Tight margins and deadlines, complex projects, price increases, and unexpected challenges–that’s the construction industry in a nutshell. Effective construction planning tackles these aspects head-on so you can deliver projects on time and within budget. And stick to the highest quality standards.
But, traditional methods depend more on experience and intuition. Unfortunately, that means bias and inaccuracies can easily creep in. On the flip side, because project management analytics takes a data-driven approach, it introduces informed decisions and precise planning.
What do you stand to gain? Short answer: better project outcomes and consistent profits. This article explores project management data analytics and how to leverage it to improve your strategies and bottom line.
What is Project Management Analytics?
In simple terms, it is applying data analytics in project management.
The construction industry is data-intensive. In fact, according to MarkWide Research, big data’s value in this market should reach $10.8 billion by 2025. There is everything from performance data to benchmarks, market trends, feedback, resource data, and even demand gen information.
Delivering successful construction projects depends, to a large extent, on understanding past performance and keeping up with trends. Project data analytics comes in to help you make sense of the numbers so you can make better business decisions.
Here are some relevant use cases:
- Warranty and risk analysis.
- Subcontractor performance evaluation.
- Budgeting/Cost estimation.
- Quality control/defect detection.
- Schedule optimization.
- Equipment management.
- Streamlined workflows.
- Risk analysis.
- Detection of human errors.
How Does it Work?
1. Data Collection: Project teams track materials, equipment, subcontractors, budgets, feedback, timelines, quality benchmarks, and more. All that information is collected from project management software, financial records, IoT sensors, and customer service channels.
2. Data Analysis: Next, you analyze the collected data to unlock its potential. This stage could involve:
- Data visualization.
- Predictive modeling.
- Risk modeling, etc.
3. Make Data-Driven Decisions: Analyzing data is only useful if you act on it. You want to use the insights gleaned from the second stage to inform your decisions in the planning and execution stages.
The Benefits of Project Management Data Analytics for Construction
More and more business people and managers, including those in the construction industry, are relying on data for their decision-making.
That means the more information you collect (and analyze), the more effective ideas, tactics, strategies, and tests you can infuse into your construction planning for it to succeed.
Let’s take a look at the practical ways data analytics for project management can improve the strategic decisions for your construction plans and boost your profits:
Better Cost Estimation and Control
Ever seen a construction project where the final bill matches the initial estimate? In the construction business, that’s rare. Still, miscalculations and inaccuracies can mean you endure significant cost overruns and delays. It can even result in the project failing.
Sure, cost estimation is nothing new. Traditional methods rely on gut feeling and historical data that may be dusty. Project data analytics, on the other hand, throws predictive models into the mix for enhanced accuracy, so you save more money.
On top of that, thanks to real-time expense monitoring in the execution stage, you can keep an eye on where the money is going.
Improved Resource Allocation
There’s a lot of juggling when planning a construction project without analytics. You’d have to estimate the labor needs, manage your finances, ensure you source the suitable materials and equipment — and have everything in the right place at the right time. It’s easy to see how messing this up can delay you or even grind your project to a halt.
Thankfully, using analysis for resource planning allows you to streamline this. For instance, a project manager estimates that a crew of five installers can efficiently handle 2,000 square feet daily on your long-term project.
Great. But what if analyzing past project data shows that four workers could complete the task just as well without overworking them? Or, conversely, can complications like uneven walls or complex layouts throw the time estimate off wildly?
Improved Risk Management and Mitigation
Construction projects are inherently risky. Everyone knows that. Anything from an unpredicted hurricane to a supply delay can send your timeline and project budget spiraling.
Effective project management demands you consider potential risks. Still, without analytics, you usually react to problems as they pop up.
Data analytics flips that switch. Will you automatically have every risk planned for and under control? Unlikely. Still, you can quickly identify and mitigate the most likely threats before they become real.
Better Project Scheduling
While SMART project goals are beneficial, many schedules are overly optimistic. Throw in the inevitable delays and project demands, and you have serious cost overruns and ticked-off clients. Analytics can help you create more realistic and achievable timelines.
How? Analyzing historical data can pinpoint areas where delays typically occur in a project life cycle. For example, it could reveal that you should expect issues with fire code compliance in your high-rise construction project.
But it goes beyond that — project managers can even optimize the sequencing of your tasks. You can avoid unnecessary downtime if you can identify the most efficient order for one phase to follow the other.
Streamlined Communication and Collaboration
There is no way to effectively plan when there’s a communication gap. Imagine everyone using different platforms to discuss, share files, and collaborate. Achieving project success is easier when everyone’s on the same page.
Project management analytics bridges that chaotic gap between teams and fosters stakeholder engagement.
Here’s the idea: the various parties can use and contribute to a centralized platform. That way, they all have real-time access to project data for better and more informed decisions.
It is also better for team collaboration. For example, separate teams can work on HVAC ducts and electrical conduit installation. Without that central hub, there could be conflicts during planning that don’t pop up until construction begins — and that could mean costly rework on-site.
Agile Construction
In agile project management, the goal is to deliver high-quality project outcomes quickly and efficiently while adapting to changes. And data-driven decision-making can make your planning all the more flexible and responsive to changes and customer feedback.
Data analytics provides real-time, actionable insights into your project progress. As discussed so far, it covers everything from risk assessment to costs, resource allocation, and even communication.
Say you’re applying a version of the Agile framework and breaking your project into smaller tasks. Project data analytics helps you to:
- Spot and deal with risks early on
- Optimally allocate resources and workers for each phase of the project
- Quickly identify areas for improvement
- Be flexible to changes
Tips for Leveraging Project Management Analytics
Collect Quality Data
Garbage in, misguided decisions out. Web traffic data is relevant for lead generation for B2B, but it hardly applies to your construction plan. You want to collect high-quality, accurate, complete data throughout the project life cycle for the best results.
Here’s what to do:
- Use consistent methods to collect the data.
- Validate your data sources. They must be credible.
- Review and update your data so it stays relevant.
Measure Your Performance Metrics
Your project metrics or key performance indicators (KPIs) help you see how you’re doing and where you can improve.
There are loads of them, but thankfully, tools for advanced analytics can monitor them. There’s no one-size-fits-all approach, and what you need depends on your project scope and size.
Here are some KPIs to keep an eye on:
- Risk: risk exposure, average time to resolve, probability of occurrence.
- Cost management: total budget v. actual cost, cost performance index.
- Quality: cost of quality, defect rate, customer satisfaction index.
- Team performance: velocity, cycle time.
- Time: task completion rate, actual time v. estimated time.
Have Clear Responsibilities and Easy Access
When there’s ambiguity regarding construction data management, you’re asking for costly mistakes. You probably didn’t need to create a separate role for it, but there should be clear ownership and responsibility for data.
A data governance plan is your playbook. It ensures integrity by outlining protocols for data collection, storage, security, and access.
Make sure data is easily accessible to those who need it. Most modern platforms are cloud-based these days, for good reason.
Invest in the Right Tools
Business Analytics and Business Intelligence software are the tools of the trade. You want to invest in a reliable option that can handle extracting and analyzing the data collected for your project.
Your ideal project management software goes beyond basic task management and scheduling. It has user-friendly, interactive dashboards and can generate rich, dynamic reports. But you don’t have to wait for detailed reports to make decisions, thanks to the real-time monitoring of project metrics.
Final Thoughts
Project management analytics can radically improve your construction planning so you can handle risks and meet deadlines, budget estimates, and stakeholder expectations.
Change is constant in the construction industry, and you cannot afford a static plan. Make sure you embrace continuous development to refine your decision-making process even better. Your bottom line will thank you for it.