IMAGINE is a conceptual model used within the discipline of social media intelligence to govern data acquisition, its management, meaning derivation and utilization to serve the organization. This model is intended for medium to large scale organizations.
The first step required to perform analysis of any form of data is to obtain it. To acquire data from social media at an aggregate level that can serve an enterprise or organization, it must be ingested into a technological capability that can contain it. Due to the plethora of data available in social media selection for its ingestion in a repository or warehouse must compile to parameters that suit the goal(s) for the data’s eventual usage. That is, the entirety of the internet cannot be contained within one from either a warehousing or practical application standpoint.
Once parameters for ingestion and storage are established, data requires maintenance. Selection for meaningful social media data is currently based on keyword search strings. Due to the nature of vernacular, slang and internet jargon, the meaning of words and phrases is constantly changing. Therefore, regular maintenance of what data is being ingested and which queries are being used is required. Additionally, since there are generally multiple stakeholders consuming the data it is paramount to ensure both ingestion and maintenance suits their individual organizational needs.
Preparing and analyzing the data may require the longest time commitment in this model. During this step, the researcher is trying to make sense of the data through its aggregation, visualization and/or calculation. Analysis may take place within a dedicated social media analysis capability or special tool. The goal of the analysis is to derive insights. These may confirm existing hypotheses or provide new directions for further investigation.
Once the meaning of the data is understood, stakeholder must be notified of the actionable information it contains. This can be done in a variety of forms that fits within the organization’s standard practices.
However, going beyond simple notification, the model concludes with its hardest step, that of engagement. Siloed data in which powerful, new insights and ideas has been derived cannot and should not rest in a forgot file or report. Engagement requires an advocate to relentlessly argue for its evaluation and/or incorporation. Unless this step is successful, all the work done earlier in the model has been a waste of time.
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