You’ve seen statistical data before: bar charts, pie graphs, histograms, and the like. However, without a geographic component, it is difficult for most people to understand just how important non-spatial data can be for your business. A business analyst will use many different tools and methods to link data to business operations, helping you achieve goals, increase efficiency, and improve your business for the future. One of those tools can focus on geography.
Geographic data is vital to ensuring all this happens. Melding geographic data with data analytics provides you with geospatial data analytics, a niche field, but one that is very important to your bottom line.
Geospatial data analysts do not just make maps. Pretty pictures mean nothing out of context. A huge component of geospatial data analytics is spatial analysis, where data with geographic components are analyzed based on their assumed value to your business.
Here are five ways you can see the power of geospatial data analytics in your business.
1. Exploring data
You have questions and the data can provide answers. This is the basic sense behind data exploration. If your data is robust enough, you can derive answers from it, which will fuel your upcoming business decisions.
For example, if you want to advertise in neighborhoods with a higher than average population density, in order to maximize your customer base efficiently, you can easily use data exploration to isolate neighborhoods based on population density (population of an area, normalized by the size of the area). From there, you can view the data in standard deviations. Standard deviations will show you how data is distributed based on the average value of the data.
Higher than average clusters and lower than average clusters of data will become apparent. Color-coding this data, you can easily see which neighborhoods are best to advertise in, allowing you to ignore the rest.
2. Measuring data
Measuring things is a simple concept, but one that can really propel your business forward, especially when it comes to geospatial data analysis. The measurement part is easy. When you were in school, you probably had a slide rule or a 30cm ruler. You’d measure your desk, your pencil case, and maybe even 8.5×11 inch piece of paper to audit the paper companies’ claims. Measuring geographic data for business purposes is no different. The only thing that changes is the context. This is the more challenging part.
Measuring data can take many forms for you. You could measure the distance between your business and your competitions (either locally or throughout your city). You could measure the distance of your customers and compare these measurements to the shipping rates customers pay for product delivery. You could measure the distance it takes to deliver products and services to customers based on data like route length, mode of transportation, etc.
3. Transforming existing data
When you transform data, you are simply turning one set of data into another, or manipulating data in some way. You can add data, subtract data, or fundamentally change the appearance of data. This may seem like a misleading thing to do, but it’s not. There is a difference between transforming data and lying.
While changing the look of data (visualizing your data with different data classification schemes, for example) can potentially provide different results, this is exactly why good business analysts provide several maps, based on the data being observed. The business analyst that only shows one map is analyst that you should avoid.
Mapping your competition’s locations throughout the city will provide different results based on data classification, for example (to use my previous example). However, by viewing the data with different classification schemes, like natural breaks, equal intervals, quantiles, etc., you will start to see similarities between the mapped data, as well as differences.
By honing in on these similarities and differences, you will have the mapped data provided to understand your business’s location within the wider context of your competition.
4. Optimizing data
Optimizing data simply means you are being more selective about chosen data. You are essentially narrowing your focus, and viewing data with more stringent requirements. Optimizing data is great if your initial forays into spatial analysis provide too much data to handle. Then, you can re-evaluate your data requirements, and modify them to result in a smaller, more manageable dataset.
If you are a food cart operator, for example, and you want to find the most-dense areas of Toronto with regards to green spaces (because who doesn’t like eating burritos in the park?), you can optimize your data by only considering “large” parks for potential locations to sell food at. Toronto’s a big place. Makes sense, right?
5. Describing data
Alluding to the first paragraph in this article, describing data is quite difficult to describe, especially without a geographic component. Geographic information systems are just another tool that we can use to show how data affects us and our businesses, as well as inform better business decisions. Once you have performed your spatial analysis, describing data and how it affects your business becomes easier, even if you doubted the process at first. In the words of every child who meets Santa at the mall, “seeing is believing.”
You can describe data with maps, with reports, with specific statistical outcomes like stating the standard deviation, variation, mean, median, mode, and more.
In future articles, we will delve into specific business analyses that business owners require.