Software

Organizing, Cleaning, And Prepping Battery Data For Analysis Takes Significant Time And Effort

Scope:

165 decision makers in the automotive industry responsible for EV battery testing, validation, and development in the US and Europe

Executives from Electra Vehicles, Ionworks, Form Energy

“Lack of access to data is the #1 problem.” “Without [good data], you can’t make good models. It’s very hard and costly to get [good data] yourself.” “We absolutely do want to model this, but you can’t validate your model without... collecting all that data.”

57% of respondents cite deciphering complex relationships in vast, multiparameter datasets as a significant barrier [to battery validation]. 61% estimated months to years of time savings from AI-powered cell characterization testing that leverages standardized data sets.

Sources: Medium: Battery Researchers have a problem..., Forrester: AI for Battery Validation / Doc, Anaconda: State of Data Report 2023 / Doc, Big Data Wire: Data Prep Still Dominates Data Scientists’ Time, Survey Finds

SURVEY: Data Prep and Cleaning reported as the most time-consuming activities for data practitioners. Scope = 1,071 respondents

Data Prep Still Dominates Data Scientists’ Time, Survey Finds Alex Woodie

Data scientists spend about 45% of their time on data preparation tasks, including loading and cleaning data, according to a survey of data scientists conducted by Anaconda.

Extracted from the PDF; charts and images may read imperfectly.