There are three things that have to happen in an enterprise if Quality is to exist there. The first is that the management and staff come to know and appreciate what Quality is, the second is that they are committed to bringing it about in their enterprise, the third, and most important thing, is that they take the necessary actions to bring Quality into being.
Traditionally, what happens after the benefits of Quality have come to be appreciated, is that staff in each area of the enterprise then set about trying to improve the Quality for all of the tasks and products for which they have responsibility. The approach is used for both product quality and data quality.
The above approach will definitely improve the quality of the Business Functions and Processes over which staff have total control. However, in areas where they have only partial control, the level of quality will very quickly reach a plateau due to receiving input of low quality raw materials or data.
In the area of Data Quality the standard approach to this is to use software products to find and repair data errors. No matter how good these products are, this approach actually creates what is a never ending process of first creating and then finding and fixing data errors.
This ‘Quality Control’ approach to Data Quality has spawned a global ‘dirty data industry’ that actually thrives on the fact that, as data volumes grow, more and more data errors have to found and corrected. Its protagonists are proud of the fact that they are so good at finding errors – and do not see the irony of this in the field of Quality. They say that the concept of ‘zero defects’ might be feasible in all other areas of industry but that it is not achievable with data.
The Use of Lateral Thinking
In the late 1960s Edward De Bono introduced the concept of lateral thinking. In his book ‘The Use of Lateral Thinking’ he explained how problems that seem intractable when people use straight-line thinking, become relatively easy to solve when they are looked at from a completely different perspective.
At this time, the problem of river pollution was plaguing all industrialised nations. Local and national governments were trying to fight it, with minimal success. In the UK many large industries polluted rivers at will. The fines that local government had the power to levy against these huge corporations proved to be no deterrent at all.
In one of his articles, Edward De Bono, demonstrated how a simple, lateral shift in thinking could solve this problem at a stroke, without the need for any policing or punitive fines.
The traditional layout of manufacturing plants along these rivers was as shown below. The water needed for processing was taken in upstream from the manufacturing plant and, after being used, was discharged downstream from the plant.
In most cases this water was completely polluted and unfit for use by people or industry downstream from the plant.
Lateral Thinking Layout
So how can you, without spending millions of dollars to totally rebuild or reposition this plant, get the owners to reduce the pollution in the river so that it is suitable for everyone to use?
The answer is amazingly simple. Instead of allowing the plant to take its water from upstream and discharge it downstream, you simply reverse the flow. As the plant now takes its water from downstream of its own discharge, it is forced to make every effort to ensure that its discharge is pollution free, otherwise it ends up polluting its own processes.
Very interesting, but what does this have to do with Data Quality? Exactly the same type of thinking can be applied.
What you need to do is to think of ways in which all data input by anybody will end up effecting them in some way or other. It could be that they themselves are going to be using the data. When this is the case they would very obviously want to make sure that they enter high-quality data.
The real challenge comes when all of the data that is being entered is going to be used by someone else. What is the incentive to get it right?
This is when you have to put on your ‘Lateral Thinking Hat’ and ask, ‘How can we make all low quality data entry the equivalent of having the person responsible poo on the own doorstep?’
One approach I came across that worked very well was where data entry staff were paid a low basic wage, plus a high bonus based on volume of data entered. Each item of data entered added to the bonus, each identified error subtracted from it. This encouraged the data entry staff to have high productivity and low error rate. It was up to them to decide how much they earned.
What approaches would you suggest? Remember, in order to get the most innovative results, you will need to abandon ‘straight-line thinking’ and take a completely sideways or Lateral step. Viewing the problem from an entirely new perspective will, if you keep an open mind, enable you to come up with ideas that could be termed ‘crazy’, ‘off the wall’, ‘bizarre’ and, if you persist, ‘genius’.
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