Catherine Evelyn's Profile
Catherine Evelyn
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Thank you for this informative article on a surprising innovator! I was pleased to hear the ways that Sherwin Williams is focused on responding to their customer needs and designing clever innovations for them. I really liked your idea about directly including customer voting in the decision processing. I think this would be a very valuable path for Sherwin Williams to continue their innovation path!
Thank you for the article! This is a fascinating topic area. In particular, I would caution Siemens on the threat of vertical integration that you highlighted. As companies are looking to take on more of their replacement part production, I would suggest that Siemens focus on the most difficult parts and / or potentially include themselves within the 3D printing value chain (e.g., licensing 3D printing design tools).
Wow! 14 billion miles of driving data! It’s pretty impressive that Progressive has taken these steps to develop and implement their machine learning efforts.
Your article clearly articulated the business case and application towards the auto insurance industry. It also left me considering how Progressive might apply similar technology to other product lines (e.g., home, life, etc) and what the ethical implications are.
For instance, they could take activity data (e.g., FitBit / Apple watch) and combine with other inputs to better predict the health of individuals with life insurance policies. However, this application seems to have some sinister ethical implications. That being said, if they reward people for being “healthy” drivers with discounted auto insurance, should they reward people for healthy lifestyles with discounted life insurance?
Lots to think about – thank you for starting the conversation!
Thank you for your article!
You raised many interesting points on the applications of AI within the advertising agency. While I was initially skeptical, you have convinced me that AI can be a valuable technology even within a ‘creative’ field like advertising. Going forward, I particularly agree with your point on the importance of the data that is used to power the AI technology. In order for the algorithms to develop content that will resonate with customers, data scientists will have to appropriately design which data is used as a input.
Thanks for the article! After spending two years working on Fintech products to address the student debt crisis, I can certainly agree that there are many problems in this industry!
I think the biggest problem that servicers can address is the lack of transparency in the industry. Navient administers many different plan options, however, the vast majority of users in my experience don’t know the options that are available to them. Navient has the opportunity to ask their customers what elements are most important to them (monthly payment, years of payments, interest paid) and help them to select the right plan for them. Enabling this type of innovation will require active engagement and collaboration with users.
It’s great to learn about these initiatives being developed in Boston!
I believe that reliable access to quality data will be the greatest barrier to successfully applying machine learning across a broader range of governance problems. While I am pleased to hear that the city has made a strong effort to add procedures to its collection and storage of key data elements, given that the analytics team has already experienced high levels of turnover I worry about the sustainability of these efforts. Given the long implementation time for initiatives within the government, it is especially critical to hire a team that can ensure consistency in the city’s data strategy.
Thank you for the thought provoking post! It seems toys have come a long way since my Furby!
My apprehension with this toy comes from the data security and privacy concerns that you highlighted. In particular, I worry that the data on these children may be mismanaged. As such, it would be very important for Mattel to develop strong security and standard processes for how this data is measured, saved, and protected.
Thanks for the interesting article!
I am intrigued by the potential applications of the “signal combination” model you described. I believe that these models could present tremendous value when combined with the judgement of “fundamental” fund managers. As data and quant is increasingly incorporated into investing methodologies, I would wonder how we can ensure that these models are maintaining their fiduciary responsibilities to investors. As such, I believe that oversight will remain an important part of this industry.