Adopting Analytics Culture: 5. How can change management be improved via analytics? (5 of 7)

PART 5 OF 7 IN A SERIES ON ADOPTING ANALYTICS CULTURE

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5.    How can change management be improved via analytics?

We have built-up the case for using organizational change management to adopt analytics culture.  By analytics culture, we mean the set of both formal and informal organizational attributes which emphasize robust decision making. Thus, analytics culture can be considered a byword for evidence-based decision making.  In particular, analytics culture implies organizational processes and practices which emphasize the use of analytics IT systems (BI, statistical analysis packages, and predictive analytics systems and software) and ‘Big Data’ approaches (solutions focused on managing and mining large sets of data to gain business insights).

Organizational network

As we have proposed that decision making is, at base, a factor of organizational processes and behaviors, it becomes natural to frame analytics culture as, beyond technology, a program requiring organizational change.  However, a wrench was thrown into the works by detailing the unreliable track-record of organizational change management. Simply put, organizational change often results in less-than-ideal outcomes.  Thus, there is a risk factor of worsening decision practices by attempting to engineer analytics cultural adoption.

There is hope: it lies in the application of analytics to organizational change processes themselves.  Promising new methods are emerging from sociological and organizational research which can help to make change management less of an uncertain art, and more a structured science and informed technique. In particular, social network analysis (SNA), a technique for the quantitative analysis of social interactions, is a promising method by which change management initiatives can be framed and managed.

This rise in computing power combined with sophisticated analytical tools creates the ability to countenance ‘deep’ socio-structural phenomenon in organizations. SNA is specifically advocated as a methodological approach to quantify and analyze organizational structure for the purposes of managing change programs. Although present in the sociological tradition since the 1930’s, and having proliferated across a number of social science disciplines, SNA is also a trending contemporary organizational research method, bolstered by the increasing visibility of social networks and powerful new computational tools.

The growing popularity of SNA-based organizational research methods is both: a) paradigmatic, bolstered by the emergence of social network media as a powerful and omnipresent cultural zeitgeist (i.e. LinkedIn, Facebook), and b) methodological, supported by the proliferation of increasingly powerful quantitative techniques, themselves facilitated by powerful analytical software tools and steadily growing computational power. Expanded data collection, storage, and handling capabilities have been matched with increasingly powerful software tools for conducting advanced SNA (i.e. UCINET, Sienna, Pajek, specialized R packages).

Conceptually, the key to applying SNA to gain insight into organizations is viewing organizations as networks of agents who are bound together by transactions or exchange-based ties.  Workers are role-based ‘agents’ or ‘nodes’.  These agents interact via transactions, which can be reporting relationships, exchanges of information, sharing of knowledge, co-processing a good or service, submitting requests for assistance, or simply expressed friendship and social affinities. Organizations in this context are network-based decision making mechanisms populated by various interacting agents who also inhabit sub-groups and cliques. Individuals are role-based agents interacting within networks via communication paths to process information into decisions. Workers, as organizational agents, both operate within and compose the network based upon various significant exchanges with other agents.

In the larger context, organizations can be viewed as dynamic information processing ‘organisms’ characterized by a network structure: systems which process information based on unique network structural characteristics and conditions. Agents, tracked as ‘nodes’ via SNA, can be quantified with the full range of attributes of interest to change orchestrators: expertise/experience level, role, affinity towards change, general attitude, and even ‘softer’ measures, such as respect in the organization or ‘buy-in’ on change program.

Likewise, agents-as-nodes can be tracked and linked to other agents via a range of relational ties, exchanges that define one’s participation in an organization.  Examples include communication frequency, respect, membership in cliques, and ties to central processes or systems. Of key concern are both the functional and social / relational ties which define an agent’s participation in the organization.  In the next article we will dig in deeper, specifying the types of quantitative measures that emerge from SNA-based analysis.

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complexity

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