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Tableau Power BI and other business intelligence tools have revolutionized and helped many companies and organizations become more data-centric and discover clear business insights within the data they often already have at their disposal and they do an amazing job of visualizing that data.
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And often this data may contain a location component such as a state county address or other geographic feature – or more importantly something that can be displayed on a map. In many cases if you create a dashboard with a map, you can display something like this:
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These BI tools are perfectly suited for something like this so if this is the scope of your analysis you can probably stop reading now.
…You probably know how much it actually takes to create something like this using a BI tool. Tons of data preparation balancing data size and load time linking spatial to non-spatial data thinking about some of the geospatial features in Tableau.
If this sounds familiar, then you’re in the right place. So what are the key challenges of working with geospatial data in a generic BI tool and how can you overcome them?
To understand why BI and maps are not the best match we need to understand a bit about where maps or geospatial data and tools like Tableau or Power BI intersect. Below is an example from Tableau from 2005.
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Maps have always been a part of these platforms in one way or another because inevitably most data has some location attribute. Think of a data set or spreadsheet you’ve been working with recently. Did it have any of the following?
So much data contains a location element of some form so alongside your bar charts and other graphs you might want to see that location data on a simple map. For example, if you have a customer information table with a country name you can create a map visualization that looks something like this.
For simple data visualizations like this, BI tools provide a great interface. You can quickly see your data in the context of a location and learn about the spatial arrangement of your data along with other data. However…
If you want to go beyond the state or province map, you certainly can, but you’re likely to run into roadblocks if:
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In short, geospatial data has additional complexity compared to non-geospatial data and tools like Tableau and Power BI are built primarily for non-geospatial tabular data. This presents a number of problems when trying to move geospatial data into Tableau. You can see the problems illustrated in videos like this one and this one. Have you ever had to deal with EPSG data projection before? Welcome to geospatial analytics.
This makes it really difficult to explain to stakeholders why their data cannot be mapped in Tableau or Power BI even though they have already seen the maps in them. Once you see a map many times you assume that all maps can be delivered using these BI tools.
Of course, many organizations often equate Tableau or Power BI dashboards with data, or this is the only tool they have to present data. This quote from a post titled “The Tableau Era Is Over” by Taylor Brownlow makes this clear:
For many business users, data is now synonymous with dashboards. Although a seemingly benign misunderstanding, this actually causes a whole host of downstream effects…With Tableau being the only tool many teams have for presenting data, they are forced to turn everything into a dashboard, which significantly reduces the impact of more nuanced, thoughtful analysis.
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There is one major limitation that actually makes spatial analysis difficult and unsuitable for universal business intelligence tools.
All of these tools can quickly visualize data because they store visualized data and analyze spatial data in memory. Those spatial functions you use that operate on that data also run on the data in memory.
If you are not familiar with the concept of in-memory data it basically means data that is stored or displayed in an application in this case a BI tool that lives on your computer or in the cloud. Traditionally these tools don’t actually store any data but pull it from another source anything from a spreadsheet to a big data system or data warehouse.
The fact is that there are natural limits to the amount of geospatial data you can display in any application. For example on our Maps API it renders data up to a certain point (30MB for ~200,000 features) directly via the data source beyond that we rely on map tiling to take care of the rest.
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And that sums up the number one reason not to use a business intelligence tool for mapping. At some point performance will always degrade because they only have access to the computing resources or CPUs where they are installed (either on your computer or in the cloud – see the last section for why this is important).
There are many examples of this happening. This Tableau user asked how to display millions of points.
Tableau recommends data aggregation as a method to combat this approach which we also use in our tile systems that are integrated with all major cloud data stores. Even then, there are still limits to how many data buckets and how granular you can display the data.
While there are many great reasons to use business intelligence tools, I think it comes down to the fact that it’s hard to explain why BI tools just can’t handle geospatial data. Yes, many times these tools are what’s available widely used and understood and that’s also a big part of the decision.
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But if your data is complex or large enough you will inevitably reach limitations in data visualization and processing. If you need to cross these thresholds listed in this post you will need a better toolset a modern data stack to analyze geospatial data.
Geospatial data is growing and that won’t change anytime soon. Cloud data warehouses are at the heart of solving this problem for big geospatial data. And yes, you can still connect and query data from a data warehouse from a BI tool but even when you bring that data into Tableau or Power BI you will hit the limits of the visualization. You simply cannot display that much data.
There are no hard and fast limits on this as the limits can vary depending on factors such as whether you have point or polygon data and the complexity and size of that data. But as you’ve seen, point rendering in these tools tends to stop working once you get past the high 6 digit points and polygons will start to stop working between 35 and 70k features.
At the end of the day when you hit geospatial limitations in a BI tool you really need to use a system that leverages the power of the database or data warehouse that houses your data.
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This requires two systems the first is the data store and the second is the application layer to do this. There are many options for this. You can use a traditional database system which has many advantages or take it a step further with a GPU accelerated database which is fast but has many cost implications.
Addressing these limitations is what we’ve focused on over the past few years to become the premier Location Intelligence platform for the modern geospatial data stack. We do this by using the built-in processing power of the data warehouse to process and query the data and then enhance our platform to manage and handle the visualization and analysis of so much data.
Let’s focus on visualization. If you have a large number of features to display on the map, you will need to use map tiles. Have you ever wondered how Google can display so much data on its maps so quickly? Map tiles are the answer. Now Google has billions of users and the data is pretty consistent.
What we’ve done is take the same technology and apply it to your data but also allow you to dynamically change your query and still gain the resulting performance benefits. And the queries you run are processed directly in the data warehouse which is extremely efficient for these operations due to the fact that they scale in parallel to perform very large queries extremely fast.
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Our next big step in this direction is called Workflows, which brings the power of spatial analytics to a familiar modeling or data preparation interface, all focused on geospatial analytics, but working directly in the data warehouse to enable spatial analytics without writing a single line of SQL. We also build data warehouse tools such as
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