Issue link: https://iconnect007.uberflip.com/i/1228683
44 SMT007 MAGAZINE I APRIL 2020 of big data are occurring faster than original- ly anticipated. This trend will continue high- lighting existing challenges and introducing new gaps that were not considered previously (Figure 2). As an example, data retention practices must quickly evolve; it has been determined that limitations on data transmission volume and length of data storage archives will disappear (e.g., historical data retention of "all" will be- come standard practice). Examples of data flow key considerations are data pipes, machine-to- machine (M2M) communication, and synchro- nous/asynchronous data transmission. A flexible, secure, and redundant architec- ture for data flow and the option considerations (e.g., cloud, fog, versus edge) must be articu- lated. The benefits and risks must be identified and discussed. Data flow and its ability to ac- celerate the evolution of big data technologies will enable the deployment of solutions to real- ize benefits from increases in data generation, storage, and usage. These capabilities deliver- ing higher data volumes at real-time and near- real-time rates will increase the availability of equipment parameter data to positively impact yield and quality. There are several challenges and potential solutions associated with the in- creases in data generation, storage, and usage; capabilities for higher data rates; and addition- al equipment parameter data availability. The primary topics to address are data qual- ity and incorporating subject-matter exper- tise in analytics to realizing effective on-line manufacturing solutions. The emergence of big data in electronics manufacturing opera- tions should be discussed in terms of the "5 Vs Framework": 1. Volume 2. Velocity 3. Variety (or data merging) 4. Veracity (or data quality) 5. Value (or application of analytics) The "5 Vs" are foundational to appreciate the widespread adoption of big data analytics in the electronics industry. It is critical to ad- dress the identified gaps—such as accuracy, completeness, context richness, availability, and archival length—to improve data quality to support the electronics manufacturing in- dustry advanced analytics [4] . Figure 2: Example of a connectivity architecture providing smart manufacturing functionality. (Source: Cimetrix)