Unlocking data insights to improve project planning and delivery: Four key steps

BY: Karthik VenkatasubramanianVP, DATA SCIENCE & ANALYTICS, ORACLE CONSTRUCTION & ENGINEERING

To say that we are creating a lot of data these days would be an understatement. It was hard to imagine why we would ever need terabytes or petabytes twenty years ago; today we’re racing towards even more exotic sounding names like zettabyte and yottabyte.

Engineering and construction (E&C) has played a huge role in this data accumulation—so much so that data visualization has moved on from reports and dashboards to art.

The opportunity to leverage this data to make a tangible difference to project delivery is huge, particularly given the siloed nature of construction ecosystems. This is even more apparent given new social distancing protocols on-site, supply chain disruptions, funding risks, productivity dip, delay claims, etc.

The industry has been brainstorming clever ideas and solutions to keep jobs ticking. Much of this is a result of what I refer to as the “ation” revolution.

The “ation” revolution

This term is my attempt to label the changes that are happening across the industry. There is some form of “ation-ing” happening all the time—standardization, prefabrication, modularization, integration, automation, and systematization, and so on.

What is even more interesting is that these developments lead to other “ations” that lay the bedrock for true transformation. For example, digitization leads to datafication, driving innovation and disruption. A lot of technology is used on projects depending on the problems being solved.

The key is to have a rough map that shows where you are and where you are headed. And much of the journey will happen in four key phases: Flan, Learn, Apply, and Predict.

1. “Flan”: Define the problem and leverage data

I created this word to indicate the need for a “flexible plan”; a plan that can respond to the business realities and ever-changing project landscape.

The “flan” step includes having a set plan to address what business problems need to be solved, what data you have, where the data exists, and how to use it. More importantly “flan” is about defining the analytics journey. This starts with learning from past data, applying past data to current projects and pursuits, and using this collective knowledge to predict the future.

2. Learn: Accessing historical project data

There aren’t many organisations that lack data from historical projects. The data might be in a system, in an Excel file, or in an archive somewhere. But, if the data exists, something can be done with it—depending on the quantity, quality, recency, and applicability of the data to the business problem you are trying to solve.

The learn step answers such questions as: “How long do processes take on average?” “Who are our best and worst performing subs?” “What activities have typically been delayed in the past?”, etc.

These questions help define baselines, benchmarks, key performance indicators (KPIs), and standards. Historical data is a treasure trove that offers an excellent starting point. Once you have extracted the insights from this data, it’s time to apply it to the present.

3. Apply: Reviewing historical insights and predicting future outcomes

Applying historical insights (often called hindsight) to current projects for estimating can include asking the following questions: “Are the estimates built right?” “What did we learn from the past?” “Have we constructed the schedule right?” and, “Are these the best partners to work with for the job?”

These can be extended into contracts to ensure that the agreed upon metrics and KPIs are aligned with what has traditionally happened on jobs of this type. When projects are up and running, these same metrics can be used to compare against historical averages and benchmarks to see what’s anomalous and to pick up signals for impending issues.

Of course, this is when you are also ideally placed to forecast and predict future outcomes.

4. Predict: Schedule, budget, quality, safety, and risk

The final leg of the data journey—and the ideal outcome—is to predict things the industry cares about: schedule, budget, quality, safety, and risk. You can truly transform the business by replacing lag indicators (looking in the rear-view mirror) with lead indicators (looking ahead) including: probability of delay on your projects, amount of predicted delay, likelihood (and severity) of a cost blow-out, and hidden risks within your project around safety, design, rework, and litigation.

The true power of big data emerges with some fancy sounding terms you’ve probably heard, ranging from regression, decisioning trees, random forest, natural language processing (NLP), and the ever-popular neutral networks. However, prediction accuracy as a way to measure value should not be the only indicator.

Accuracy and other measures used in data science, like recall, precision, and F1 (the ways in which the accuracy of a prediction model is measured) help you navigate the data journey in the quest to improve the prediction of project outcomes.

Change is perhaps the hardest step of this journey. There will be a lot of change involved as systems are implemented, processes are redefined, data is collected, analytics starts to drive decision making, and the data starts to define strategy.

And that is where we come in. Our mission at Oracle Construction and Engineering is “to help people see data in new ways, discover insights, and unlock endless possibilities.” And just like you, we are only getting started!

Oracle Construction and Engineering, the global leader in construction management software  and project portfolio management solutions, helps you connect your teams, processes, and data across the project and asset lifecycle. Drive efficiency and control in project delivery  with proven solutions for project controlsconstruction schedulingportfolio managementBIM/CDEconstruction payment management, and more.

 

 

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