to automate tedious low-value tasks, including the generation of sheets and schedules and bubble diagram volumes and pulling area calculations. We’re curious about whether we can use machine learning and pump all of our previous projects’ data into an algorithm to define rules we may not be able to see or articulate ourselves as designers. This would further automate tedious tasks based on the decisions we've made in previous projects.

We're already approaching an era of smart objects, and soon our projects will be plastered by sensors. How will we as designers use that data to validate design intent and inform building decisions? If we learn how to organize that data, can we offer the service of helping our clients make the most of it as well? 

Once you start using data to reduce uncertainty and illuminate hard-to-notice patterns, you see opportunities that you would have never spotted before. This way of thinking has fundamentally changed how I look at problems, and it allows me to add more insight and value to our design practice than if I had followed a more traditional design career path.

Author bio:

Jess Purcell
Purcell is the Design Technology Manager at Shepley Bulfinch in Phoenix, Ariz. Purcell has expertise in computational design, VR, data analytics, and programming and is an active contributor and speaker in the AEC technology community.