AI training and inference are all about running data through models — typically to make some kind of decision. But the paths that the calculations take aren’t always straightforward, and as a model ...

Neuenfeldt: We’re working with data standards like CIM (Common Information Model) 61 970, 301 for normalization and alignment, and the emerging 61 850 standard for smart grids. The goal is to ...

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.

As a use case, the Buildings IOT Integration team used the OAP standards to model the data in all connected systems at 800 West Fulton Market in Chicago. This delivered multiple benefits for the ...

Data normalization facilitates the flow of data across front-, middle-, and back-office operations—in both directions. For example, when Broadridge provides dashboards with real-time lifecycle data to ...