Our last topic in this guide will deal with the burning question that most readers likely have on their minds: “How do I make geospatial data work for my organization?” Well, as you might expect, there isn’t a one-size-fits-all answer. Every company is different in its needs, processes, and goals.
With that said, leading geospatial data firms such as SafeGraph and Esri have suggested some geospatial data management best practices that your organization can use as a framework. We’ve sorted them based on a five-step process developed by Esri to build a winning geospatial data strategy from the ground up:
As we go through each category, feel free to choose and tweak the practices you want to use in order to create a geospatial data strategy that’s right for your organization.
In some ways, the principles of geospatial data management aren’t all that different from those of managing other types of data. For example, you probably won’t need much hardware and software other than the industry standards for data science.
However, as we talked about with the challenges of using geospatial data, it has its own quirks in that it’s inherently related to physical locations (and occasionally times, as well). So there are some extra things it can tell you, but getting the most out of it may take some more specialized knowledge and management practices. Here are 15 recommendations to get you started.
Geospatial data is about more than just static locations. It also offers insights into relationships between points of interest, products, brands, and people.
With this information, you can ask questions such as: how likely are people from a certain census block group to visit particular businesses? Based on that likelihood, how likely are they to shop for products of one brand over another? Which businesses in an area are likely competing with each other? Which businesses may be complementing each other by allowing people to quickly and easily complete a series of tasks in sequence? These are questions that geospatial data can answer, whereas other types of data may not be able.
Although geospatial data can do a lot of things, your time will be better spent if you know specifically what your stakeholders want. Sit down with them and have a non-technical conversation about the types of insights they’re looking for. This will inform the types of geospatial data you will need to collect and work with.
Geospatial data is a powerful tool, so don’t let it go to waste on side projects. Map out the most pressing issues and challenges your organization is facing, and then think about how you could use geospatial data to solve them. This will help to get higher-ups on board, which will make things easier for your best practices later.
Take the things stakeholders say they want and map them to common use cases for geospatial data. Are you trying to visualize or map something? Monitor and analyze activity at a particular location? Plan or design buildings or other infrastructure? Support investment decisions? Better engage your customers or constituents? Once you translate your organization’s needs into geospatial data use trends, it becomes clearer how to apply geospatial data to meeting them.
Once you’ve matched stakeholders' needs to geospatial data use cases, go a step further. Based on the patterns you identify, go back to stakeholders and discuss with them how the concept of geospatial data management fits into the organization’s overall philosophy.
What does your organization ultimately hope to achieve? What beliefs and behaviors do you adhere to in pursuit of that goal? And what milestones will serve to mark progress towards the greater objective? Using your likely primary uses of geospatial data as bases, develop a broader vision of how geospatial data will work in service of your organization’s mission, principles, and desired accomplishments.
You can actually use standard data analysis tools to work with geospatial data. However, you’ll likely only be able to accommodate small-scale data production that will be difficult to expand as your organization’s operations grow. Instead, a cloud-based data platform offers a much more speedy, reliable, convenient, and scalable alternative. Beyond that, you should also have a data lake, a data storage solution, a processing compute platform, a task scheduler, and a tool that simplifies writing data processing pipelines.
A geospatial data analysis IT infrastructure can be rather resource-intensive. That’s why it’s a good idea to devote a separate IT department to maintaining it. This helps to avoid overtaxing your main IT department, which may leave your other departments fighting over limited IT resources.
You’ll also need to develop geospatial data standards, guidelines, and policies. Use the vision for geospatial data’s place in your organization as a guide. From there, build rulesets that can be backed up by industry-standard procedures, baselines for minimum compliance, and proven methods and practices.
Ideally, your rules should cover the following:
For a smaller organization, you may only want to focus on problem areas where having documented rules will improve productivity. Larger organizations may need more comprehensive documentation to ensure the compliance of all stakeholders.
One issue you may encounter with regards to your geospatial data infrastructure is how to balance its cost efficiency with its flexibility. Preprocessing data increases your system’s cost efficiency, but reduces its ability to help answer unique queries on demand. You may want to split your infrastructure and have each part focus on one goal or the other.
It’s recommended that you set up a committee to build and manage a complete catalogue of all geospatial data your organization has on file. This makes it easier to communicate to stakeholders and clients what you have (or don’t). It’s also important to have so that employees in different departments can ask about the availability of data outside the kinds they normally use. This simplifies things by allowing teams to internally connect their datasets instead of needing to search for the data themselves somewhere else.
You should also assign one or two employees to act as managers for each individual dataset you have. Having experts on specific datasets will, again, make communicating their capabilities to stakeholders and others within the organization easier.
It’s ideal to have your geospatial dataset managers in close contact with your corporate analysts, and playing under the same (or similar) rules. Doing so will help you acquire and manage the assets, equipment, and insights your teams need more quickly and efficiently. This also helps to improve your organization’s data quality – leading to better analyses and decisions – by having extra people reporting on it to catch any errors.
Once you have an idea of the most common ways geospatial data is used within your organization, you can start adding the appropriate metadata to your datasets. Classify them based on traits such as which departments typically use them, which format(s) their data is in, how often they’re updated, when they were last updated, and which geographical areas they cover. This will help to streamline management of your datasets by allowing you to organize and sort them based on the most often-used and/or up-to-date data.
More often than not, geospatial data will have some use in more than just a select few of your organization’s operations. That’s why it’s smart to form a geospatial data technical guidance committee, made up of employees from all of your different departments. This ensures that a few people in every department know how to analyze and interpret geospatial data, and discuss its use with members of other departments.
This makes sure your departments are all on the same page with regards to how each of them is using geospatial data. Employees will also know exactly who in their department to go to if they need help with a geospatial data problem. This helps to cut down on delays from one department needing to ask another for help. This also avoids multiple people in the same department needing to ask for assistance with the same problem from other departments, which can result in duplicated work.
Develop a set of performance indicators based on how your organization’s geospatial data management plan relates to its short-term and long-term goals. Be sure to review them at least once or twice per year to make sure everyone is sticking to your ground rules, and that your organization's use of geospatial data is driving you towards where you want to go. Look not only at how much progress has been made, but also at whether that progress has been in the areas most important to you.
Of course, an organization’s values and priorities can change over time. So don’t be afraid to adjust your geospatial data strategy’s success metrics if you conclude that it’s warranted.