It strikes me that the starting point for data analytics in construction is rules based definition of shared data.
Data Analytics in areas where it has thrived, e.g., retail collects simple, rules based data automatically. For example Zara can determine, daily, from sales data collected automatically, which item is selling, in what locations , in what numbers, in what colours, in what sizes - allowing them to quickly make decisions about whether to manufacture more or less, in what sizes and colours, and where to deploy them. All retailers now follow this process. These are single large corporations that harvest, at a cost, and own the data.
Construction is highly fragmented, with “virtual businesses” (construction projects) each working under a dozen Rules of Association (Contracts, subcontracts, terms of sale).
The quantity surveying profession was responsible for the creation of structured rules for creating rules-based structured, shareable critical scope and cost data (in words and numbers) for building works. Attempts were made to include civil engineering work, with little success. They were then responsible for creating the data, to be shared between project stakeholders and used for creating and refining accurate budgets, as designs developed, and then for agreed contract management processes (payment, valuation of variations, delay costs etc.) that were cost effective, unambiguous, and ,most importantly, did not inhibit construction progress. The structures were simple, divided into building elements for forecasting and cost planning (Australian Cost Management Manual), and Standard methods of Measurement for contract establishment and management. (AIQS, RICS, ICE and other national standards). I describe the decline in the use of independent data creation here. They were not replaced by any uniform standards at all, resulting in the chaotic systems we have inherited today.
I described and proposed a development of data descriptions for more comprehensive analysis of Work Breakdown Structures to the Aus. Institute of Quantity Surveyors in 2007, to include new yardsticks - object definition, dimension groups, activity, resources, room data and functional elements (Tax, facility management, due diligence).
The data gathering is likely to be more granular, with trainable AI assembling data into many of the above yardsticks for particular cases and uses.
Object definition is an obvious starting point, though it seems to be limping along. Definitions at a bill of quantities level (becoming more automated) may be the most appropriate level, since it is less generic than object definitions, taking into account the particularities and vagaries of particular projects and types of projects.
Clients, particularly governments, must talk to their retail and manufacturing counterparts to understand the value of creating and owning and deploying critical financial data, and the loss to them and the community of leaving it to the four winds.