Thanks for highlighting this incredibly fascinating concept in practice, Viria. To address your question, what scenarios does open innovation make sense for a company, I am concerned with the ability for companies to act in a timely manner when every idea is developed through this process. Thus, I would consider this to be an important restriction. I would suggest that companies should consider this tactic in innovation when the task is so large that the company itself can’t solve the problem. The easiest example at hand, while not a company, is the open concept of the Hyperloop. The development of this project has to be so incredibly unique and different from most previous lines of thinking that it takes the entire population thinking about the challenge to make any significant progress. The unfortunate part of business today is the pace of change. The problems that most companies are concerned with are typically ones in need of immediate attention, thus the time spent crowd sourcing can be a significant pain point over the consistent long term.
Thanks for sharing such an insightful article, Tasnia. While there is a big challenge in keeping the innovators engaged, are you at all concerned with the company losing it’s “innovator” brand image? My concern is that by going to more direct stores such as Sephora and others, you risk alienating the innovators who view you as an anti “big makeup” brand. The challenge here is not only keeping the innovators engaged in designing new products and providing feedback, but also doing it for your company and not another.
I do really like your idea of innovation from within, to go along with the crowd sourcing component. Over time, your employees are the ones who truly love your brand and value your platform. However, this cannot replace the full effect of large scale input from the masses. Thus the importance of keeping the community connected, as you mentioned will remain crucial as the business scales.
Great choice of application of this very radical use of this technology. Given the challenges with regulators, coupled with highly entrenched companies, do you see that there are any barriers that will be impossible to overcome? Most notably, I am concerned with the large conglomerates with deep pockets to pay for lobbyists to command control of regulation.
I do see this as a disruptive technology that should replace traditional construction in the long run. However, to enable more rapid adoption, I do see the benefit to making the practice more widely accepted by being a complementary component in the short run. The goal should be to make this more widely accepted, which will come by making it highly visible and economically unquestionable.
Interesting read, I hadn’t fully appreciated the benefits to the aerospace industry since my beliefs were that materials needed for space travel would be too complex to utilized additive manufacturing. While I agree that there are challenges with meeting the scale requirements for rocket design, I concern myself less with this aspect as my belief in the same human ingenuity that reduced costs to get us to the moon will allow us to find ways to get us to mars. However, one different potentially large opportunity for additive manufacturing in the aerospace industry could be “on-site” repair work. For example, as machines fail with great variability over long flights, the ability to produce spare parts on flight could be immensely beneficial to the safety and efficiency of the flight.
Further, I do agree with the need to bring 3D printing specialization in house, however, I do worry about closing off our base of knowledge if we don’t partner with research institutions that are pushing the limits of 3D printing. I would suggest a combination of internalization of 3D printing specialty with a partnership with a research organization specializing in the large scale manufacturing with the use of additive materials. The goal being to have the largest breadth of knowledge and expertise that we possibly can.
Terrific read. I agree with your points on the “black box” concerns raised about the classification of planets and solar systems. In particular, the concern with the public apathy could be further amplified as people begin to understand that the conclusions that NASA is coming to are not a product of human discovery but rather machines taking data and machines processing that data. While I agree that this process is simply highly automated, the general public opinion is still that NASA is “confirming” the existence of planetary objects with the human eye, but this is simply implausible. The data that is being collected is in fact very raw and messy, thus the use of machines to sift through it is critical, as you lay out, in order to make any meaningful progress in feasible process times.
One concern I have with the use of ‘amateur’ scientists is the issue with sub-par inputs leading to lower accuracy ratings and more work over the long run for NASA scientists. In order to accurately process these data sets coming from satellites, a scientist has to be fairly advanced in their computational capabilities. Because of this, I would recommend specifically targeting academic research institutions to aid in processing this data as these are the scientists that will likely be closest to competency and perhaps one day competing for a job at NASA or a competitor.
Mike, very informative and intuitive description of how machine learning is being utilized by First Data to try and get ahead of the move to more complex fraudulent behavior in the digital marketplace. I fully agree with your short for First Data – getting to market quickly and targeting your largest data source will allow your machine learning processes to build it’s data set to base it’s decisions on. Long term, I am curious how practical it is for a payments company to migrate into the big data industry in an age of “internet of things” with companies becoming more inundated with copious amounts of data.
I do wonder how much the 3.5x increase in fraud from 2013 is due to fraudsters already employing machine learning or other advanced tactics. My read of this increase of fraud is that either (or both) 1) fraudsters are already becoming increasingly more technologically advanced or 2) payments companies have been lagging behind in keeping up to speed with it’s fraud detection practices. If it turns out to be the second case, then there is the potential for a steady state of fraud to be reduced below its current level and to return to the 2013 levels or lower with the incorporation of machine learning to course correct. However, if First Data is simply catching up with the fraudsters, then this reactionary movement could lead to First Data constantly being behind the fraudsters.