Today, data has become everything. It is the boon because it offers unimaginably valuable insights about everything under the sun and it is a bane because it is hard to store something as Big GIS data, secure and process. Also, if you do not know how to use data, there is no fun in having it.
Many would think that GIS and related data analytics is all about creating maps to help you navigate across streets and places. While it is one of the applications of it, there are more important, relevant and major applications of the same. It becomes an important factor in creating such powerful insights as in climate change, population dynamics and natural disasters along with many others.
As such, data science and data analytics have become a great industry in itself, churning out training programs, workshops and technology solutions to effectively capture, store, process, generate insights from and manage the data. This is where data analytics becomes highly important in GIS as well.
Before we delve into the ideas of GIS and how it supports us, let us take a look the three major aspects of analytics;
Descriptive Data Analytics: Where data explosion is tamed
This is the first and foremost step in the entire process of data analytics. This is where businesses try to collect data from different sources and also the foundation upon which businesses can drive useful details and insights. Without this particular level, no business can get the data they need and structure it for the further requirements the business has in store for the data.
Predictive Analytics: The next level of descriptive analytics
This is based on descriptive data analytics and it seeks to learn from it. This helps the business find the best possible solutions to effectively tend to the risks it faces and understand about the kind of action they need to take in order to sail further. Making use of various risk-based models of machine-learning algorithms to effectively address the most pressing problem, the businesses generate and prioritize the insights arrived at.
Prescriptive Analytics: Drive actionable and relevant insights from the descriptive data
This is the step where the business strives to understand, learn and transform the descriptive data into relevant, actionable and business-significant pieces of information and details. These insights can offer the business great understanding of a range of aspects about the clients, their buying patterns, preferences and even the competition. Based on the information, the management can create viable and relevant business strategies to course the business towards better service offerings and propositions.
There is no doubt that even with a deluge of sources to gather highly sensitive data from, sorting it for effective and useful prescriptive, predictive and descriptive analytics is the most important job. Businesses wanting to make use of data must begin to rely on technology-driven solutions for must.
At the same time, is also important to see how advanced analytics factors into the bigger picture of Geographic Information Systems. There is no doubt, that millions of sets of data are being constantly powered by GIS, and in multiple ways, for a range of purposes such as showing the users a store location, letting the users know about the shortest and least-traffic roads to office, to predict elements and degree or climate change, how fast a cyclone is moving or even letting the authorities know about the crime patterns in a particular geographical area.
In every stage of GIS, all the above steps take place in collecting, storing, analyzing and mining data constantly to arrive at comprehensible, intelligent and valuable sights for users.
If you will, GIS or Geographic Information Systems are a range of collection of analytical tools connected to different data sources from diverse parts of the world with the intention of correlating sets of data to each other in order to arrive at humanly understandable insights. One of the most important aspects of geographical data, however, is that they do not make sense unless they are studied with respect to each other and compared with similar data available from different parts of the world or even from space.
In a nutshell, GIS can be condensed down to four simple steps;
- Creation of geographic data sets
- Managing the collected data sets
- Analyses of the collected data sets
- Presenting the same as comprehensible to the people
In doing so, advanced analytics play a crucial role to ensure that the data collected is studied effectively and efficiently without creating any room for errors. As Geographical data can be very much sensitive and used for as reliable source for a range of studies at various governmental and private studies, the importance of the same is really understandable.
As you have already seen, GIS and related data analytics are here to stay. As powerful and robust technologies come, the processes of collection, storage and analysis become easier and simpler. It will definitely make people get more effective, comprehensive and simpler answers.
While it will continue to make effective navigations systems impacting even your daily life, it will also bring more radical changes to understand and address issues we have touched upon earlier such as climate change, weather forecast and crop-planning along with a deluge of others.