Today we have to accept that the digital transformation of people’s lives has become an unstoppable force. Network connectivity has extended its geographic reach and multiplied as 5G, and the FTTP network is now available in many places. Today, devices’ computational ability is increasing, large-scale data is becoming commonplace, and cloud computing has become more effective, while IoT devices are becoming smaller in size.
The growing advancement in sensor technology is converting every object into the origin of data. The collaboration of the latest technologies has driven us to a new place. Now the network of physical objects like buildings, infrastructure, vehicles, and other equipment can collect and exchange large-scale data and work together. This allowed devices, sensors, and systems to pursue goals and meet the system operator’s objectives automatically.
Data generated in the IoT is always helpful for a specific type of audience. We can use the example of data collected from a smart chip attached to vehicles traveling on a highway that could be used to generate possible anonymized datasets like traffic congestion time or volume in the daytime, beginning/destination routes, etc.
These data can be used to answer the demands of a particular type of audience. However, the data can be made more valuable if combined with other essential datasets like weather updates or crowds present in retail centers. This would help in finding out the best solution for any particular issues. If a traveler is having a weather report of a particular place, he/she would either delay the plan or postpone it for a day or take the best measure while traveling on the same day. This is how real-time data helps in sorting out the present and future issues.
Converting Raw Large-Scale Data into Actionable Capacity
To know the exact location of something is critical situational information, which is essential for the successful functioning of the IoT. We can take the example of road safety systems installed in a car. When the vehicle senses the upcoming slipper way, its road traction systems respond in real-time to keep the car on the road. This entire process happens automatically without any human interference and without needing to know the location. Nevertheless, the information about the road’s slippery condition is precious to other road users if they are likely to use the same route. The use of advanced technology has contributed to minimizing the risks and hazards.
Using Location Data
Mainly location data are used for descriptive, predictive and prescriptive analytics.
For Descriptive analytics:
With descriptive analytics, data mining and analysis provide detailed information on the history. It tells us about ‘what happened?’ and transmits the location of the hazard so that the car can warn other vehicles traveling in the area of the risk.
For predictive analytics, modeling techniques are utilized to predict the future. It is possible by gathering past large-scale data from millions of vehicles over time and associating weather data by location; the techniques can forecast where and when a slippery condition can befall. This would be helpful for other vehicles as it would warn the cars before the risk actually happens.
Scenario modeling and assumption can be the best way to evaluate solutions’ impact when utilizing prescriptive analytics. By modeling the effect of different solutions on interest location, one can find the best possible solution based on success criteria to assure the least hazard chances.
Geo-analytics has empowered us to answer all the questions that had been a challenge in the past, either due to data unavailability or computational power.
Questions such as:
- What is the current situation of the area?
- What is happening in the area of interest?
- Where else the same problem is emerging?
- Where else have we seen this before?
- Where might we face the same problem in the coming years?
Traditional Geographic Information Systems use maps to display geographic information in a user-friendly manner so that humans can easily get it. Geographic information is fundamental to the IoT, but the map’s primary function is to help people visualize the large-scale data. Sophisticated spatial queries and Geo-processing algorithms built-in IoT platforms can connect data that was earlier unconnected. This supports the fact that geospatial information holds a vital place in the IoT data market.
To get real-time data feeds holding value, they must be built on a consistent geographic base synchronized with time to support predictive analytics tools.
The availability of information on a map allows people to use and identify visual patterns, which helps decision-makers increase sales or monitor cost-effectively. The skill to manage large-scale data sets in the cloud, display data geographically, and provide the tools for analysis is the best way to add value to raw data.