12 AUG 2020
Often, digital transformation initiatives fail to meet expectations because one key element is either overlooked or not accounted for from the start. That key element is people.
In isolation, process and technology may deliver artificial intelligence but if team members are not data literate, they will not be able to ensure adequate data quality or assess the validity of machine generated analyses. Similarly, if leadership teams are not digitally fluent, they will be unable to provide teams with the software or hardware tools needed to compete in this new era.
Ultimately, by elevating a team’s data science capabilities, organizations may move beyond the inherently flawed constraints of single-point, deterministic estimating and instead employ a more realistic, scientific approach, rooted in probability theory, that expresses a plausible range of goals or qualifies targets in terms of their confidence of success.
When teams possess the skills and knowledge to support advanced analytics, in addition to artificial intelligence (AI), benefit is derived from two other functions: augmented decision-making and collaborative adaptability.
Fundamentally, recent advances in AI are attributed to the brute force nature in which we can employ machines to perform an astronomical number of rapid calculations on a limited number of inputs. Although the number and speed of assessments people may perform may seem poor relative to machines, one crucial advantage we hold over them is that we may receive (if not comprehend) a near infinite number of inputs. It may be decades before machines are capable of contextual analytics, if it happens at all. For this reason, humans are essential to performing augmented decision-making. To emphasize this point, it is said, machines will not replace project professionals but project professionals who use machines will replace those who don’t.
Similarly, teams who possess 21st Century analytical skills can facilitate and benefit from collaborative adaptability. That is, teams who more effectively share data across the value chain, may collaborate to more effectively respond to risks or changing conditions than others who do not possess such insights. In this sense, data analytics offers a risk-based competitive advantage.
Great challenges, however, exist for many organizations in establishing and maintaining a minimally viable data science capability. Given the rapid pace of technological development, most employees leave full-time education with a data literacy gap. Institutions such as RICS have an opportunity to help members develop contemporary skills and knowledge, close this gap, and provide a means for our sector to establish a data-driven culture.
Finally, in this brave new world, professional standards have never been more important. To ensure big data does not generate big problems, transparency and accountability will remain vital traits. Further, ethical reasoning will be critical to ensure decision makers take reasonable steps to remove bias from their systems and final decision-making process.
In a Post-COVID-19 era, the democratization of data will expediate the rebuilding of economies, for the good of the environment and society at large.
The insight paper provides answers about the evolving ‘what’ and ‘why’ of BIM and its potential for surveyors, including: • the background to BIM • the need to innovate • why the development of the definition of BIM needs to be explored • the need for surveyors to examine the digital technologies available, and a plan of action for implementing them.
 Mirror Worlds: The Day Software Puts the Universe in a Shoebox … How it will Happen and What it will Mean, David Gelernter, 1992, Oxford University Press
 https://en.wikipedia.org/wiki/David_Gelernter (accessed July 25, 2020)
 ISO 19650-1:2018, Organization and digitization of information about buildings and civil engineering works, including building information modeling (BIM)