Filling in the gaps in your data
There's no reason to not combine and use what we've got just because it's incomplete. We can identify what we'd like to be complete and go back and get it, but if we only have a point for a building, well okay so we go back and we can maybe use some AI from the aerial photographs to draw a building outline and maybe we can get some data from somewhere else which is about its height and we can start building on that, and that's part of the critical nature of a digital twin that once we can identify a thing, we can actually upgrade the information about it its visual quality, its overall digital resolution can be increased over time as we gather data—either visual data or attribution data relationship data, so a digital twin is going to evolve and change over time with higher and higher definition and resolution.
So, invest in a framework that anticipates those changes in growth and that naturally is going to anticipate that your existing data will be very patchy, it will be incomplete, it will need to be evolved, and grown and refined.