Data is the new gold. But to leverage this data to develop smart cities, the raw data has to be converted into intelligible assets that transforms it into gold. The procurement of good raw data – defined as reliable, complete and collected in real-time – ensures that the final data sets are highly valuable.
How ‘good data’ helps in data analytics?
When there’s good data available, data analytics tools can be fully leveraged to get valuable insights into existing issues, emerging patterns, and future trends. Artificial intelligence can be used to run predictive analytics on such data, and generate accurate predictions that can be helpful for smart cities. Powerful data analytics platforms, like Microsoft’s Power BI, use artificial intelligence to generate extensive business intelligence reports via in-depth data analytics, using multiple data sources within a business’s infrastructure. The use of verified data sets improves the efficiency of such data analytics platforms significantly.
Addressing the lack of ‘good data’ in smart cities
We live in a world filled with powerful innovations like smart apps, augmented reality, AI-based object detection, and IoT. But our quality of life is still far from the standard we would like to achieve! Case in point? Traffic congestion and energy wastage, to name just a couple. This happens because we are not effectively leveraging data analytics to pinpoint the root cause of such afflictions. And we’ll probably never be able to, until we consider data analytics as a core part of our infrastructure, rather than as an afterthought. We need to think of a novel infrastructure for our smart cities, that places data monitoring, collection, and analytics at the heart of it.
An advanced infrastructure with data analytics at the heart
The basic idea behind the creation of a smart city is that it should be ever-evolving. New demands, new implementations, and new plans are supposed to be addressed in real-time as human needs evolve. The only way administrative professionals can keep a finger on the pulse of their smart cities and make changes in real-time is by leveraging predictive analytics. Prediction of high-crime areas or an increase in energy demand or water wastage can be done by using advanced data analytics tools.
And this is how the role of big data gets even bigger with smart cities. It’s no longer just about organized data sets, but also about procuring reliable data that can be fed into data analytics tools. Smart city administrations using modern ERP systems can exploit data analytics tools to get intelligent insights and actionable data for day-to-day improvements, new implementations, and future planning. Administrations running on old/legacy systems with unorganized datasets, can take help from their ERP solutions provider and migrate their entire data. This will help in aggregating, labeling, and structuring all data, along with storing it in one centralized cloud-based system.
Integrating data analytics in smart cities
To have insight into what technologies to implement and what direction to take planning-wise, data streams need to be holistic in nature. Every data generation point – sensors, cameras and electronic devices -needs to be taken into account to create a unified data collection, aggregation and monitoring channel, that can be linked to a real-time data analytics model.
Let’s see how data analytics can be integrated into various aspects of a smart city –
1. Public Transportation
Getting people from point A to point B is the goal. But to ensure that this keeps happening efficiently, routes need to be continuously optimized so that the least number of buses are deployed, while maintaining an efficient frequency. Analyzing data points like the number of passengers on specific routes, average journey length, most popular stops, and peak demand hours, can help generate useful analytics.
With this, administrators will get a good sense of how routes can be planned better, the number of vehicles plying on a specific route, and the frequency of the services on each route. This effectively reduces traffic congestions throughout the city and helps in avoiding gridlocks during peak hours.
2. Public Surveillance & Safety
Public surveillance is already in place. But data analytics can run through historical records on criminal activities, along with real-time data from local police stations, to figure out which geographical areas are witnessing high crime rates. It can then predict which areas are likely to witness more criminal activities and which ones are about to witness a drop.
Using such data feeds, data analytics algorithms can offer intelligent predictions based on which appropriate actions can be taken, such as increasing police patrolling in specific areas, installing more surveillance cameras, and creating special units to deal with increased crimes.
3. Energy & Water:
With deep-rooted integrations of sensors and beacons, the IoT technology can play a critical role in generating good and reliable data on consumption patterns across the city. Based on this data, smart grids can be implemented and controlled easily for ever-evolving power demand. Along with that, water usage patterns can also be monitored in detail to identify sources of leakage or wastage.
4. Web Infrastructure:
Being an essential aspect of a smart city, internet and broadband infrastructure can greatly benefit from predictive analytics. Providing quality internet is more than just about giving access to fast internet. It’s more about providing the right type of set up for each type of area so that the demand is adequately fulfilled.
The ability to shift or adjust bandwidth within a city is key here. City administrators need to prioritize commercial, financial, and high-tech areas for high-speed bandwidth. Also, they need to identify peak demand hours in residential and commercial areas, so that the bandwidth can be switched accordingly. Gaining usage data and consumption patterns across the city will allow for advanced data modeling, which will help in predicting the right bandwidth settings.
5. Future Planning:
Spending public money on building new projects is easy. But identifying the right opportunities and scope for investment takes immense data and planning. Data from public surveys, popular media, and historical results, can be factored in to analyze public sentiment and generate viable predictions. These predictions can tell what kind of projects will garner public interest and be economically feasible – a spanking new mall or a recreational park.
In Conclusion:
As we can see, if every aspect of a smart city is planned by keeping data analytics at the core, it automatically creates scalable infrastructure. Having holistic/unified data streams across the city makes it easier to produce insightful analytics and actionable predictions for city administrators.
Thus, it can be said that ‘good data’ is the essence of human development and progress. And when it comes to building smart cities, builders, innovators, and planners need to start thinking of solutions with data analytics at the core for an economically viable market.If you’re looking for an expert data analytics platform provider who can help in setting up your entire data framework, you need to connect with Suyati. Suyati is a pioneer in implementing cutting-edge ERP solutions, data analytics tools, and data storage solutions, offering both standalone and integrated packages.
For more insights on Thought Leadership, you can reach out to Tim Dubois on Linkedin.
