For today’s scientific organizations, a laboratory information management system (LIMS) is necessary tool for effective data management in the laboratory. For many reasons, companies often find themselves in a situation where it is advantageous to upgrade the laboratory environment by migrating data from legacy systems into a new or even existing LIMS. A LIMS data migration project will often involve the use of ETL technology to assist. Migrating data to a LIMS can be a challenging endeavor, however. Companies often have years of historical data and knowledge stored in their existing LIMS that must be migrated over to the chosen system. Depending on the amount and complexity of the data sources in the legacy system(s), as well as the complexity of the new LIMS, the data migration can require much time and labor. Unless the project team does proper planning and execution, the migration process can end up being the cause of major project delays.
In today’s highly competitive global economy, innovation is mission critical for research and development (R&D) organizations. An important key to effective innovation is the efficient capture and sharing of experimental data to help organizations leverage their collective experience and knowledge.
How best can we retrieve value from the rich streams of data in our profession, and introduce a solid, systematic process for analyzing that data? Here Kayser et al. describe such a process from the perspective of data science experts at Ernst & Young, offering a model that "aims to structure and systematize exploratory analytics approaches." After discussing the building blocks for value creation, they suggest a thorough process of developing analytics approaches to data analytics. They conclude that "[t]he process as described in this work [effectively] guides personnel through analytics projects and illustrates the differences to known IT management approaches."
In this 2018 paper by Murtagh and Devlin, a historical and professional perspective on data science and how collaborative work across multiple disciplines is increasingly common to data science. This "convergence and bridging of disciplines" strengthens methodology transfer and collaborative effort, and the integration of data and analytics guides approaches to data management. But education, research, and application challenges still await data scientists. The takeaway for the authors is that "the importance is noted of how data science builds collaboratively on other domains, potentially with innovative methodologies and practice,"
This recorded Lab Informatics Tutorial series is designed as a management level view of laboratory systems and is appropriate for anyone planning, reviewing, or approving the acquisition of laboratory informatics. A background in science is not necessary to follow the presented material. Its purpose is to provide you with an understanding of how these technologies (Laboratory Information Management Systems, Electronic Laboratory Notebooks, Scientific Data Management Systems, Laboratory Execution Systems, Instrument Data Systems, and supporting technologies ) can be used to support/improve your labs operations, and the considerations that need to be taken into account before they are purchased.