No matter where you are when it comes to analyzing your location data, we have steps to help you implement geospatial analytics. By adding location data to your business data, you can uncover all new insights and transform your business.
Any geospatial project should begin with a strategy. The first step of your strategy is to think about the business reasons for implementing geospatial analytics in the first place. What questions are you trying to answer? How does this help your business? What decisions will you make?
Then, determine the specific questions you want answered. Read more
What data is readily available by your organization? Take note of how many location points you have to understand and if it makes sense to plot them all at once or present in a different way. Then, make some determinations about how you will aggregate data and show addresses before standardizing the data.
There are tools used exclusively for geospatial analytics, ranging from a full-blown GIS, to products that enhance the capabilities of data visualization tools (like Analytics8’s tool, QlikMaps). Consider these questions if you need something more robust that your existing BI tool.
Check out our more exhaustive geospatial analytics worksheet to help with your tool selection.
This is the time to perform analysis and refine your solution. It’s important to let your business users test the tool to ensure it’s intuitive to use and providing insight.
Now is time to start taking action with your data. Seeing and analyzing your data in the context of its location will make you consider strategic questions about your business and help you gain a competitive edge.
To thrive with your data, your people, processes, and technology must all be data-focused. This may sound daunting, but we can help you get there. Sign up to meet with one of our analytics experts who will review your data struggles and help map out steps to achieve data-driven decision making.
In one hour, get practical advice that you can use to initiate or continue your move of data and analytics workloads to the cloud.
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