A typical product design process is a knowledge-driven process with very little data involved. Product designers and engineers tend to trust their knowledge and expertise as the main resource for product design decision-making and product brief writing. Involving data in a product design process can improve the design quality, reduce costs and shorten time-to-market. In this article, I’ll explain what a data-driven product design is, what its benefits are, why it’s not common, and how to implement this useful method.
What is the difference between data and knowledge in the context of product design?
Data is a collection of individual facts. For instance: a list of materials’ prices. Knowledge is a meaningful acquaintance with facts, principles, methods, or practices to understand or perform a specific subject. For instance: the ability to choose the right materials for a product.
Data without knowledge is meaningless. The data is (among other things) the knowledge’s structure. The knowledge is the meaningful needed skill to make decisions and act.
Knowledge is more complex than data. Yet, the human brain can comprehend and remember knowledge much better than data because knowledge is based on logic and data is based on many random details. This is why the human brain prefers a knowledge-driven work over a data-driven work. It’s simply easier and faster to perform.
What is the problem with a knowledge-driven design process?
There is no problem with a knowledge-driven design process (in fact, it’s a must), but rather with the lack of a data-driven design process. Knowledge is based on data, and some data tends to change over time (suppliers, prices, new materials, etc.). Continuously using the same knowledge without refreshing it with updated data can lead to inaccurate knowledge.
Let’s see how a product’s materials-choosing process would look with a knowledge-driven process and a data-driven process. Knowledge-driven process: The engineer chooses the product’s materials based on his knowledge and then reviews only the chosen materials’ data (sheet) for final approval. Data-driven process: The engineer searches and reviews a wide range of optional materials’ data (sheet) and then chooses the best materials based on the data.
The knowledge-driven process is faster, but it might result in missed opportunities for better and more proper materials. The data-driven process requires more time and tedious work, but it can lead to a better and smarter decision making process. A data-driven process is not replacing a knowledge-driven process, but take place alongside with it.
Product designers and engineers tend to skip data-driven design methods because it requires a lot of data searching, analysis, and because there are no good tools to support this process.
Implementing a data-driven design method
The main problem with a data-driven process is the huge amount of data involved in any of the product’s attributes; you just can’t find and analyse it all. I recommend choosing one or two main attribute(s) of the product—the one(s) with the most potential to improve the product, and then to perform a data-driven process only on for these attribute(s).
Implementing a data-driven product design method alongside the common knowledge-driven design method can improve the product design quality, stimulate innovation, and reduce time-to-market. Product designers and engineers should make the needed effort to learn how to use this this valuable tool and implement it.
My next post will be about “Designers’ tools for non-designers”
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