A tool which analyses how people work together to solve problems and come up with new ideas
Definition from Knowledge Management Specialist Library.
What are the benefits?
In the context of knowledge management, social network analysis (SNA) enables relationships between people to be mapped in order to identify knowledge flows:
- Who do people seek information and knowledge from?
- Who do they share their information and knowledge with?
In contrast to an organisation chart which shows formal relationships – who works where and who reports to whom, a social network analysis chart shows informal relationships – who knows who and who shares information and knowledge with who.
It therefore allows managers to visualise and understand the many relationships that can either facilitate or impede knowledge creation and sharing. Because these relationships are normally invisible, SNA is sometimes referred to as an “organisational x-ray” – showing the real networks that operate underneath the surface organisational structure.
Once social relationships and knowledge flows can be seen, they can be evaluated and measured. The results of social network analyses can be used at the level of individuals, departments or organisations to:
- Identify teams and individuals playing central roles – thought leaders, key knowledge brokers, experts, etc.;
- Identify isolated teams or individuals;
- Detect information bottlenecks;
- Spot opportunities for knowledge flow improvements;
- Accelerate the flow of knowledge and information across functional and organisational boundaries;
- Improve the effectiveness of formal communication channels;
- Target opportunities where increased knowledge flow will have the most impact;
- Raise awareness of the importance of informal networks.
How do I go about it?
The process of social network analysis typically involves the use of questionnaires and/or interviews to gather information about the relationships between a defined group or network of people. The responses gathered are then mapped using a software tool specifically designed for the purpose (see Resources and references below for examples). This data gathering and analysis process provides a baseline against which you can then plan and prioritise the appropriate changes and interventions to improve the social connections and knowledge flows within the group or network.
Key stages of the process will typically include:
- Identifying the network of people to be analysed (e.g. team, work group, department).
- Gathering background information – interviewing managers and key staff to understand the specific needs and problems.
- Clarifying objectives, defining the scope of the analysis and agreeing the level of reporting required.
- Formulating hypotheses and questions.
- Developing the survey methodology and designing the questionnaire.
- Surveying the individuals in the network to identify the relationships and knowledge flows between them.
- Use a software mapping tool to visually map out the network.
- Reviewing the map and the problems and opportunities highlighted using interviews and/or workshops.
- Designing and implementing actions to bring about desired changes.
- Mapping the network again after a suitable period of time.
Are there any other points I should be aware of?
In order for SNA maps to be meaningful, it is important to know what information you need to gather in order to build a relevant picture of your group or network. Good survey design and questionnaire design are therefore key considerations.
Questions will be typically based on factors such as:
- Who knows who and how well?
- How well do people know each other’s knowledge and skills?
- Who or what gives people information about xyz?
- What resources do people use to find information/feedback/ideas/advice about xyz?
- What resources do people use to share information about xyz?
From NHS Knowledge Management Specialist Library ABC of KM
Reference: Social network analysis as an analytic tool for interaction patterns in primary care practices
J Scott, et al, Annals of Family Medicine, 2005, 3(5), pp443-448
Page last reviewed: 16 June 2021