Pretty much every industry in today’s digital age relies on big data to mould future strategy and solve business problems, but many organisations are still nervous about getting into big data due to the various challenges that accompany it.
Collecting data is only worthwhile if you are able to do something with it and that has so far been the message of the day when it comes to the rapidly-developing world of smart cities.
At Smart to Future Cities in London today, the early focus has very much been on the importance of being able to drive insights from the huge amounts of data that is now being collected across our urban areas.
Karl-Filip Coenegrachts, chief strategy officer for the city of Ghent, kicked things off by speaking about creating knowledge from data. Creating detailed and pretty reports is all well and good, he said, but understanding how to interpret that and knowing it relates to the real world is vital.
These thoughts were echoed by Alex Montgomery, UK business lead for IoT, AI & advanced analytics at Microsoft, who said: “Data is absolutely king, data is the key currency people look at these days. But the key point around that data is we need to be able to drive insights.”
And these insights need to be linked back to the city’s specific goals, whether that be increasing efficiency, reducing operating costs, driving intelligent service delivery or boosting productivity.
This is where technologies such as artificial intelligence (AI) and machine learning come in. These tools, used across cloud platforms like Azure or AWS, can provide the insights needed to create services which improve how a city operates.
There are examples of this happening all over the world. An engineering company in Japan, for example, is using Fujitsu’s AI platform for predictive maintenance, helping to detect potholes in roads before they appear on the surface.
The city of Melbourne is investing in various smart city initiatives to improve the health and safety of its citizens and Cardiff is using IoT data collected from over 3,000 sensors to ease congestion and reduce air pollution.
These examples show that when you don’t get bogged down by the data and instead focus on the possible outcomes, innovative services can be created.
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