Essentials of Knowledge Management

5.12. Data Capture Tools

Data capture—getting information accurately and efficiently into a machine-readable form, whether a payroll total or the notes from the latest community of practice meeting—is typically the most challenging part of a Knowledge Management initiative. Even if knowledge workers and experts are willing to contribute their rules and heuristics to the corporate data warehouse, there is the issue of capturing the information efficiently and accurately. However, a KM initiative can't be expected to improve the company's bottom line without information.

In general, the technologies used for data capture are defined by the source. On the web, for example, public search engines form the basis for data capture. For printed material in the office, optical character recognition (OCR) technologies, from flatbed scanners to hand-held wands, can be used to convert printed text to machine-readable text. Whiteboard recorders and digital cameras can save error-prone transcription from traditional whiteboards. Similarly, tape recorders can be used to capture voice for either manual or automatic (voice recognition) transcription later.

Besides working with text, speech, and images, data capture technologies can be applied to physical objects. Bar codes allow rapid tracking of inventory, for example. Similarly, real-time location sensors support object tracking—whether what is tracked is knowledge workers in a factory or widgets on an assembly line.