‘Given that the commercial environment is awash with risk and uncertainty, few leaders will want to trust important resourcing and investment decisions to gut instinct. Indeed, risk management may be the top priority in times of crisis, but what if business leaders could avoid the crisis in the first place?’
The contention in this piece is that Organisational Sensemaking has the power to convert ‘gut instinct’ into not only verifiable and actionable data, but also to lay the foundations for predictive analytics by augmenting traditional empirical data sources with the latent knowledge and experience that abounds in all organisations.
Sensemaking is an area of science much used to deconstruct and reconstruct major disasters where people have played a role in making a bad situation worse; when their sensemaking processes have been overloaded. It refers to the way that people make sense of the world around them. This concept is based on the idea that individuals and groups are constantly trying to make sense of their experiences and surroundings, and that this process is essential for successful adaptation and survival.
Sensemaking is a continuous process that involves both cognitive and emotional elements. It involves taking in information from the environment, filtering it through our existing beliefs and biases, and then interpreting it in a way that makes sense to us. This process is not always rational or logical, and can be influenced by a variety of factors, such as personal experiences, cultural norms, and social pressure. Key is that sensemaking is not an objective process, but rather a subjective one that rapidly assimilates broad swathes of cues and clues into [often] generalised, plausible meaning. This means that different people may interpret the same information in different ways, depending on their individual experiences and perspectives. This is, inherently, a social activity, which means that individuals do not make sense of their experiences in isolation, but rather through interactions with others. For example, we might seek out the opinions of others, share our own interpretations, and engage in discussions and debates in order to better understand the world around us.
The allure for leaders to be able to tap into the shared meaning that their people have about important and ongoing issues in their organisations is obvious, however, its main weakness is that the theory of sensemaking does not offer a clear framework for how people and organisations actually go about making sense of the world. Instead, it tends to focus on the general idea of sensemaking as a process without providing concrete steps or strategies for actually doing it. This is why Tensense has mimicked and harnessed the power of sensemaking through the collection of data and its analysis by use of powerful software and an interpretive framework that provides organisations with actionable insight; more than ever, the call is for those insights to come from intelligent foresight.
‘The CEO ……is charged with identifying the issues that span the enterprise and formulating a response that brings all the right resources to bear. To do that well requires a broad range of contradictory perspectives: outside in and inside out; a telescope to see the world and a microscope to break it down; a snapshot view of the immediate issues and a time-lapse series to see into the future.’
This article by McKinsey gets to the heart of the tensions that we seek to remedy for leaders in organisations. Organisational Sensemaking offers the ‘span’, it synthesises a ‘broad range of contradictory perspectives’, and ‘a snapshot view of the immediate issues.’
We also believe that Organisational Sensemaking offers ‘a time-lapse series [of views] to see into the future’ because sensemaking, when used as a tool, can be used as part of predictive analytics by helping organisations to make sense of complex data sets and identify patterns or trends that may be difficult to see otherwise. This can involve using a variety of tools and techniques, such as data visualisation and machine learning, to identify and interpret the underlying meaning of data, and to develop predictions about future events based on this information. By using sensemaking as part of their predictive analytics efforts, organisations can gain a deeper understanding of their data and make more informed decisions about how to use it and achieve their goals.