The geospatial industry does not lack data. In fact, new data is coming out all the time. With technology improvements in satellite, IFSAR and LiDAR sensors, image quality is constantly improving. Not to mention the fact that more geospatial data is becoming available publically. But data by itself does nothing for an agency or organization. It’s how you leverage the data that makes the difference. Here are some questions to ask to see if you are taking full advantage of your data.
Geospatial Data Needs: What are your current data needs? Do you have the spatial data to meet those needs? Do you know and understand your future data needs? Do you have a plan in place to meet those future needs? What other data do you need that can leverage your spatial data? Do you have that data, or can you get access to that data? How frequently do you use your data? Can it be used more frequently? Can the data be used in a better, more effective way? Are there other departments or agencies that can use your data? If so, why not reach out to them? Is there a synergy that can develop between your department or agencies and other departments or agencies that can provide a win-win situation? Can you use your data to communicate more effectively with your constituents?
Geospatial Software: How are you viewing and sharing the data? Is there a better way to view and share your data? Do you get input from others on how well the data is solving problems? If not, how can you receive input from them? Are there analytics that can be leveraged with your data to solve problems? If so, who in your agency or department have needs and how can you better meet those needs with your data?
The value of your data is not in the data itself but how well your data is helping you solve problems. Don’t be one and done with your data, learn to maximize your investment in data. To leverage your data, ask better questions – different questions than you have been asking in the past.
Add comments below on how you have used your data in a different way that has added value to the data. How have you used it to solve real-life problems?