Risk assessment and mitigation with location analytics
Data analytics has widespread applications in all fields ranging from the solving crimes to providing customised advertisements to the users. The field of location analytics which mainly deals with the processing of and gaining insights from location data which has mainly to do with the geographical components of data is gaining popularity and is being made to use in risk assessment and mitigation.
Location analytics can be defined in simple words as the process of gaining insights from the location component of data. Data which is generated by the different sources contain a location component which is harnessed using the proper mechanisms brings out new dimensions of the data which were earlier not visible. This can have several applications in the real world, ranging from helping in the delivery of items to helping in disaster management and planning. A very significant use of this could be in the military where the data of the locations of the troops and that of the enemy movements can lead to better decisions and offers added advantages. The data used for location analytics could be the real time location data or it could be historical location data.
For Risk assessment and Mitigation
The unpredictable events which could be natural or due to human errors have been causes of widespread damage to humans and have affected the economy as a whole. Even though controlling the behaviour of mother nature is not in our hands, but the proper use of location analytics opens up a plethora of opportunities for reducing, to a large extent the negative impacts of the events that occur.
The weather example
Consider the example of unpredictable weather, proper use of historical data of the weather patterns can help in reducing to an extent the damage that would be done in the future by predicting the vulnerable locations for similar occurrences in the future. Consider that a disaster has occurred. When there is exact data on the location of the occurrence of the incident, it can be used to help the insurance companies in providing the benefits to the genuine cases who were effectd in the incident and thus reduce the amount of fake claims.
Improvement in data quality
Even though there have been several advancements in the data analytics fields there still is room for improvements when it comes to effective data governance. The use of location intelligence can help in improving the quality of the existing data. When the data is used along with the location component, several insights become visible which help in much better processing of that data. The location data can be made efficient by making use of Geocoding.
Traditionally the locations have been specified by using addresses. In geocoding, addresses are converted into spatial data where the exact geographical coordinates are associated with the address. This method is useful in pinpointing the precise location and thus allows for providing much better analytics. This solves several doubts about the location components of systems. The confusions between similar or same addresses, two people having similar names and so on are solved by using geocoding. Geocoding thus improves the reliability factor. This technology also allows the institutions to give a clear picture of its working to the stakeholders by using better location display. Geocoding enables the institutions to better understand the geographical location of the customers on the map and also better analyse customers based on their geographical profiles.
Location analytics is and will be in future a major way for risk assessment and mitigation for the institutions to know their customers as well as for protection against possible risks which can include weather events, crime and so on.
About the Author
DataFactZ is a professional services company that provides consulting and implementation expertise to solve the complex data issues facing many organizations in the modern business environment. As a highly specialized system and data integration company, we are uniquely focused on solving complex data issues in the data warehousing and business intelligence markets.