On the surface, it seems as if “big data” and 3D printing would naturally overlap. When you think about it though, they don’t often do so in ways that are readily apparent. Despite being a (relatively) new, advanced technology, 3D printing doesn’t necessarily need big data to operate. Where most popular applications are concerned, in fact, the two have little to do with one another.
With that said, however, it would also be incorrect to assume that there isn’t any overlap at all. There are in fact various interesting ways that data science intersects with modern 3D printing – and accordingly, there are jobs to be found for data scientists in the world of 3D printing. In our article on ‘Reasons Why a Career in Big Data the Right Choice?’, we explored how the demand for data scientists is increasing as more and more organizations are acknowledging the significance of data analytics. Below, we’ll take a look at some of the specific areas of opportunity related to this field.
Before delving into a few ways in which data scientists can involve themselves in the actual practice of 3D printing, it’s worth noting that there is also a need for analysis about the 3D printing industry. It’s not often, after all, that we see entirely new methods of manufacturing beginning to take hold. In this case, it’s happening in everything from the design of handheld consumer products to aerospace engineering, which necessitates a great deal of market analysis.
A brief look at the overarching state of 3D printing provides some glimpse into what this sort of data analysis might look like. IDC posted a statistical breakdown just last year and revealed some of the big numbers people are beginning to keep track of worldwide spending projections ($22.7 billion by 2022); a five-year CAGR (19.1%); specific spending on 3D printing materials (two-thirds of total spending on an annual basis; and so on. The overview also includes a breakdown, as of early 2019, of the 3D printing market share by industry: 53.8% for discrete manufacturing, 13.1% in healthcare, etc.
These are broad numbers meant to provide a sort of snapshot of where 3D printing is now and where it’s headed in the near future. Clearly though, there is potential for much deeper analysis regarding the breakdown of activity by industry, spending, material consumption, and more. Given all of this, data scientists can be invaluable hires for companies seeking detailed insight and perspective relating to 3D printing.
One misconception about 3D printing tends to be that it’s meant for some combination of niche hobby projects and forward-looking experimentation. These indeed are the types of projects that tend to make for fun headlines, and thereby generate attention. In between them, however, 3D printing’s greatest utility is behind the scenes, in industries building product lines and trying new parts for countless applications.
As a leader concerning this industry-scale 3D printing, Fictiv claims to have manufactured more than 10 million parts, from early-stage prototypes to full product orders. As an example of business-oriented 3D printing ventures, this is an immense number, and undoubtedly a complex one as well when it’s broken down into different kinds of orders, the various materials used, how long each project takes, and so on. And naturally, all of that complexity – especially when multiplied across manufacturers and endless orders – yields a vital need to keep tabs on any and all data relating to production.
Specifically, in another interesting piece identifying some of the connections between 3D printing and big data, Smart Data Collective covered the need to monitor manufacturing such that any problems or inefficiencies can be identified and corrected. This means deploying relevant sensors and equipment, gathering virtually innumerable data points at the industry level of production just described, and analyzing that data to improve practices.
As any good data scientist knows well, clear visualization of findings can be as important as the findings themselves. While spreadsheets and similar groupings of numbers may read easily to those who handle complex data on a daily basis, the people paying for the analysis often benefit from something easier to understand and refer back to. This actually leads to fairly steady innovation in chart presentation; for instance, it wasn’t long ago that Viar360 pitched virtual reality as a new asset for data presentation. Through this technology, it’s possible to interact with and manipulate graphs, models, and other representations in 3D, virtual space.
However, virtual reality is not alone in furthering data visualization. In perhaps the most direct example of data scientists’ relation to 3D printing technology, we have actually seen for a few years now that this technology can be used to create physical models that are representative of data findings. In short, a company looking for a detailed analysis that it can keep on hand can hire data scientists to gather and record information in a way that will be fed into a 3D printer in order to create a model.
Altogether, these examples make for a great deal of opportunity connecting data science with 3D printing. And as the latter industry continues to grow and expand, those opportunities will likely only multiply further.
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