Business Insights Applications
Zensors is capable of leveraging crowd-sourcing and machine learning to distill data from images. We have discussed the applications of real-time monitoring and notifications. Today, we want to take a look at the data-analytics side of the story and how Zensors could benefit data scientists.
AI Landscape
To begin our research, we looked at existing offerings in the machine learning product space. Some of the products we looked at include AWS SageMaker, AWS DeepLens, and C3 IoT. We found that current machine learning offerings mostly target developers, researchers, and technology professionals. AI and machine learning technologies are more accessible than before thanks to efficient cloud platforms, but most existing AI products still require technical expertise to set up and maintain. Compared to large companies, which usually have in-house technology team or employs third party technology products, there’s a gap in what’s available for small to medium sized business to benefit from machine learning technologies.
Alternative use cases
Zensors’ primary use cases demonstrate strength in real-time monitoring and notifications. To supplement these use cases, we brainstormed alternative use cases of Zensors. Operating in the domain of machine learning and data science, we wanted to explore how Zensors could benefit machine-learning engineers and data scientists. One of the use cases we discussed was to use Zensors’ crowd-sourcing platform to train machine learning classifiers specifically for a context, but this idea was later discarded due to lack of competitive advantage.
Another proposal was to use Zensors data for downstream data analysis to generate business intelligence. In this use case, users utilize Zensors’ unique strength in generating data from physical spaces and use this data to fill the gap in their existing data workflow. This data could be used for efficiency optimization and process re-engineering.
Downstream integration
Once Zensors generate data, the next question is how would our users consume the data. For business that have already established data analytics workflow, they use analytics software such as Tableau or Microsoft Power BI. These tools help them visualize and analyze their vast amount of data to gather insights. Another possibility is for companies to have their proprietary software infrastructure to handle their data needs. In either case, we would need to learn how to integrate into users’ existing data pipeline and if there is a standard in their specific industries.
Conclusion
There’s enormous value in the data Zensors generates. The value of this data really depends on how our users choose to use it and if they can form effective queries. There are two approaches of delivering the value of Zensors data to our users: targeting users who have already established a data analytics workflow and integrating into their existing system, or educating new users on the value of data analytics and helping them set up their workflow. Either approach proposes a unique set of UX challenges, and we are excited to learn how to best deliver Zensors data to users in our user research.
