You have data. Here’s what to do with it
On March 11, 2020 by Karthik Venkatasubramanian, Oracle
Karthik Venkatasubramanian, Vice President of data and analytics at Oracle Construction and Engineering, discusses how construction businesses can take the data they have and apply it to different projects.
Data continues to fascinate the construction industry. Where other industries are more mature in terms of their ‘big data’ journey, in construction it is still gaining traction. The industry is often considered as behind in its digitisation journey, but in this weakness lies a significant strength: the ability to innovate like never before. And the opportunities presented by technology are starting to justify the cost more than ever.
In the Middle East, data was often not considered in the construction phase in the past. Now, project owners are seeing the negative results of that, including large increases in the cost of maintenance projects because the data was not maintained to start with and not created in the right format. The consequences are high. When you consider the number of mega or giga-projects in the region, as well as their complexity, starting the data strategy the right way, is crucial.
We’re at a stage where it’s challenging to understand how construction and engineering companies managed without what we now consider ‘big data,’ particularly considering the level of control, transparency, threat awareness, and accountability it provides.
Today, the Middle East construction industry appears data hungry, with companies keen to know what data science – including artificial intelligence (AI) or machine learning (ML) – can do for them.
This clamour for innovative data solutions invites some obvious questions: Have construction businesses in the Middle East ignored the data they already have? How can they capitalise on the sheer volume of data they want? Can they even cope with more data? Is more data really the solution?
Before AI, ML, and building information modelling (BIM) methodology became widely understood, projects were being delivered successfully; just look at what the Mesopotamians and Egyptians achieved.
Many construction businesses in the Middle East already have data available to them collected over a number of years without them realising. It might be sitting in rudimentary systems or spreadsheets but it often exists from previous projects and activities.
Typically, just using data from any sort of system can help a business to leap ahead of where they were. It might not be integrated, clean, or perfect, but there’s still value in it. The real trick is in finding out how to extract that value.
Capitalising on data
The art of deriving value from data often starts as a three-step process:
- Define the goals and frame the questions: Being clear about goals and questions is vital, i.e., are you trying to improve process turnaround or better schedule performance? Is cost-blowout a specific concern or risk? Each of these questions will need to be tackled on its own based on the underlying source systems and data collected.
- Identify the data and analyse: If you are trying to answer scheduling questions, you need raw scheduling information. This will help to answer questions around the quality of the schedule such as how accurate it was, whether the accuracy of the duration was correct, whether the schedule needed to be reconfigured, etc. At the end of this analysis process, you’ve gained hindsight for future use.
- Apply the analysis to current projects: This is vital as it enables change to happen. For example, if the analysis reveals that hanging drywalls for certain types of projects by a specific sub-contractor has typically taken 30% more time than allocated, the next project should change either the time allocated, how the drywalls are hung or the sub-contractor employed. Using hindsight to gain insight that drives change is where the value of using data lies.
What about AI, ML, and predictive?
Once data has been collected, analysed and applied, it can be used as a training dataset to predict future outcomes. For example, an AI driven algorithm can automatically predict schedule delays in hanging drywalls on new projects using historical data and creating models that are based on a set of features (actual duration, project type, historical activity variance, etc.).
The accuracy of the prediction gets better over time through learning from user input and new data that becomes available. This is where hindsight leads to the creation of insights, where models are built to gain foresight. Each of these steps can be independent and operators don’t need ML to generate and use insights – any analytics tool can do the job.
What about augmented reality (AR), virtual reality (VR), Internet of Things (IoT), etc?
There is significant value in these technologies to provide real-time, deep level knowledge about a project both onsite and off. However, it’s important to understand that they would have to be used on a whole project before the value of the data captured could be recognised. There needs to be a baseline established. For the mega or giga-projects in the region, this could take many years.
The integration of these new technologies is not about creating isolated data sources. They need to be integrated and contextualised into an organisation’s current data flow. New technology innovations add to the data pool that is already available, enabling an organisation to gather richer, deeper insights. Eventually, all of this data will feed into gaining greater insights and driving better predictive outcomes.
Where does the data journey begin?
For many construction businesses in the Middle East, the data journey has already begun. To see the value in a data approach, they need to explore what data they have and identify what they need the data to tell them by creating a data strategy.
Before it considers AR, VR, IoT, sensors, etc., a business should ask whether it can use the data it currently has. If not, it’s just adding to the complicated pool of information that already exists.
Either way, the key is to start from within; see what you have and grow your data approach from there. As with any large, complex undertaking, your data strategy needs a solid starting point. The insights will follow.
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