How data quality becomes undisputed

We work a lot with data in greenhouses. More and more. I often hear objections and problems related to working with data. And the quality of the data is almost always the reason. It's true. If the data falls short, if it's contaminated or simply incorrect, this will logically affect everything else, such as your reports. Annoying!

Errors in data can be very diverse. Manual input is a common cause. But linking different systems, with no clear protocols for data exchange, can also be a logical source of misery.

Many different data solutions are proposed in the market. Better tooling, better documentation, user education and training, etc. However, I am not convinced that this is where the real solution lies. The most important thing is the right incentive to handle data carefully and to focus on quality. Such an incentive is crucial. Without that incentive, there's no point!

The question then is: what is such an incentive? In my opinion, the most important incentive is that working with data brings indisputable value to your business. That it's not only convenient but forms the core of your processes. For example, without the use of data, you would achieve consistently lower crop results. That's what happens when you put data to work for you, for example with the help of algorithms. If you know that your operational process will run perfectly with the right data, then logically you will devote much more attention to the quality of that data. After all, there's a direct link between data quality and your operational business results.

Algorithms enable you to optimize your operating result. Algorithms put data to work for you. You can grow, improve and avoid mistakes. They also help identify which data is deviant. In addition, working with algorithms makes it immediately visible what data does and does not add value, for example in achieving a better crop result. This helps tackle only those data problems that are of real importance. Which saves time and money. And the implementation of data improvement in your company has a noticeably positive effect.

Many growers use an Excel sheet to keep a grip on the operation. This is always retrospective and very laborious. It is something that is addressed on a daily basis. My call is therefore to shift some of this fine and good attention to concrete innovation projects related to data analysis and automatic decisions based on data. This is hard, but it can be done! A good example is autonomous cultivation: the data that you already collect can then be used to optimally control the systems in the greenhouse in terms of climate, energy and cultivation. Algorithms work in tandem, continuously, and process far more data than us humans could ever do. Steering with algorithms is very close, but it requires a different approach. If you embrace this, better quality will automatically follow. I'm curious... Are you actively steering for data quality? Are you aware of the impact of data in your daily operations?

Source: Goedemorgen