BIM technology has been improving and its use is spreading in the construction industry more and more every year, reaching a point that we can make sure that it here to stay, but even today
most of the BIM processes and workflows are clear and established for the geometric and visualization tools of the technology but not for the Data management. We all agree that the parametrization and the data provided by the BIM Software is the key of the everything, but even today is not really clear which are the best practices to obtain data and process it to
understand patterns or get conclusions that can lead us into better designs on every project.
That is why the main objective of this lecture is to dig on the better practices to go from raw Revit Data to great visualizations that will lead us into better decisions and workflows on our
construction projects.
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Turn Revit data into useful information with visualization techniques and workflows
1. Join the conversation #AU2017Join the conversation #AU2017
Turn Revit Data into useful information with
visualization techniques and workflows
Valentin Noves
BIM Project Manager
2. Valentin Noves is a BIM project manager at ENGworks, where
he leads teams on medium and large scale international projects
and provides support on geometric rationalization, programmatic
solutions, development of computational workflows for
interoperability and MEP analysis. He has continued to push the
boundaries of technology focusing on computational design and
BIM to deliver buildings in ways that improves current working
methods; from rationalizing geometrically complex buildings to
workflow automation. He is a Revit certified professional and a
Lean Manager Certified Professional by the AGC
Valentin.noves@engworks.com
@valenovesl
Valentin Noves
6. Learn about the potential of data coming from Revit.
Learn about best ways to extract Revit data.
How to transform Revit Data into useful information.
Data-visualization techniques for architectural projects using Power BI
and Tableau.
Learning Objectives
50. Clean Data
• Detect False positives
• Detect deviations
• Make sure we have exported the right
data
• Check the Data integrity
• Check that the Data is complete
51. Normalize Data
• Make sure all values are in the same
units
• Same sure everything is in the same
format
• Reorder lists
• Normalize data given an specific range
• Check length of lists