Five ways designers and builders can use business intelligence with data they already have

Business intelligence (BI) – technologies used for data analysis and reporting – has been a trending topic in our recent work. Nearly all areas of our consulting service make use of one form of BI or another and Nate’s previous blog post highlights a few of these uses. Organizations are seeking insight about what they’re good at – or not so good at – in order to make improvements to their own processes.

In the AEC industry, many business, design, and construction processes overlap. Designing and constructing a building requires intense orchestration of multiple moving parts and increasing complexity. Tricky construction budgets, large project teams, and unique designs needing extensive coordination are all problems increasingly being handled with new software tools and data

Designing and constructing a building requires intense orchestration of multiple moving parts and increasing complexity. Business intelligence tools and methods can be used to unpack and understand the moving parts of a construction process – and take advantage of data you already maintain. 

Business intelligence tools and methods can be used to unpack and understand the moving parts of a construction process – and take advantage of data you already maintain. Many enterprise business platforms already assist in business intelligence efforts – such as analyzing financials and details regarding staffing and utilization (to make informed decisions about new hires, for example). But there are growing capabilities in other widely-adopted platforms that are ripe with analysis potential.

We’re finding that while many architecture firms and owners we work with have capable and established enterprise-grade platforms within their organization, they are not leveraging the data to drive impacts on their businesses. Currently, these platforms are tuned to perform specific functional tasks, leaving opportunities to exploit the troves of data untouched due to a lack of query and integration with other data sources already in their organization. While it’s no small task, getting internal data platforms to talk to each other in a way that provides insights and analysis is hugely valuable to any company. By connecting these established resources with other growing buckets of data – one can begin to make unexpected insights that were previously often difficult to discover (for example, “how many hours does a single RFI ‘cost’ on average?”).

Here are five ideas for better positioning business intelligence within your organization:

Use ‘journeys’ to help connect multiple data points and sources.

A ‘journey’ is a process-centric question and can be used to help  to guide how to connect multiple sources of data to answer it. Once answered, these journeys become evidence to suggest change within an organization. By defining a key number of journeys, we set the course for data exploration. This is vital for starting data efforts – without it, you will find that there are too many places to start and it will be difficult to gain traction.

A ‘journey’ is a process-centric question and can be used to help  to guide how to connect multiple sources of data to answer it.

We recently worked with a facilities owner on a business intelligence and data visualization effort. They were interested in gaining better understanding of their service worker’s efforts across campus, specifically regarding most frequent issues and trends about their buildings, equipment, and labor. They presented to us a plethora of data points surrounding more than 50,000 service requests across their campus they had logged in just the past year alone. While this was certainly overwhelming at first, we focused on developing a clear strategy by defining journeys (for example, “which piece of equipment has been subject to the most number of service requests?”)

With key data points and relationships defined, we were able to slice and manage the monstrous amount of data points we had been given to satisfy each journey. The owner is now capable to make an educated decision about the equipment that require the most amount of service time given their new insight. Stay focused on the task at hand and your data connections and explorations will expand naturally.

Use the data sources and platforms you have.

Your organization may already have established platforms that make business processes easier. In addition to the software that manages firm financials and timesheets, you may also keep track of change orders and construction administration processes in another system, and photographs of your most recent award-winning project in yet a third (I’m sure there’s more).

The next step to uplift your business intelligence efforts is to redefine the ways you access and analyze the data you already have. While merging business data from other platforms may not be a feature of any one of your systems, many have backdoor access – either by an API or direct database connections. By utilizing this, you can begin to carefully look at the data available to you in an environment that is more suitable for analysis – such as a data visualization program like Power BI, Tableau, or development libraries like D3js.


Minecart stores Revit model data in a relational database, making it available for analysis in a visualization platform amongst other data sources.

In our work with the aforementioned campus, we quickly realized that their existing platform wasn’t designed to provide analysis in an easy to use manner. We found ways to connect to their system, make connections between data, and visualize the key data points in an easy to access dashboard outside of the platform itself. Keep an open mind and think outside of the box (literally) when it comes to combining and analyzing data from familiar platforms.

 
This proof-of-concept visualized IFC model data in Unity, a lightweight and interactive 3D-viewer. This application allows for room data, category, and parameter information to be accessible outside of a production environment such as Revit.

Identify gaps and shortcoming in your data

A well defined journey can also be useful in identifying gaps in your data or among your integrations. If you can’t answer the question you are trying to ask, then it becomes important to determine what you need to do to get the missing information. For example, we have been working with an owner who has installed sensors to track utilization of their physical spaces. We worked to dynamically visualize this data in a heat map superimposed onto floor plan graphics. We discovered that the output sensor data and the room naming in the Revit model did not match. The lesson learned: when it comes to data integrity, “maintenance” and “janitor” are not the same

Data integrity issues and gaps that you have currently may be helpful clues for identifying important process change and future data opportunities.

A gap like this proves that connecting data sources can be tricky. While the fix was easy – renaming the data provided in the sensor data platform to match room names and numbers from the Revit model, the gap hadn’t been identified until the data opportunity was pursued. Here, two platforms were referencing similar data points (the janitor’s closet), but described them in different ways which posed a challenge once the data was connected. Data integrity issues and gaps that you have currently may be helpful clues for identifying important process change and future data opportunities.

Establish accountability to uplift data quality

As you begin to establish processes that lead to more robust data about your business, you may experience some push back from individuals who input or maintain the data itself. Here, a universal rule may apply: no one wants to put in the effort until it’s clear how they’ll benefit. This is where describing value becomes important.

We had the pleasure to work with an architecture firm that understood the value of data integrity across their portfolio of architecture projects. Their benchmarking efforts worked to identify how key categories and subcategories would apply to spaces across their projects over a variety of clients.  By having a clear hierarchy that positioned room and area data in their Revit models into specific themes, this firm could compare these spatial-use concepts without trouble (we helped them develop a data visualization platform to do this).

An example dashboard shows relationships between room areas across multiple projects. Data was sourced directly from Revit models using Minecart.

Drive data initiatives and the efforts behind them by focusing on the value it will provide to those involved.

They key to their success was the accountability they built into the design and documentation processes that led staff to input and validate the needed information. These members understood the value they would achieve by making sure the information was right. For example, new insights about their expertise sectors would provide insurmountable value in the design of future projects as well as uplift their ability to convince potential clients of their design intelligence. Drive data initiatives and the efforts behind them by focusing on the value it will provide to those involved.

Grow data literacy and cultivate a culture that embraces evidence.

Data literacy is the ability to understand and manipulate data and successfully interpret data to drive decisions and outcomes. For your data explorations to provide value, it must be consumed by individuals who understand how to translate the numbers into action. Furthermore, a culture of “that’s not how we’ve always done it” has already failed. Successful organizations, with healthy temperament, will see key indicators from evidence and integrate it appropriately into current processes. Over time, as iterative improvements based on evidence find success, trust will build.

Remember our client working with service requests across their campus? They’re making efforts to integrate data literacy within their organization, and they’re taking it one step at a time. First, we helped train key individuals on how to access their platforms and visualize data that responds to strategic journeys so that they are empowered to continue their own exploration. With these skill sets, over time they’ll take the evidence gleaned from trends to decision makers to shape and route future decisions about the equipment they buy and how they manage their labor (as two brief examples). By analyzing data and reviewing quick insights, they’ve already identified a few areas for improvement and are establishing the foundation for a culture that embraces evidence to support change.


Conduit, an open source tool, provides a heads up display that updates realtime as adjustments are made to a design. This workflow supports evidence as a driver for design decisions.

Moving forward with business intelligence

We’re excited about the projects we’ve been apart of focused on business intelligence. Making evidence-based decisions in design and construction of projects and about your business will make an organization better at their mission. The first step to making informed changes in your organization is collecting the data.

Efforts surrounding the use of data for business intelligence will lead the industry to develop new skills in data literacy. Firms, owners, and contractors will command concepts in research and the application of evidence as a means to improve business and design processes, maintain technological innovation, and ignite process evolution.

Think of a problem or question you have had in the last week about your work:

  • What data sources (including people) in your organization contain the information you need?
  • What connections and relationships between data sources will make this question easier to answer?
  • When you have the answer, what action do you intend to take based on the available data and evidence?


How we can help…