Under the ominous, overarching shadow of infections and our now changed social norms, data visualizations and mappings have never been more material to our decisions.
But thankfully, the backend mathematics(once hard to come by) has become more accessible. That said, you also don’t want to oversimplify to the point where you lose the flexibility to drill down into data.
So while geospatial analysis will also turn out to be more important than ever, knowing what representations or graphs to look at is also equally crucial.
As you read this, dashboards to track the pandemic’s effects on the government, data, economy, and the pandemic itself are used to track spread and impact to make data-driven decisions.
Where Tableau shines: Control and Context rolled into one
Right out the gate, you’ll notice, as with most mapped visualizations, that impartial hue pallets, and other granular controls that TB comes with, are important to make data feel more contextual to inform decisions.
Then, since most data out there isn’t even tied to locations, you’ll also need to move to more explicit use cases.
That includes covering different layerings, juxtapositions, combinations of data points for isochronic maps, all while adding context to nearby locations in geospatial areas.
And yes, this can get very compute-dense, very quickly, but it’s vital to get more rounded-out pictures of bottlenecks in your physical client interactions. More context is fundamental, and efforts to institute it should reasonably precede any reform to business strategy from this point on.
Sane data collection and inferences
Preparatory Responses with inventory stocks will count on these physical parameters and dependencies
For now, it looks like customers and retailers may need to pool data and coordinate with state-run APIs to auto authenticate street traffic volumes. It’s a good thing Tableau pairs really well with Google Maps APIs.
But that’s easier said than does and may spell sizable compromises on hard-won demographic data. Interestingly, there’s a speck of possibility it might also drive more anonymization.
Further down though, denoising demographic data needs a softer touch, besides checking reliability, especially for smaller data volumes. Your choice of data mapping and timelines can swing variations in the story your data tells vary widely.
What customer-facing businesses might look to
Visualizations for clearance sale campaign insights seem like a reasonable thing to want right about now. To get the ball rolling, one might suggest what other contextual data to append for repurposing old data against changed rubrics for campaign success.
Next, this should have buy-in from all departments and shared.
And overall, the inclination should be to model data-driven behaviors
Oftentimes, it’s easy to get carried away and forget entirely that economies are but chaotic, dynamical systems with interdependent moving parts. There wouldn’t be an industry around making sense of them if they weren’t so convoluted.
Our best bet at understanding effects is to watch over changes in purchasing patterns and how they affect each other. Visualizations for that sole intent, have and continue to influence decisions that define our lives for years to come. And they’ll be crucial to sustaining a reopening.
Key considerations for you to take from this:
- Devise categorizations for affected customers
- Make case appropriate response plans
- Leverage online forums, threads, communities to drive best practices and ensure the best coverage of grievances.
- Exchange and converge on best dashboard practices
- Ranked mediators can drive trust in responses
