Utilizing Data Automation and Visualization in Project Controls
Project Controls can be defined as the process whereby actual performance is compared to planned performance to analyze variances, monitor trends, and ultimately take necessary action to meet project goals.
The process of project control is practiced in virtually every industry from food service to sports and entertainment. For example, Chick-Fil-A standardizing two drive-thru lanes with an attendant in each lane to achieve the quickest average turnaround time for drive-thru orders despite having nearly twice the number of cars of its competitors is the result of data analytics.[1] Data Analytics in the NBA is the reason teams attempt on average about twice as many 3-pointers as they did 20 years ago.[2] This has resulted in a faster, more exciting pace, millions of new fans, and billions of dollars in revenue for the league. In both examples, the success of these organizations’ control efforts is directly tied to their ability to analyze large data sets and take the necessary action to achieve their goals.
In the construction industry, tools such as Primavera P6, MS Project, Procore, and MS Excel are commonly used to gather and analyze cost, quality, and schedule data. While these tools are effective monitoring solutions, by themselves, they are limited in their capacity to efficiently shape, model, and visualize data in a way that is easily comprehensible to a variety of audiences. It is for this reason that the leading construction and development organizations are investing in business intelligence software such as PowerBI and Tableau. This report will discuss some of the benefits of implementing data automation and visualization tools in construction project controls.
Improving Project Efficiency and Performance:
The efficiency of the project team often reflects the performance of a project. According to a global construction survey performed by KPMG International, just 25% of projects came within 10% of their original deadlines and 31% of projects came within 10% of their original budgets.[3] Of course, several contributing factors may lead to the failure of a project to meet its schedule and budget goals. However, it is certain that detailed and thorough project control practices help to mitigate or even prevent risk events that lead to budget and schedule overruns.
Typically, project controllers are responsible for collecting data from multiple sources such as field notes, pay applications, and external software. Next, this data must be aggregated and collated before reports are finally produced. This process is repeated iteratively over the course of a project. Consequently, an exorbitant amount of time spent entering data often leaves reporting vulnerable to human error.
Teams that utilize data automation and visualization tools adopt a data reporting workflow that eliminates the need for redundant data entry and instead gives users the ability to link data sources like P6 or Excel to software like PowerBI.
Establishing this workflow requires:
· Mapping out data sources to identify cloud and on-premises data sources.
· Establishing an organizational breakdown structure.
· Identifying who is responsible for entering data at each level.
· Defining a schedule to determine when data will be gathered and reported.
Ultimately this workflow gives project controllers the ability to provide a more accurate picture of a project’s progress.
By adopting an efficient workflow in conjunction with utilizing data automation and visualization tools, teams spend less time reviewing and correcting data, and more time analyzing data to make informed decisions in the field.
Enhanced Decision-Making:
Implementing Business Intelligence tools gives project controllers the ability to develop customizable and comprehensive visual aids to inform decision-making on projects. As project controllers, it is our job to take complex data and present it to both executives and field personnel in a manner that is appropriate for its intended audience. For example, a project executive might only need to view a program's monthly Earned Value metrics to perform descriptive and predictive analysis. With this information, he or she will be able to look at past performance as well as predict future performance. On the other hand, a project manager may need to perform diagnostic and prescriptive analysis to know exactly why the critical path for a project has slipped, how much that slippage is affecting revenue goals, and what corrective action to take. Data visualization software provides the solution to this challenge by enabling project controllers to take data sets and create interactive dashboards that give stakeholders the ability to view the same information from varying perspectives.
Implementing data automation and visualization solutions to help streamline data management, enhance decision-making capabilities, and improve project performance will require change management strategies. These strategies include redefining standard operating procedures, implementing software updates, and training staff.
As the construction industry adapts to a climate where technology continues to play a larger role in the success of projects, adding resources and software to a team to facilitate data-driven decision-making is well worth the investment.
[1] Business Insider. (2023, May 31). Chick-fil-A has the slowest drive-thrus and the longest lines, while KFC and McDonald's are fastest, according to a new survey. Retrieved from https://www.businessinsider.com/chick-fil-a-slowest-drive-thrus-long-lines-kfc-mcdonalds-2022-10
[2] The Analyst. (2023, May 31). How Advanced Analytics Have Changed Basketball. Retrieved from https://theanalyst.com/na/2021/04/how-advanced-analytics-have-changed-basketball/
[3] KPMG. (2023, May 31). Global Construction Survey 2015 [PDF]. Retrieved from https://assets.kpmg.com/content/dam/kpmg/pdf/2015/04/global-construction-survey-2015.pdf