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PART 6 OF 7 IN A SERIES ON ADOPTING ANALYTICS CULTURE
6. What particular information is gained from social network analysis and how is it interpreted?
To recap, we have established that adopting analytics culture requires organizational change management. Beyond analytics technology and expertise, analytics culture depends upon effective organizational decision ma.... In particular, evidence-based decision making best practices need to be sharpened via a focus on effective processes and supporting organizational architectures. It was asserted that decision effectiveness can be improved via concerted attempts to optimize analytical processes, decision rights, access to information, proper incentives, assessment systems, and communication pathways.
However, it was revealed that thetrack record for corporate change management initiatives is quite poor. It was proposed that two factors contribute to ineffective change management: 1) over-emphasizing the organizational chart, and 2) a lack of focus on relational interactions and networks. Social network analysis (SNA) was proposed as a method for improving understanding of relational factors and engineering organizational network change. This article goes into detail concerning the information that can be gleaned from SNA.
By characterizing the organization as a set of overlapping networks, beyond a singular management hierarchy or accounting artifact, deep insight into organizational structural dynamics becomes possible. Collating simple data on communication ties in an organization allows maps of organizational structures to be extrapolated. SNA provides the capacity to extract detailed maps and network quantitative measures by framing and interpreting data on actors and their relations.
For instance, surveying workers regarding ‘who they go to for advice’ allows one to generate a detailed map concerning exchanges of expertise, some of these exchanges which may be reciprocal, some of which may be one-way. By aggregating all ‘ties’, a structured set of relations emerges as a network map. It soon becomes visually and quantitatively apparent who the ‘go to’ people are, as well as those who ‘bridge’ information between subgroups.
Subsequent analysis can be conducted concerning demographics and actor attributes such as gender, seniority, and specialty. From here, research questions and hypothesis can be framed, such as ‘male workers with higher seniority in this firm tend to have a higher density of incoming connections.’ A change program which aims to increase diversity and involvement amongst younger workers in a particular discipline (i.e. statistical analysis), could then use an understanding of the as-is network to implement a rotation program, incentive system, and/or networking body for a target demographic they wish to work into the organization.
As well, such analysis allows for more sensitivity and care to be taken in reorganizations. With such insight, it becomes quickly apparent that, for instance, quiet, kindly, and unassuming Lois, the legal secretary, who has been with the organization for 30 years, is a ‘hub’ in conveying key organizational process information throughout the organization, often by passing knowledge through several cohorts. There are unassuming but crucial ‘Lois’ workers in every organization.
Often, the ‘Lois’ worker is the last person to be picked for the ‘guiding coalition’ in a change management initiative and they may be even at risk for being ‘downsized’ due to age and/or ‘unclear value’ (i.e. lack of visibility at higher management levels). By second-guessing the biases and interests of the guiding coalition and actually conducting such hands-on quantitative research into the organizational network, change initiatives can avoid the costly mistake of, after giving Lois a cardboard box on Friday, finding on Monday morning that she was running the company from below.
Any type of relational-exchange network can be analyzed within an organization: information flows, respect, reporting lines, cross-functional collaboration, decision making, etc. The benefit of the SNA approach is that it is an exhaustive method: all agents report the basis of their exchange individually. The network structure emerges from the aggregation of the network ties. Both formal and informal patterns emerge. Insight is gained into the existence of cliques, or sub-groups which may be somewhat isolated or disconnected from a process. Insights from SNA map visualizations often are surprising and lead to rapid insights. It becomes clear who is not talking to whom, who is hoarding information, who is a bottleneck, etc.
SNA information is quick and relatively painless to gather: a survey is conducted asking each individual for a list of names regarding their collaborations (the object of exchange being targeted). ‘Snowball’ surveys can be conducted in larger organizations, whereby people are asked who their ‘go to’ relations are, those people are surveyed, and the subsequent layers are surveyed in turn. Pictures of the interconnected network emerge.
Also, data can be gathered from passive sources, especially where electronic records are available (and access is permitted): email exchanges, phone connections, meeting invitations, text messages, etc. When permitted, electronic records of exchanges can be used to generate rapid, detailed maps concerning who is talking to whom (or not talking too whom) in the organizational network.
Beyond the visual maps, there are more focused quantitative measures which can be extrapolated from the network. Based on a long sociological research tradition, a number of key SNA measures have been developed which can be determined from network data.
A number of standard quantitative measures of network characteristics can be extrapolated from aggregate connection data:
By clarifying the types of quantitative measures that emerge from a SNA program, it becomes cleared how a change program can benefit. The variables which emerge from SNA each have implications concerning particular goals for a change initiative. For a change initiative focused on adopting analytics culture, those measures which seek to strengthen decision making effectiveness would be of particular interest.
For instance, an organization could be measured in terms of its financial planning and analysis (FP&A) network. What would emerge would be a map and quantitative measures representing the degree to which the FP&A process is cleanly connected (in terms of agent communication). It would also become apparent where certain cliques or sub-groups consolidated. It might become apparent, for instance, that there was a lack of connection between functional business specialists, corporate finance planners, and IT people managing the FP&A systems. Such an organization could benefit by putting in place FP&A functional sub-teams which would encourage tighter network relations between the various FP&A decision actors.
Examples abound and are constrained only by the goals and scope of corporate change objectives. In the next and final article, we will consider more specifically how the above measures can be used to plan and adopt analytics cultural change.
END OF PART 6 OF 7 IN A SERIES ON ADOPTING ANALYTICS CULTURE
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