Making Construction Data Analytics Easier to Understand

Making sense of all of your data from all of your projects can feel like trying to understand a foreign language. But, when we break it down into steps, it starts to make a lot more sense. 

The recent surge in construction technology has been aimed at facilitating the collection of data to improve day-to-day workflows. However, little has been done to help automate construction data analytics to make better decisions. According to Deloitte’s 2023 State of Data Capabilities in Construction report, industry leaders are still dedicating 11.5 hours weekly to data analysis and research.

This considerable time investment highlights a significant challenge: industry leaders spend over 25% of their time analyzing data despite the tools available, meaning the data provided is not instantly understandable. The question then becomes: how can you make your project’s data into something useful?


The Challenge: Large Volumes of Data


Lots of Different Data

Construction projects generate a lot of data from many sources, such as schedules, accounting, project management systems, workforce productivity metrics, etc. There’s so much information — and so many types — that it’s nearly impossible to organize and understand it all.


Projects Always Evolving

Construction projects keep changing; things are always being adjusted. This means the tools we use to look at and understand all this information need to be able to keep up with these changes and work with the latest information right away.


Making Important Choices

Sometimes, the sheer amount of diverse information can be overwhelming. This makes it difficult to find the really important bits. As a result, decision-makers often lack immediate access to relevant data. This impedes the process of making important decisions based on evidence. Instead, it encourages the use of subjective reasoning, which might cause further delays or problems within a project.

Basically, the most important data needs to be brought to the surface so that you can understand the risks of your project without letting less important data cloud your judgment. So, the question now is, how can you use automation to take complicated data from different places, make it easy to understand cohesively, and collaborate with your team to make important decisions?


Turning Construction Data Analytics into Meaningful Insights


Step 1: Creating Standards for Data Interpretation


Why It’s Important: Standardizing how data is viewed and interpreted promotes consistency and clarity. When everyone in the company uses the same guidelines for understanding data, it eliminates confusion and enhances collaboration.


How to Do It: Creating standards involves developing clear guidelines on what data to collect, how to format it, and what it means. This could include agreeing on common definitions for key metrics, deciding on the structure of reports, and setting benchmarks for performance and quality. 


Why it Helps: Agreeing on a standard way to measure project success – whether it’s through milestones or physical completion – ensures that when different projects discuss the data, they’re comparing apples to apples. It also helps with change management (or ramp-up) when resources move from project to project if they are already trained on how to interpret their data.


Building Quality Standards with SmartPM: SmartPM helps teams ensure that every project, big or small, is checked and measured the same way. For example, by allowing teams to create custom Quality Profiles, companies pick their own way to check each schedule’s details so everything is aligned with each organization’s unique needs. This method keeps everyone on the same page and simplifies the process of sharing project data consistently.



Next Steps: After establishing a common language for data through standardization, the next step is to implement automation. This involves selecting which data points are vital for automated tracking and analysis based on the standards set. Automation can streamline data processing, but it’s crucial to identify which aspects of data collection benefit most from automation to ensure efforts are focused and effective.


Step 2: Automating Analysis


Why It’s Important: Automating the analysis of construction data speeds up the process of turning raw data into useful insights. It minimizes human error and frees up valuable time that project teams can spend on decision-making and strategic planning instead of the mundane task of data processing. 


How to Do it: Identify key data sets and processes that were standardized in the first step and implement software tools and technologies that can automatically analyze this data. This might include using AI or machine learning for pattern recognition, predicting project outcomes, or simply automating data analysis from disjointed sources into a single, understandable, and unified platform. 



Why it Helps: The project team can focus on the construction project and use the data to make decisions instead of spending countless hours analyzing it. This allows the project teams to be more proactive instead of reactive to unforeseen circumstances on projects.



Considerations: Not every piece of data needs to be automated. Data that is critical for regular decision-making or that involves complex calculations are good candidates for automation.


PRO TIP: Simplifying CPM Scheduling with Smart Tools


Critical Path Method (CPM) schedules are like a detailed guide for construction projects, showing which steps must be completed to finish on time. CPM schedules are not a static document; they continuously change throughout the entire project’s execution in response to delays, change orders, resource constraints, etc. This makes it very difficult to stay on top of your true critical path, but here’s where using smart tools to help look at your CPM schedules can make things a lot easier.

By using automation, SmartPM helps spot the really important aspects of your project quickly and makes sure they get the necessary attention. This way, projects are more likely to finish when they’re supposed to, and everyone can see the true rate of progress instead of just focusing on a few dates that can be made to tell whatever story you want. 


Next Steps: With standards established and key points being automatically analyzed, the final step focuses on turning these automated insights into reports. These reports should be designed to communicate information effectively to all stakeholders based on the insights gathered from automated analysis.


Step 3: Reporting – Standardizing Insights for Decision-Making


The final step, reporting, ties everything back to the initial standards. Reporting is about designing and constructing reports that communicate project information consistently across your organization in a format everyone can understand. 


Why It’s Important: Standardized reporting protocols mean every project’s data is presented uniformly, making it easier for everyone within the organization — from field teams to executives — to understand the project status and risk at a glance. This consistency aids in change management by requiring stakeholders to familiarize themselves with only one reporting method rather than adjusting to new formats with every project. 


How to Do it: Design reports at the company level that adhere to the established data standards. Determine the most critical data points for decision-making and highlight these in reports. Utilize software tools that allow for the customization of reports to focus on these key areas, ensuring they provide the insights needed without being cluttered with irrelevant data.

Example: A standardized project health report might include key performance indicators like planned versus actual progress, schedule performance index, and upcoming milestones. Once established, this format can be used across all projects, enabling quick information assimilation and facilitating a smoother transition for personnel moving between projects. 

By focusing on reporting that adheres to established standards and highlights essential data for decision-making, organizations can leverage their data more efficiently, making smarter, more informed decisions that drive project success.


Creating Standardized Project Control Processes with Construction Data Analytics and Technology


Making sense of all of your data from all of your projects can feel like trying to understand a foreign language. But, when we break it down into steps (similar to a construction project) – standardizing data, automating the analysis, and crafting clear reports – it starts to make a lot more sense. 

First, we make sure everyone agrees on what the data means. It’s like deciding on the rules of a game before you start playing. This helps everyone understand the information similarly, which is super important for managing projects smoothly.



Then, we bring in automation. This is like having a super smart sidekick who does all the hard work of sifting through the data for us. It can spot patterns and important bits of information without getting tired or making mistakes. This means we can have greater trust in the data because it’s consistently processed the same way every time.



Lastly, reporting is all about sharing what we’ve found in a way that makes sense to everyone. By sticking to a consistent reporting style, it’s easier for people to understand what’s going on, whether they’re starting on a new project or have been with the company for years. This makes managing changes a lot simpler since there’s no need to learn a new way of doing things for each project.



Embracing Smart Project Management with Construction Data Analytics


In summary, by setting up good standards, using automation to handle the data, and sticking to a clear way of reporting, we can make better and more informed decisions. This not only makes our lives easier, it also makes our projects run smoother and helps everyone stay on the same page – no confusion, no fuss. And the best part? It’s like getting important news delivered right to your inbox, without having to go hunting for it, so you can keep focusing on the work that matters.


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