Environmental scanning systematically searches for signals of change, which can be recognized from a wide variety of sources. This process is fundamental to identifying where your organization needs to position its purpose, process, product, and people for the highest impact. However, to move from weak signal scanning to meaningful data clusters—the precursors of relevant and impactful trends—requires evaluation processes rooted in qualitative and quantitative methods.
Gathering information during the environmental scanning process can be accelerated through automated processes and the use of artificial intelligence. A high volume of data can be assessed using machine intelligence which can relieve manual labor demands and free up human capacity for qualitative and intuitive analysis. From large pools of data that have already been tagged and categorized through automation, the sensemaking process uses a combination of machine and human intelligence to group and evaluate data.
Evaluation and quantifying processes are necessary to help teams identify the most relevant directions of change. Relevancy must consider external factors as well as respective organizational variables. Applying a rating and evaluation system to these variables should include both qualitative and quantitative data points alongside collaborative inter-division expertise to yield the most robust results.