Location data has an invariable tendency to stay opposed to integration in the scheme of things concerning unspecified data. Though data with location information adds more descriptive attributes like demographic characteristics, this variable data often proves deterrent to analytics.
When it is about reaching the best solution through analytics location data as an attribute often plays an ambiguous role without proper role specified. But whether in IOT development or integration of multiple device data to boost the effectiveness of a connected reality, location data always proves challenging.
Location data always helps fulfilling company’s real goals by encompassing several attributes together along with the location information. But to utilise multiple layers of data including location information the company needs to take help from different device sensors and types of data including the data generated through moving devices. Product engineering solutions also need to take location and various types of sensor data into consideration to allow data centric decisions.
Quicker processing of location information is crucial to address the real time changes of location. This is easily accomplished with the help of a data flow accelerator. Thanks to this geoprocessing the data quality is improved and the big data framework like Hadoop can offer an edge with geocoding and geospatial calculations integrated.
Organising Data For Objective Use
For organising data the overwhelming volume remains always a challenge. The challenge seems to come in respect of both data storage and organization. While the lack of organization with digital data contributes to the deterioration of the analytics taking data geospatially is important. Such spatial organisation of data allows accessing and processing data for different purposes. For example, in an insurance company the so called underwriting process requires undergoing several steps and processes. Spatially organised data lakes can offer comprehensive utilisation of data by these processes.
Visualizing Big Data
Finally, data has been subjected to widespread visualisation allowing easier and quicker access to relevant data. From mapping solutions to presenting complex set of data with infographic, visualisation of data continued to play great role in boosting access to the big data. Among other things, data visualisation can easily facilitate easy operational workflows.
Finally integrating location analyses to big data is one of the key challenges that every business is concerned about. With the company groping for operational ease with a low budget, yielding best business results through big data integration became tougher than ever before.
From the huge growth of IOT to the multifaceted data generation across industries to the big data lakes to serve diverse business processes, location information continued to add value to every data centric endeavours. Today without location intelligence playing a role, we cannot think of delivering data driven insights for real time business usage.
Whether it is retail or financial industry or hospitality or healthcare or any other enterprise niche, real time location data along with the user activity data based on feedback from multiple device sensors will continue to have more importance than ever before. But, integration of location intelligence across different facets of data pools remains still a challenge and will be overcome in the time to come.