Analytics and Artificial Intelligence are fuelling the growth for Internet of Things
Setting the context
“The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment.” I think we are not so far from the vision of Warren Bennis primarily due to the rapid evolution of technology world.
IoT used to be about the connectivity of products, machines and objects but now we talk about the value that can be derived from the data these connected objects produce. IoT platforms have matured enabling businesses to quickly scale up technical solutions and resulting into mass adoption of this technology. IoT is becoming “Intelligence of Things” – making devices intelligent, not just focussed on connectivity but focussed on making sense of IoT data to create business value.
The real potential of data is in creating new efficient and customer centric business models. The emerging technologies of Data Analytics, Artificial Intelligence and Machine Learning have matured and can be used to provide actionable insights from the data IoT solutions are producing.
In this way IoT has symbiotic relationship with these technologies. IoT generates data to be analysed. AI helps us make sense of the data and create new business models using all these technologies together.
What is the impact?
Analytics, AI and machine learning are literally unlocking the true potential of IoT and therefore fuelling the growth of IoT.
Some reference use cases reflecting the true potential of IoT when used alongside these technologies are:
- predictive maintenance by anticipating failure before they occur
- real-time production planning and supply chain tracking
- shop floor automation streamlining production line
Getting the best out of it
Finding a way to derive business value from the data being produced by your IoT devices can create massive efficiencies in the way you do business and lead to better business models for both yourself and the customer.
Start with the use case – delivering new ways of thinking with a focus on innovation.
This is why we believe – in our position paper on AI – that the three conditions necessary for enterprise to adopt AI solutions are:
- Use cases – what is the business issue we need to solve or business outcome you desire.
- Computing power and analytics – how do you make sure you are able to manage the volume of data you are getting from IoT devices and elsewhere.
- Trust and compliance – for sustainable business models you will need a system you can trust. As you begin to rely heavily on the value of your data you must ensure it is secure and regulated.
Once you have these in place you can move to mass roll-out of connected devices – up to millions – ensuring that your data analytics platform is able to scale up.
Alongside the technical domain, ensure you have the business strategy to reflect the changing environment and End-to-End IoTservice management framework to support availability and quality of data. Understand the impact on workforce and how your business will need to develop and upskill workforce in line with these new solutions.
We are entering a new age of artificial intelligence but for the enterprise to take full advantage requires the right roadmap that brings benefits to all entities involved – business, workforce, customers, and partners.