Tis chapter considers how the smart laboratory contributes to the requirements of a knowledge eco-system, and the practical consequences of joined- up science. Knowledge management describes the processes that bring people and information together to address the acquisition, processing, storage, use, and re-use of knowledge to develop understanding and to create value. Te objective of a smart laboratory is to

improve the performance of the business and maximise the value of its intellectual capital. Although knowledge management has a strong human component (tacit knowledge), its success is dependent on the preservation of, and access to, explicit knowledge. Traditionally this has been achieved with paper; the digital age presents a new set of challenges.

Scientists have been involved in the development of artificial intelligence (AI) for decades. Te modern version of AI, which sought to create an artificial human brain, was launched in 1956 but had been


Document management

fermenting many years before that, following the discovery that the brain was an electrical network of neurons that fired in pulses. Over the years, there were apparent breakthroughs that were followed by troughs of despair caused by hardware and soſtware limitations. Among the highlights of this era was a Siri- like machine called ELIZA which, in 1966, could be asked natural language questions and provided voice-appropriate – albeit canned – answers. Te birth of the discipline of knowledge

management (KM) was in the early 1990s. A tidal wave of KM consultants appeared, heralding the birth of this newer AI version and the emergence of supporting hardware and soſtware. A few years later, several scholarly journals appeared as forums for advancing the understanding of the organisational, technical, human, and cognitive issues associated with the creation, capture, transfer, and use of knowledge in organisations. Today the value of tapping into an easily

accessible collection of information, such as a smart laboratory’s assets, is appreciated

more than in the past. Te amount of data and information generated by instruments and scientists increases exponentially, and staff turnover is rising to the point where someone with seven years of service with the same employer is now considered an old-timer. Undocumented know-how and locations of information resources are now issues. Reinventing the wheel is becoming more commonplace – not a desirable occurrence, because the costs of drug development are ever-increasing and fewer blockbuster products are hitting the market. Many soſtware solutions offered to assist

information management are specialised and fragmented. Oſten, disparate divisions of an organisation select their own local soſtware solutions, and IT departments oſten dictate requirements that restrict the scope of possible vendor solutions. Excluding small, single location labs, it is rare to see a smart laboratory where all associated information resides under one roof. Tere are general solutions to support

the processing of large data sets in a distributed computing environment. One


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44