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Different approaches of pattern management and strategic intelligence
Authors:Tuomo KuosaAuthor Vitae
Affiliation:
  • Nanyang Technological University, S. Rajaratnam School of International Studies (RSIS), Centre of Excellence for National Security (CENS), 50 Nanyang Av. S4, Level B3a-29, Singapore 639798, Singapore
  • Abstract:The world around us contains endless amounts of information. That information is mostly loose in our minds. Very often, it does not at first contact fit in with our conventional understanding, experience or any context we are used to. Hence, we may say that we are overwhelmed with constantly changing raw data, and strategic actors especially tend to be short of more rapid, up-to-date, valid and in-depth understanding of the transforming business landscape and social environment. Strategic intelligence is an emerging field of business consulting, which aims to undertake the task of revealing large, complex or complicated issues of transformation in a more understandable form. Pattern management, however, can be seen as one field or one approach of strategic intelligence. It is an approach that may, on one hand, be based more on empiric data and formal structures than other forms of strategic intelligence, but, on the other hand, it is a very heuristic approach to integrate quantitative data, reasoning and narratives. The main attempts of this article are, first, to show, what are in general the most commonly used ways of managing, finding, drawing, reasoning or anticipating patterns from our environment, and second, to locate how the concept of pattern can be understood in different ways. From the gathered knowledge, this article presents three main categories of reasoning patterns: empirical calculation (EC) is common especially in enterprise consulting. Theory proving with observations (TPO) is common especially in natural sciences, and real combining (RC) is common especially in qualitative research and in narrative.
    Keywords:Pattern management   Emergence   Reasoning   Strategic intelligence   Sense making   Data mining   Weak signals   Complexity
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