The Impact of Bad Data in Automation: Why Quality Management is Critical
Can automation work without good data supporting it? The simple answer is very likely to be “no.” Naturally, the next question would be: “Why?”
To understand this, we must first consider the impacts that good—and bad—data can have on automation.
Automation can come in many forms, but essentially it is taking something that is run manually (by a person) and developing a machine or program to run that process automatically. This is quite a complex achievement when you consider all the potential variables that need to be “managed” by the automated process (AP). Designers of the AP need a very detailed understanding of the physical parameters, mechanical parameters and quality parameters to properly deliver automation.
Some aspects of automation are quite easy to envisage – like car production automation – where we often see images and videos of cars on the production line being constructed automatically by an army of robot arms. Other areas, such as the monitoring of quality and outcomes, are not so readily seen, even though they are there in the background. The computer systems that power an AP are not just there to direct the robots – they are very often changing the way the AP runs – making subtle changes based on tolerance test outcomes.
Analytical results and tolerance test outcomes are an area where data quality and management is critical. The AP will be required to deliver a product to a given specification, within certain tolerances. For example, in drug production, every pill has a concentration of drug product within 0.01% of target or every pill is within a certain range of size. These critical variables form the basis of success criteria and therefore product acceptance.
If the variables are not measured, stored and analysed correctly, then the AP will not deliver – meaning the product could have issues. Measuring variables is quite a simple process, but how accurate, precise and true the measures are, is very important. Each variable is slightly different, but you need to know these differences exist so that product quality can be assessed. And, since ‘trueness’ is a derivative of other measures, it must be calculated – and this is where the quality of the data is critical.
If the format and scale of the variable measured are not captured, you can expect complications. For example, if I collect data on a pill size, but I don’t note the scale, 5.567 could mean 5.567mm or cm or m. If the scale in this example is not captured correctly, it risks not being readable by a human or a computer.
This ambiguity introduces risk into the data process – you’re likely to be either guessing or estimating the meaning of something, not using its real meaning.
