Knowledge management vs information

“Knowledge is information that changes something or somebody — either by becoming grounds for actions, or by making an individual (or an institution) capable of different or more effective action.” — Peter F. Drucker in The New Realities

Knowledge, information and data are all fundamentally different, but linked, concepts.

The terms “information” and “knowledge” are often used as though they were interchangeable, when in practice their management requires very different processes. To manage our natural resources in a sound manner we need to manage information and knowledge resources effectively. However, as the diagram illustrates, information and knowledge management focus on different parts of the same value chain.
Knowledge management (KM) focuses on the processes and the people involved in creating, sharing and leveraging knowledge among science, communities, resource managers and policy makers. Some key papers in this area are set out below, and other linked papers in this KM area can be found in the networks page. Information management, in contrast, is more concerned with establishing processes and systems to gather, organise, summarise and package information … including it’s timely delivery to the right decision makers for the situation involved. The two processes are linked in activities that support learning such as you can find on the other pages such as co-productionorganisational learning, conceptual modelling, and participatory modelling. Some useful sources on knowledge management include:

Knowledge management – from bottleneck to success factor
Gianluca Colombo (2020) Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH
This publication introduces a conceptual and methodological framework for the design and the implementation of effective knowledge management interventions for co-learning systems in international development cooperation. Becoming acquainted with this framework can enable practitioners to coordinate collaborative knowledge work more effectively and share experiences in order to achieve better project results and disseminate findings.

Calling for a new agenda for conservation science to create evidence-informed policy
David Christian Rose et al. (2019) Biological Conservation
This paper by David Christian Rose and colleagues notes that there is general agreement on the main barriers to developing evidence-informed policy. The key barriers: i) that conservation is not a political priority; ii) that there is poor engagement between scientists and decision-makers; and iii) that conservation problems are complex and uncertain – have often been highlighted in the literature as significant constraints on the use of scientific evidence in conservation policy. There is also repeated identification of the solutions to these barriers. In this perspective, we consider three reasons for this: (1) the barriers are insurmountable, (2) the frequently-proposed solutions are poor, (3) there are implementation challenges to putting solutions into practice. The authors argue that implementation challenges are most likely to be preventing the solutions being put into practice and that the research agenda for conservation science-policy interfaces needs to move away from identifying barriers and solutions, and towards a detailed investigation of how to overcome these implementation challenges.

A typology of barriers and enablers of scientific evidence use in conservation practice
Jessica Walsh et al. (2019) Journal of Environmental Management
Over the last decade, there has been an increased focus (and pressure) in conservation practice globally towards evidence-based or evidence-informed decision making. Despite calls for increased use of scientific evidence, it often remains aspirational for many conservation organizations. The key characteristics that facilitated the use of science in conservation decisions were associated with an organization’s structure, decision-making processes and culture, along with practitioners’ attitudes and the relationships between scientists and practitioners. This taxonomy and inventory of barriers and enablers can help researchers, practitioners and other conservation actors to identify aspects within their organizations and cross-institutional networks that limit research use – acting as a guide on how to strengthen the science-practice interface.

A roadmap for knowledge exchange and mobilization research in conservation and natural resource management
Vivien Nguyen and colleagues (2016) Conservation Biology
This 2016 paper by points out that rapid environmental changes and calls for sustainable management practices mean the best knowledge possible is needed to inform decisions, policies, and practices to protect biodiversity and sustainably manage vulnerable natural resources. They propose a knowledge-action framework that provides a conceptual roadmap for future research and practice in KE/KM. The framework integrates concepts from the sociology of science in particular, and serves as a guide to further comprehensive understanding of knowledge exchange and mobilization in conservation and sustainable natural resource management.

What is KM? Knowledge management explained
This 2018 page by Michael E. D. Koenig reminds us that the most central thrust in KM is to capture and make available, so it can be used by others in the organization, the information and knowledge that is in people’s heads as it were, and that has never been explicitly set down.

Why Knowledge Management Is Important To The Success Of Your Company
This short 2012 article by Lisa Quast highlights the importance of knowledge management in business, and the costs of not investing in it.

Strategic Intentions: Managing knowledge networks for sustainable development
This book by Heather Creech and Terri Willard  is written for practitioners who are working with different models of individual and institutional collaboration. The authors have tried to capture the details of network operations and management: what it really takes to help knowledge networks achieve their potential.

Knowledge networks and communities of practice
This paper by Verna Allee first describes the new logic driving interest in knowledge management and then focuses on how OD practitioners can participate in that strategic conversation, and support knowledge creation and sharing through building communities of practice.

ABC of knowledge management
This report by Caroline De Brún provides a substantial but useful introduction to KM. It not only covers concepts and principles, but then goes on to provide tips and guides for those wanting to manage knowledge more proactively.

Organisational learning

Within business, learning is a conscious attempt on the part of organisations to improve productivity, effectiveness and innovativeness in uncertain economic and technological market conditions. The greater the uncertainties, the greater the need for learning. Learning enables quicker and more effective responses to a complex and dynamic environment. In turn, effective learning is associated with increased information sharing, communication, and understanding. Because of these reasons the concept of “learning” is probably more pronounced in business than any other area. Although business-oriented papers and research are not widely cited in agricultural, conservation or environmental literature – there are a lot of lessons to be gained from related work in organisational development and learning literature.

Approaches to conceptual and participatory model building

The two processes both link, and enhance each other in learning-based initiatives such as participatory or cooperative model-building. Often one of the best techniques for generating improved thinking at a social level is to use models as an aid to help stakeholders visualize the wider social and bio-physical processes that they cannot see unaided. Many forms of modelling can be used in this way, and underlying methodologies cover such things as conceptual modelling to help build a shared graphical representation of the problem situation, and participatory modelling which involves stakeholders in different aspects of the modelling process.