The value of data for product innovation and machine learning

Data is big news. From personal fitness wearables to air pollution sensors on buildings, tracking user behaviour, variables and efficiency is everywhere. The question is, are we making the most of the data? Mostly, monitoring and tracking are directly linked to the user experience. For example, tracking delivery vans or autonomous delivery pods enables logistics companies to improve supply chain management and save time and cut costs. Tracking footfall in shops helps retailers to deliver target advertising or mobile Points of Sale for different customers at different times of the day.

What about products?

A different use for data is gathering speed inside Enterprises. Embedding connectivity within products means that Enterprises can collect data on customer behaviour and usage right from what happens at in-store display stands – what shoppers do when they try out the device in store, how long they play with it, how many times they circle back before finally making a purchase. This is just the beginning and it will create positive disruption for product innovation and development.

Traditional methods of tracking product success might involve focus groups, feedback requests, tracking sales and returns, reacting to customer complaints or comments, to name a few approaches. All of these take time, are not necessarily 100% accurate or reliable, and are mainly measured in patterns and trends, rather than on an individual basis. By enabling their products to relay real-time data back to them from the field, Enterprises can receive constant feedback and insights on performance and efficiency.

Customer insights

User behaviour and usage patterns are notoriously difficult to measure as they depend on small samples of returns/feedback, surveys or select customer monitoring. But if Enterprises are able to monitor patterns of behaviour through sensors embedded in their products, customer insights suddenly become much more accessible and meaningful for product innovation and development. In terms of improving user experience and planning for maintenance, repair or replacement, real-time data is the only real way to gain accurate insights. For design and planning, innovation teams can also access information on which components of a product wear out quickest or need improving.

Maintenance

Delivering updates or carrying out software maintenance also becomes much simpler if products are connected. eUICC technology makes product connectivity a seamless experience for the user and gives the OEM or Service Provider flexibility and control. Cellular connectivity just works without the need for pairing with smart-phones or WiFi access points. This assured connectivity enables software updates to be deployed remotely so that fixes and upgrades can be installed without disruption or inconvenience to the user or device itself.

Of course, the sheer volume of data creates the need for accurate and efficient data management tools. Specifically designed platforms are required to collect, analyse and provide meaningful analysis on data.

AI data management

Data management and analysis is becoming ever more sophisticated with innovations and developments in AI and machine learning. We may well be heading towards a time when robots are able to analyse large volumes of data and real-time streaming analytics are fused with machine learning to provide multiple iterations of data analysis and product monitoring.

It isn’t just the data management that could become more efficient through increased input from machine learning and AI, but the product innovation and software development in reaction to analytics and insights.

As innovation and development become more rapid and dynamic, simplicity and security of deployment become even more important. Secure connectivity and scalability become essential factors in an Enterprise’s capability to deliver product innovation and updates to reflect insights.

Interoperability

Integration and interoperability of systems within Enterprises suddenly becomes much simpler with this kind of connectivity strategy. Gone is the need to wait for a report from a different department before making product development decisions. With all the information easily accessible on a cloud-based platform, informed maintenance and innovation can happen more smoothly and easily.

Security, interoperability and authentication are all critical components of what is needed in IoT – emerging technologies like Blockchain and middleware solutions from Iotic Labs are helping to address this. IoT and AI will only flourish when data can be anonymously shared or exchanged in a way that enables Enterprises to augment their own device data with data from other Enterprises, or even from public crowd-sourced data. Read more about eUICC here and about Iotic Labs here.

 

 

 

By Arkessa

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