China 
Africa 
Explore chapters and articles related to this topic
Big Data Analytics for AV Inspection and Maintenance
Published in Diego Galar, Uday Kumar, Dammika Seneviratne, Robots, Drones, UAVs and UGVs for Operation and Maintenance, 2020
Diego Galar, Uday Kumar, Dammika Seneviratne
Without careful cataloguing of data as they are captured, autonomous car vendors run the risk of creating a “dark data” problem. Dark data is the term used to describe data an organization collects but fails to take advantage of because they don’t know how to or have forgotten they have them. This will be a significant problem for self-driving cars because of the sheer volume of data they generate. And as we see more vendors enter the autonomous driving market, the ones that will ultimately win out over others will be those vendors best prepared to analyze data at the local level and have catalogue their databases properly so future autonomous applications can find the legacy data they need, when they need them (Chala, Bayliss, & Camper, 2019).
Data Discovery
Published in Preston de Guise, Data Protection, 2020
Traditionally, dark data is considered to be data that hasn’t been classified or associated with an analytical tool or use. For example, log files that are generated but never processed might in the simplest way be considered a form of dark data.
Data Discovery
Published in Preston de Guise, Data Protection, 2017
Traditionally, dark data is considered to be data that hasn’t been classified or associated with an analytical tool or use. For example, log files that are generated but never processed might in the simplest way be considered a form of dark data.
The best of times and the worst of times: empirical operations and supply chain management research
Published in International Journal of Production Research, 2018
Steven A. Melnyk, Barbara B. Flynn, Amrou Awaysheh
Dark data (DD) is data that exist within an organisation, yet it is not examined or analysed and thus managers do not use it to gain any insights into the firm’s operations. Researchers will also need to expand their data gathering and analytical skill set to help them discover and tease apart the data that is available. Researchers might believe that firms don’t have much data for analysis or that such data doesn’t exist. This might be because managers themselves might not know what data they have available or what data is collected within other units. Such data typically doesn’t exist in perfectly formatted BD datasets ready for analysis and interpretation. However, such data might be ‘hidden’ within the firm’s existing data systems. Thus, researchers need to work with managers to understand all the data that is available so they can discover such DD. DD might be difficult to extract and format in preparation for data analysis. However, DD can contain a lot of information that can provide rich insights for academics as well as managers.