Get the Right Decision Making | Building News
Richard Harpham is Chief Revenue Officer at Slate Technologies
Like many industries, construction people often fall victim to various pitfalls when it comes to making decisions.
Sometimes workers and managers make choices because of unconscious biases that prevent teams from tackling old problems in new ways, or they miss key information, leading to a bad decision or inaction.
At other times, key information can slip through the cracks due to poor multitasking, affecting how and why a decision is made.
The problem is that these decision-making problems often lead to errors that are generally avoidable, which leads to waste.
Like Latham and Egan reported decades agoup to 30% of construction efforts are caused by preventable problems, of which up to 70% are information errors – errors that ultimately waste resources, time and money.
What if the right decisions were made more often by those involved in construction?
Decisions, decisions, decisions
For most humans, decision making is usually blocked by one of these categories:
- Decision bias: Use past knowledge to make future decisions, even without all the information to make an informed choice – “what my gut tells me” or “if my memory is good”;
- Behavioral economics: Having only part of the data and being influenced by a single point of view;
- Inattentional (perceptual) blindness and amnesia: Focusing only on small data samples when other elements are present or previously existed, creating unintended biases;
- Multitask : When people make decisions by trying to do too many things at once;
- Context/relativity: When there is not enough information to make an informed decision, no decision is made or the wrong one is made.
During the life of a construction project, hundreds of thousands of decisions are typically made, based on little or no real-time data.
Rather, they are made primarily based on personal experience and memories. Couple this with inattentional blindness and amnesiamultitasking and the aforementioned contextual and missing data issues, and that means decision-makers make decisions with a narrow perspective, effectively handicapped by what they don’t know.
When new information becomes available, it is rarely available in real time and is instead collected and reviewed a month or more later, which is too late to act on it.
It is a management by “where we have been” and not “where we are going”, and if it were a proprietary process it would be called Management by Mirror (MBRVM).
To prevent MBRVM (and give construction workers more information to make better decisions), construction companies need access to more data, both in sight and in the dark.
The data dilemma
Some might believe that the construction industry has a digital data shortage problem and that there is a limited amount of data available for companies to exploit.
The real problem, however, is that we have an unstructured data problem, with up to 60% of data containing potential decision-making context that is never available in a way that would reveal new avenues and choices.
This is the definition of ‘dark data’ – data that an organization has stored in silos and software, scattered everywhere, which, when discovered and contextualized, can provide tremendous value by providing useful information for decision-making (e.g. a report summary of “lessons learned” from previous construction projects).
This is not a software integration problem, but rather a data intersection problem. To exploit this obscure data, the industry must therefore take a close look at artificial intelligence (AI) and machine learning technologies.
The rise of machines
When AI tools perform at their best, they reveal valuable situational context and insights that can dramatically improve outcomes. This is because people can make better decisions based on new data/information they didn’t have before. These tools are computational, which has advantages with evaluating data that humans do not have, such as:
- Once trained, these machines can see, count, relate and examine anything presented to them, and then predict potential problems or opportunities much earlier than humans.
- Decision bias. Inherently, machines are unbiased unless they are intentionally created by a human repeatedly accepting recommendations that offer the same type of data over and over again (AI training).
- Machines can see everything at once and in volume, avoiding problems with inattentional blindness. They process vast volumes of data in parallel, rather than the way humans serialize analysis.
Computational AI machines can empower the construction industry by helping people make better decisions based on access to real-time data in the right context, increasing the likelihood of improved outcomes.
If the industry begins to take advantage of these advanced data science methods, its human capital can become the most efficient and effective workforce it has ever seen, which will make the industry of building more cost-effective, less wasteful and able to compete with other industries for decades to come. .
Learn more: Slate technologiesan AI platform that maximizes efficiency and costs for the construction industry.