Updated: Oct 12
Is your KM programme using data-driven insights that create intelligence capital and what are you doing to link this to Lessons learned?
"If we could first know where we are, and whither we are tending, we could then better judge what to do and how to do it." (Abraham Lincoln, House Divided Speech)
I want insights, data-to-intelligence that creates value. Some might call this Intelligence Capital. Without those insights, how can we know where we are or where we are heading? Without those insights, we are blind. Some might say we are blindly ignorant because the data and insights are there if only we could see them.
Intelligence Capital is, in essence, the information, evidence and insight an organisation collectively accesses, circulates and acts upon (MRS.org.uk)
Insights driven KM
Knowledge Management has to be of the people, for the people; KM is a function of PEST (see below). Therefore, I want to know what is happening in a given time and space. What is a given group of people thinking, seeing, hearing, saying, feeling at a given moment in time? What is it that they know, and, more importantly, they don't know?
To create meaningful and valuable data for insights, you need reliable data. Reliable data requires reliable data collection tools that can cross organisational boundaries, allowing for harmonisation and exploitation at scale.
If you are wondering what I am speaking about, think of a Project Management Lessons Learned template. Suppose your project teams use different templates according to where they are in the business. In that case, your data lacks harmony (comparing apples to apples), portability (movement across boundaries) and scalability (meaningful boundary-spanning actions).
The greatest problem for any [organisation] is that of developing its resources to the utmost. The solution of this problem involves a thorough knowledge of all resources – natural, intellectual, manual and financial – and thorough knowledge of all means of making the most of them. (Nutting, 2018)
The 7 Vs for rapid data-to-knowledge
To develop an insight-driven KM programme that is meaningful and valuable, you will need:
Speed - rapid data, quickly collected and presented
Reliability - not only in terms of the data collection tool but in terms of variables such as a sample size to represent a valid insight (e.g. see the law of large numbers and regression to the mean)
Requisite Variety; in other words, it represents the population and, therefore, create value.
To be seen (e.g. known to exist and visible on a KM dashboard).
Ultimately, the data needs to lead to viable insights.
These points are what I call the 7 Vs of data-to-knowledge: Velocity | Veracity | Volume | Variety | Value | Visibility | Viability
Insights from unlikely places
As a profession, Knowledge Management needs a greater awareness of the meaningful and valuable data generated in an organisation. For example, take a Management Development Programme within an Enterprise University designed for managers across global business units.
Here, Knowledge Management can work with the Learning and Development team to hine and access insight data. For example, managers take on micro-experiments to improve learning depth, completeness, and security (leanring attainment or learning to action for impact). These micro-experiments relate to challenges and opportunities in their workplace, which bring insights into people and their experiences in a given time and space. The micro-experiment results in a reflective log (think lessons learned), built using adult learning principles that collects data on the rationale for the experiment, actions, impact and results. The value to the author is that the reflective log links to the leadership competency framework, creating evidence for in/formal appraisal processes and the annual bonus scheme: the better the template (data collection tool), the better the insights.
The data from the reflective log is manna from KM heaven, feeding insights that amplify intelligence capital and, therefore, KM's influence within the business. However, it needs a good data collection tool.
Insights start with the collection tool
A well-constructed data collection tool can surface insights around Beliefs, Attitudes, Skills, Knowledge, Experience and Talent, linking these to actions, impact and results - thinking variables suchas as Safety, Time, Inovation, Quality, Cost, Experience.
Take a look at the following project lessons learned template (reflective log) from a project I worked on with E.ON Energy in 2016.
Do your lessons learned leave learners thinking, what's the point or so what? If so, you have a significant opportunity to create something more powerful. Here, I helped E.ON to develop more powerful KM insights using a the template (below), which resulted in a new SharePoint site (see the video).
Can you see the difference in the data capture tool and what it means for knowledge insights and the creation of intelligence capital?
Back to my opening question: is your KM programme using data-driven insights that create intelligence capital and what are you doing to link this to Lessons learned?
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