The Johari Window is a well known model developed by Joseph Luft and Harry Ingham in 1955 (‘Joseph’ and ‘Harry’ combine to create the rather exotic-sounding ‘Johari’).
This model used for a variety of purposes.
From personal improvement to team building, the Johari Window illustrates a common problem: a lack of knowledge. More specifically, it helps us to realise our ‘unknown unknowns’.
For the purposes of this blog, the Johari model will help us understand how data quality becomes neglected so that we can identify a plan of action.
What is the Johari Window?
The aim of the Johari Window is to allow the subject to see what they cannot already see. Then, they take steps to increase their knowledge.
At the end of the analysis, the subject knows more and has reduced the amount of knowledge that is lacking.
Without a diagram, it is difficult to visualise exactly what this means. This simple illustration shows us the basic concept.
(Image: Wikimedia/ Creative Commons)
There are four equal panels in the window: the Open Area, Blind Area, Hidden Area and Unknown Area. The horizontal (or X-axis) refers to others, while the vertical (Y-axis) refers to ourselves.
The aim of the exercise is to expand the Open Area – the window pane at the top left of the Johari Window. By doing this, we shrink the Unknown Area – the pane at the bottom right.
In the process, the Blind Area and Hidden Area are also reduced in size.
How the Johari Window Helps Data Quality
The Johari Window illustrates that we can improve our outlook by expanding our knowledge. We may not know the scale of the problem, and we may not know exactly what to do. But when we investigate and take action, we can reduce our ‘unknown unknowns’.
In the context of data quality, how is this achieved?
- Self discovery: We must understand that there are things we don’t know, even though we may not know it yet! There is always room for improvement, even if it’s not immediately obvious where the biggest gains will occur.
- Input from others: Data quality software can locate bad data, cleanse it, remove records and intelligently find matches. In order to improve our data quality, we need input from third parties in order to tackle the problem effectively.
There is a common theme linking both courses of action: we must actively seek improvement in our data. The Johari Window teaches us that we achieve better results when we look for improvements rather than waiting for them to happen by themselves. When applied to data quality, this is an important point.
Making the Change
Data decay is a fact; data has a natural half life. In order to improve data quality and maintain accurate data, we must make a concerted effort to commit to the challenge and keep sight of our data quality goals.
When we look for improvement in our data, and put measures in place to see those improvements happen, we realise the full potential of our biggest business asset; our information.