Hey George, the ocean called and they're running out of shrimp! 🦐

George takes a zinger in a meeting, but he comes up with the perfect comeback. The only problem is....he comes up with it hours after the initial zinger, when it is useless.

To be effective, George needed to deliver the comeback in near real-time, with precious little delay after the zinger.

This is often the case in data analytics! We need the insights delivered as soon as the data is collected or else they aren't effective. Consider these scenarios:

🏪 A retailer that provides coupons or "suggested items" based on your current shopping cart

💹 A trader that uses real time prices to make buy/sell decisions

🚛 Logistics teams that route vehicles and need to be nimble for any traffic, order changes, etc.

🔐 Network security that needs to identify and counteract suspicious behaviour

None of those would benefit from responding several days later. They need to make decisions in real time.

That creates many challenges - from the way data is ingested and combined, to the complexity of analytic models, to the way the results are served.

Real-time analytics can be a huge challenge, and can be resource intensive to ensure timeliness and reliability. But in some applications it's absolutely necessary. Or else you end up delivering the perfect analysis several days after it would've been useful.

And if you deliver late...you might be the jerk store's all time best seller.

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