Smart data capture

Smart data capture (SDC), also known as 'intelligent data capture' or 'automated data capture', describes the branch of technology concerned with using computer vision techniques like optical character recognition (OCR), barcode scanning, object recognition and other similar technologies to extract and process information from semi-structured and unstructured data sources. IDC characterize smart data capture as an integrated hardware, software, and connectivity strategy to help organizations enable the capture of data in an efficient, repeatable, scalable, and future-proof way.[1] Data is captured visually from barcodes, text, IDs and other objects - often from many sources simultaneously - before being converted and prepared for digital use, typically by artificial intelligence-powered software.[2] An important feature of SDC is that it focuses not just on capturing data more efficiently but serving up easy-to-access, actionable insights at the instant of data collection to both frontline and desk-based workers, aiding decision-making and making it a two-way process.

Smart data capture automates and accelerates capture, applying insights in real time and automating processes based on extracted input. Smart data capture is designed to be repeatable and scalable to reduce low-level manual tasks and eliminate human error. To achieve this goal, smart data capture solutions are often made available using specialist software installed on commodity hardware such as smartphones.[3] However, some solutions may rely on specialized hardware such as dedicated scanning devices, wearables[4] or shop floor robots.[5]

  1. ^ Arcaro, Matt (January 2023). Smart Data Capture: A Technology Strategy to Scale Data Intelligence (PDF). IDC (Report).
  2. ^ Mueller, Samuel (17 November 2022). "What Companies Should Know About Smart Data Capture And Last-Mile Delivery". Forbes Technology Council.
  3. ^ "How smart data capture solutions on Samsung Galaxy rugged devices are helping transform business operations". Samsung. 27 October 2022.
  4. ^ Bauer, Dennis; Wutzke, Rolf; Bauernhansl, Thomas (2016). "Wear@Work – A New Approach for Data Acquisition Using Wearables". Procedia Cirp. 50: 529–534. doi:10.1016/j.procir.2016.04.121. S2CID 114410108.
  5. ^ Anstee, James (14 January 2022). "Scandit launches smart shelf management for retailers". Electronic Specifier.