Making Data-Driven Decisions in Preconstruction

In the construction industry, project teams have to make hundreds of decisions a day. The basis for these decisions is often past experience, knowledge, and a bit of gut instinct. While this can lead to making good decisions, it can also lead to bad ones. That’s where data driven decision-making comes into play.

Data driven decision-making (DDDM) involves making decisions based on data, rather than instinct
or biases (even hidden ones). The construction industry as a whole has been collecting data for years,
so there’s no shortage there. The problem is that this data is often siloed in different software programs and systems, so there’s no way to aggregate it and share it collectively with the team. The rise of ERP and all-in-one software systems has helped to alleviate these silos, making it easier for teams to share information.

We’re going to take a brief look at the steps in the DDDM process and some of the advantages for construction companies that use this process on a regular basis.

What is data driven decision management?

A system based on data driven decision management values decisions made based on verifiable data. Data driven decision-making tools include software to compile the data and dashboards to display it and help team members interpret it. Switching to a model where data drives decisions is often undertaken
to gain a competitive advantage, as it often leads to more efficient processes.

What are the four steps of data driven decision-making?

1. Define goals. Before you start mining through your data, you need to develop business goals that you want to achieve. Usually these are related to key performance indicators (KPIs) that will improve the efficiency and profitability of your company.
2. Collect and analyze data. You’ll need to develop metrics to help you measure your performance. These are often based on the KPIs you identified in step one. You can use these metrics to find out how your company compares to others of similar size and to industry standards.
3. Take action. Based on the information you gained during the analysis, you can take specific steps
   to help you meet your business goals. This may include refining processes or changing the way you manage your crews.
4. Review results. Once you start making improvements, you must continually monitor your performance. This will help you spot trends and identify future goals.

What is an example of data driven decision-making in construction?

A good example of DDDM in the construction industry is the use of building information modeling (BIM)
in preconstruction planning. Teams use the software to model the building and analyze the sequence and timing of the work. This allows teams to discover clashes, coordination issues, and missing information without putting a shovel in the ground. At this stage, changes to the design are much less expensive than they are once construction has begun.

Five advantages of data driven decision-making

There are many benefits of data driven decision-making for construction companies.

1. Your team can better understand the basis for decisions and the transparency shows that they aren’t made arbitrarily.
2. You set and achieve your business goals.
3. The team is always looking for new efficiencies and your processes are always improving.
4. There’s a sense of ownership in the decision-making process.
5. DDDM is proactive, so you can get ahead of potential issues and can even prevent them.

Construction teams that use this process are already seeing improvement in their efficiency and profitability. With modern technology and improvements in software, these tools are now available
to smaller firms who want to grow and improve their performance.