Making sense of knowledge and data-driven insights

by | Jul 30, 2021

Making sense of knowledge and data-driven insights

by | Jul 30, 2021 | Blog

Making sense of knowledge and data-driven insights 

Joe Dreyer, BI Consultant at PBT Group 

In my previous blog, I discussed the knowledge economy and why it is important for organisations to realise the value of effective knowledge management. And while technology plays a role, it is still all about the people in the business to make it work. In this next piece, I want to build on this and examine how companies can best make sense of the data they have at their disposal. 

The IBM ebook, ‘Enterprise knowledge management’, makes it clear that those organisations that want to become truly data-driven must go beyond managing their data and take a comprehensive approach to managing their enterprise knowledge. It states how this establishes a semantic and technical foundation to drive business and technology choices. Moreover, when business users can influence the technological landscape and means by which information is managed and governed, the technology part of the organisation can focus on strengthening the capabilities that provide this freedom and control. 

But it is often not as clean cut as going the one way or another. 

A recent article published by the Harvard Business Review (HBR), ‘When an educated guess beats data analysis’, mentions that managers in a study indicated they did not rely on analysis any more than on their instincts or some of the simple heuristics, despite the huge interest in big data, when it comes to innovation. 

It found that heuristics and ‘gut feelings’ offered a better trade-off in terms of decision-making speed and accuracy. The inclusion of analysis in the decision-making process did not bring about any meaningful improvement in accuracy while significantly reducing speed. 

More than a feeling 

The question to ask here is where does this gut feeling come from? 

It comes down to the concept of sensemaking for organisations. This is a relatively new theory which Karl E. Weick introduced in the ‘90s. Metaphorically speaking, when cues do not fit into a frame in our brain, sensemaking happens. Frames can loosely be described as memory gained from experience and learning. Cues are the signals received. So, sensemaking happens when companies are faced with unknowns usually caused by uncertainty or ambiguous situations. 

Managers who are faced with uncertainty are essentially playing out sensemaking. While big data analytics (the signals sent out) are available, a new frame is created in their brain. In turn, the brain filters cues to get to a point of understanding, which is called meaning and ultimately put into memory. The brain will only commit something to memory if it makes sense. Take maths as an example. As soon you know the meaning, you will be able to solve mathematical problems. 

Making sense of it 

We can use the Agile approach to put this sensemaking into practice. 

One of the main differences between the Waterfall approach and Agile is knowledge repositories. When using Waterfall, explicit knowledge is stored in documents and governed. In the Agile world, there is a reliance on tacit knowledge, trial and error, and communication among team members. 

In an article published on Information & Management, the authors refer to the use of lightweight, informal knowledge repositories in either non-digital (e.g., storyboards) or digital (e.g., LeanKit). 

Weick introduces seven properties of sensemaking in organisations: 

  1. Grounded in identity construction 
  1. Retrospective 
  1. Enactive of sensible environments 
  1. Social 
  1. Ongoing 
  1. Focused on and by extracted cues 
  1. Driven by plausibility rather than accuracy 

I am sure these look familiar to you. Of course, you are welcome to read up more on all these properties. But getting back to knowledge management and what the impact of ‘making sense’ and ‘sensemaking’ are in an Agile environment, the argument is as follows: The managers in the HBR article used sensemaking. They did not have much information supplied by the big data analytics to make sense from. They simply used their ‘gut feeling’ to get to a possible solution. Ultimately, this was done because of their learning and experience. 

To summarise – you must learn to trust your gut feeling (sensemaking) when you have the domain knowledge! 


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