Location information about the users is found everywhere starting from the device sensors to the location data of social media posts, financial transactions, etc. Processing this multifaceted and diversified data may be too much time consuming and involving with the kind of challenges involved. Tracking billions of sensor data points requires robust real time computing performance and that makes the real challenge.
Apart from the three “Vs” of big data, respectively volume, variety and velocity there is also another crucial determinant called time. Let’s see the problem from a scientific point of view. A satellite takes the image of the Earth every day at a given point of time. Now, what would be the change from image to the other. Well, there can be multiple changes like temperature, sunlight, humidity and so on. But there is little measure to ensure accuracy of data and most big data solutions providers are clueless about it. This is why we often come across false information in database.
So, the big data journey is also about providing time honoured accuracy of data and location information will play a key role in making data richer. Location based data processing is already in place. Accomplishing fast paced geoprocessed data has become possible thanks to state of the art Big Data framework such as Hadoop. Many cloud computing solutions are now trying to gain edge with this geolocation accuracy of data.
The second big challenge is concerning organising data to deal with huge volume of Big Data. Without enhanced organising capacity to deal with exponential volume of data undertaking analytics will prove to be a big stumble block. Spatially-enabled data lakes have made it possible to create different data-centric workflows for various processes. This helps ease of access for workflows and adds pace to the data centric processes.
It is also about delivering location analytics. Geocoding the fast paced and well organized data will help better location analysis of data. Adding data sets to diverse groups of geo location specific category and resolution will help garnering more relevant results for different workflows and business processes. For example, unique ID attributed to movable properties can deliver more insights from their movements concerning ownership or purpose of use.
Finally, location analytics will not have its merits if they cannot be visualised with the outcomes. Big data visualisation is now a precondition for transparent rendition of the data centric technology across environments. For instance, a mobile telecom service provider can make use of a data maps to showcase the network performance across various locations. This will give make location specific insights for different workflows clearly visible and easily accessible.
To conclude, the location analysis pertaining to big data always comes with big challenges. Companies need to consider their business needs first before considering to leverage any big data frameworks. They also need to learn from the use cases that can deliver them the best outcome. While challenges are galore, when they can grab proper solution the same challenges can deliver them great results.