The Internet of Things (IoT) are already connecting devices and appliances at our home, school and office. In some countries, Smart Cities are connecting devices on a municipality level. Before we know it, we’ll be living in a smart country and a smart world. IoT has given consumers a whole new level of customer experience. Imagine one day Alexa can advise you what your next dinner menu will be based on “her” learning of the history of your food choice against the time of the dinner, what day of the week / month, your shopping budget, availability of seasonal food etc. The smart fridge can place grocery shopping order to fill up your fridge/freezer based on its learning of the history of your fridge content. Alexa and your smart fridge work hand-in-hand together to ensure you have stocked up the ingredients ready for the dinner Alexa has recommended.
The increasing popularity IoT has been evidenced by the rise in the sale of Alexa and Smart Fridges. The benefits of IoT are not just for consumers but also the service & product providers. The increase of IoT also means the increase of data in parallel. Before the invention of e-Telepathy, I think the communication between the smart devices and the data centre (sorry, Cloud still needs a physical storage!) is still predominantly through data transmission and exchange. Service providers are holding zettabytes of data. What are they doing about it? How do they utilities the data to help their business grow? How do they make more than what they’ve got in terms of data exploitation?
Zettabytpes of data are beyond human’s comprehension to handle them manually. Yes, we need some help from the computer. Data Analytics tools have been around for over 2 decades. There are thousands of Data Analytics in the market place. How good are they to support IoT?
Business would need an analytics tool to analyse data from multiple databases both internally and externally. The analytics tool should support the input of business rules, algorithms, artificial intelligence and machine learning. It should have graphical user interfaces to display dashboards with graphs, charts, text and images. It should generate alerts, notifications, conclusions and recommendations for actions to the business. It should run on real time to assist business in responding to change in a timely fashion.
For example, smart meters are one of the IoT appliances. Currently there are 14 million 1st generation (SMETS1) smart meters already running at the UK’s households. There are now nearly 2 million 2nd generation (SMETS2) smart meters which have been installed at the UK’s households. The government’s target is by 2022, another 30+ million SMETS2 smart meters will be at the UK’s households. Utility suppliers are handling hundreds of millions of service requests and meter response messages on a daily basis.
A data analytics tool should be able to capture and analyse data on a multiplicity level as illustrated in the below example:
As illustrated in the above diagram, the data analytics tool connects to the data from:
· Billing
· Imbalance Settlement
· Wholesale tariff
· New Channels
· Customer Satisfaction Survey
· Service Desk Incidents log
· Customer Service Desk
· Engineer Shift Roster
· Network coverage
· Consumer’s switching energy suppliers
The tool should be able to yield the benefits of providing impacts and assists the business to make more informed decisions to improve performance, to reduce cost and to grow. Here is the summary of the benefits:
| Data | Purpose | Impact |
| Consumer’s tariff | The calculation of revenue by the utility suppliers charged to consumers based on their energy usage | To more accurately set the tariff |
| Smart Meter Service Requests & Meter Responses | To remotely obtain information and provide services | Automated processes to provide efficiency to utility suppliers and to improve customers’ convenience and experience |
| Billing | The exchange of invoices against payments | To more effectively check incomes against expenditure |
| Imbalance Settlement | The settlement from utility suppliers to wholesalers of discrepancies (for each half our trading period) between the amount of electricity that the utility suppliers have | To reduce imbalance settlement payment hence to reduce loss |
| Wholesale tariff | The calculation of revenue generated from utility suppliers based on energy the wholesaler has provided | To more effectively check incomes against expenditure |
| New Channels | PESTLE factors | Decisions on business strategies in response to external PESTLE factors |
| Customer Satisfaction Survey | Performance measures | To listen to the voice of the customers and to improvement customer services |
| Service Desk Incidents log | Performance measures | Quality Control and to improvement customer services |
| Customer Service Desk | Workforce Planning Performance measure | Decisions on utilisation of resources Decision on training, recruitment, retention |
| Engineer Shift Roster | Workforce Planning | Decisions on utilisation of resources |
| Network coverage | Quality / Standard | Quality Control and to maintain / improvement performance |
| Consumer’s switching energy suppliers | Customer movements between utility suppliers | To check customer’s switching against Customer Satisfaction Surveys and Service Desk Incidents Log to reduce customers’ departure |