Thank you for your comment Charu. Agree, and I think Predictive Analytics is a good fit because everything can be well-defined and measured.
Thank you for your comment, Kunal! I do not know that Apple has also invested in VR, and I will check it out!
Thank you for your comment, Caroline! I agree with you that both cardboard and Daydream are great move to develop awareness and adoption. However, if other platforms, such Oculus Rift, can provide much better experience at a low cost, they can easily attract adopters’ of Daydream since Google’s current moves do not create strong stickiness nor loyalty. Thus, I suggest that Google should focus on developing their own contents instead of relying on other players in the space.
Great post! I strongly agree with you that for mass adoption, VR needs hardware manufacturers, such as NVIDIA, to build light and convenient devices at low cost. Has anyone projected when the affordable VR devices will be available?
Very interesting post! Vertebrae is truly advant-guard and tackling advertising before mass adoption. Thus, my question is that how they can generate a strong consumer base? Since we still have not seen mass adoption of VR, do you think VR will require a completely different business model and hence a new way to advertise?
Great post! VR sickness is definitely a problem, glad that IonVR has taken care of this problem. Do you think major VR devices manufacture will pick up the MotionSync technology and integrate in its device, and hence leave IonVR no room to play?
Thank you for sharing! Satellite imaginary is a hot space. A lot of companies are trying to make better estimations (e.g. oil supply) or predictions (e.g. crop yield) with satellite images, and they differentiate by their analytics capability and types of analytics. Currently, I still haven’t seen a mature business model. From your research, do you know how Orbital Insight captures its value?
Great post! It’s exciting to see that Saildrone brings down the cost of ocean data by so much! The new business model makes sense because Saildrone may not have the expertise on data analytics. However, in long run, I think Saildrone need to build up its analytics arm or a platform for analytics to capture more values and increase customers’ stickiness. Otherwise, companies can choose to buy similar vessels and collect data by theirselves.
Very interesting! Thank you for sharing. Legendary is doing similar genome project on movies and books and has been pretty successful. Applying analytics on art helps with efficiency, but it has its limits: cannot identify masterpieces of next generation, i.e. art works completely different from the past.
Based on the announcement, Kaggle will run as a separate entity, and hence the business model will remain the same. If Google want to crowdsourcing, it still need to run competitions on Kaggle. I do not think acquiring Kaggle means acquiring its community. The deal is beneficial to both sides because Kaggle now have a better infrastructure support, and Google can promote its cloud service/platform and compete with IBM and Microsoft.
Thank you for sharing! MDP program is very interesting. Could you elaborate more on how it is different from a normal accelerator and crowdsource ideas from the crowd?
Thank you for sharing! It is an interesting idea. I know a start-up that built local news platform in China for similar thesis. However, the start-up failed. The founder thought the failure was that people do not really care about local news, and hence the platform cannot draw enough crowd.
Great post! The comparison between Tay and Weathernews is interesting and insightful. However, the algorithm that Microsoft and Weathernews want to build may be different, and it makes sense that Microsoft crowdsource from the public. Since Tay essentially will serve the public, it needs to be robust. I think this is a great lesson for Microsoft.
Thank you for the comment! Yes, I agree with you that domain knowledge is very important and predictive modeling is only part of the data scientist process. This is also why we need in-house data scientists. For the exact reasons, I think Kaggle should stay in the predictive modeling spaces and promote advanced algorithms.
If we take a closer look at the competitions, most of them involve image recognition and NLP, where a good understanding of algorithm is more important then domain knowledge.
In general, we can split data science tasks into two parts: one is to extract insight from data, which require domain knowledge and a lot of effort, and to build predictive models. Kaggle adds value to the second part if companies have already done the first part and confine the problem to a predictive modeling task.
Great article! I always find that Medium has high quality articles, and I now know the reason. What is its strategy to attract more readers? Readers are the key for platforms like this. Do you think its growth in readers has already reached a plateau?
Interesting analysis! In addition to Google, Airbnb is also competing in the same space, and it wants to become a super-brand for travel. It has instant booking and recently has released a new product called trips. Curious about your thoughts on this.
Very interesting business, and it instead need large pool to make matching efficient. Have you heard Ripple, which is tackling this problem in a similar way? It helps bank to settle transactions cross borders to fast, efficient, and at low cost.
Indeed! I think for the next, HW is as important as SW
Also, is there any social network or e-commerce company want to enter the space? For example India’s version of Facebook or Alibaba?
Great article! I have heard the concept of replacing intermediate in the insurance market quite often, and it is interesting to see an insurance that truly achieve it. Just to clarify, before IDI, people only buy insurance through agents? I understand that IDI was able to optimize pricing through data analytics. However, where was the data to start with?
As the era of internet of things is coming, what is IDI’s next step?
Very interesting! Danske Bank is really innovating! I have not really seen a traditional bank entering in mobile payment so successfully. It is also exciting to see that the Mobile-Life is extending other banking services to mobile.
Out of curiosity, I have two questions:
– How easy is for the merchant to set up mobile payment option?
– Has the bank attracted new customers? If so, how different they are from existing clients, and have they develop new products for them?
Paytm is indeed winning! I have few questions:
– if Paytm is granted with bank license, does it allowed to lend?
– Will Paytm tap into consumer financing area?
– Given there are many booming technology in the payment industry, such as blockchain, has Paytm also invested in new technology?
– What is the logic behind BHIM? Is it more like a Visa or something completely different?