The construction industry has been plagued by inefficiencies and sub-optimal performance for decades. Many of these problems arise from factors such as poor planning, management, budgeting and miscalculations [1]. Most of this can be blamed on old-fashioned processes and long-standing cultures that are resistant to the technological changes of the modern world [2]. In today’s technological world, data is being generated at alarming rates. Being able to understand trends and derive insights from this data allows for important business advantages, including:
- Early risk detection using predictive analysis
- Productivity measures through workforce and equipment tracking
- Real-time performance measures through data visualizations, dashboards and reporting.
Business Intelligence and Data Analytics
- Business Intelligence (BI) software, such as Microsoft Power BI or Tableau Software, Inc., are tools that allow an organization to retrieve, analyze and transform data into useful insights and visualizations [3]. The data used is produced by the business itself, rather than by outside sources. Unfortunately, this data is often stored in multiple locations from diverse business applications, most likely with different labels and categorization. This can have many negative effects:
- An incomplete picture of the business, making it harder to make informed decisions
- Potential duplication of data that can lead to inaccuracies
- Limited communication and collaboration on all business fronts
- Decreased efficiency in data capture and analysis
- For data to be analyzed effectively, it should be stored in a centralized location with a standardized format [3]. Some BI applications will pull data from the different applications and aggregate it locally. Other times, the data must first be aggregated in a common location, and then imported into the BI software. Either way, data is much easier to gather and analyze if it can be accessed via a native Application Programming Interface (API) connection or webhook [2]. These interfaces allow for one piece of software to interact and communicate with other pieces of software, and they grant access to the raw data which can be examined directly. This means that important information can easily be extracted for use in BI, and data can easily be synced across different applications, preventing inaccuracies. Applications that lack an open API make it extremely difficult to extract and compare data [2]. This often requires a direct download and then comparison using a spreadsheet software.
Determining KPIs
- Once data has been extracted and stored in a standardized format, it is straightforward to display this data using various BI tools including visualizations, dashboards and reports. These tools facilitate the determination of trends in Key Performance Indicators for construction performance analysis. A Key Performance Indicator (KPI) is a metric that informs how a business is doing. It is often confused for a measure, which is an observed value of a number at a point in time. Rather, a KPI is calculated or derived from measures and is typically described as a ratio, percentage, rate or average [4]. An effective KPI evaluates the events or decisions that led to results, as opposed to the results themselves. Properly established KPIs are key to making informed business decisions and monitoring performance.
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References
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[1]. I. Bobriakov, “Top 8 Data Science Use Cases in Construction,” ActiveWizards, 21 June 2019. [Online]. Available: https://medium.com/activewizards-machine-learning-company/top-8-data-science-use-cases-in-construction-9ce8035e936f. [Accessed 2 December 2019].
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[2]. J. Snyder, A. Menard and N. Spare, “Big Data = Big Questions for the Engineering and Construction Industry,” November 2018. [Online]. Available: https://www.fminet.com/wp-content/uploads/2018/11/FMI_BigDataReport.pdf. [Accessed 2 December 2019].
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[3]. TechnologyAdvice, “TechnologyAdvice Guide to Business Intelligence Software,” TechnologyAdvice, 25 November 2019. [Online]. Available: https://technologyadvice.com/business-intelligence/. [Accessed 2 December 2019].
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[4].P. Enhorning, “KPIs and the Logic of Decision Making,” Linkedin, 11 May 2015. [Online]. Available: https://www.linkedin.com/pulse/kpis-logic-decision-making-peder-enhorning. [Accessed 2 December 2019].