When 'fast enough' beats real-time: A business value perspective
MAR. 27, 2025
2 Min Read
Real-time isn't always right: When ‘fast enough’ data delivers more value than lightning-quick insights.
There’s a prevalent misconception that real-time data processing is always the best approach. Modern data platforms are constantly delivering increasingly sophisticated real-time capabilities, and that shift is leading organizations to prioritize immediate data access as a business function.
Here’s a different, potentially controversial, take. Real-time processing isn’t always the most efficient or cost-effective solution to a problem. Instead of defaulting to speed over everything else, businesses need to evaluate whether real-time processing translates into measurable value. In many cases, they’ll find that “fast enough”delivers greater returns—especially when they measure it against the exponential costs of real-time architecture.
Real-time data processing often solves the wrong problem
Real-time data processing has its place—the success and growth of powerful platforms like Databricks exemplify this trend. However, many teams default to complex real-time architectures without considering whether they truly need them.
You’ve heard the saying, “When all you’ve got is a hammer, every problem looks like a nail,” right? The same applies here. If you invest in real-time capabilities, you can easily be tricked into thinking that every problem needs real-time data.
The thing is, batch processing can often provide the right speed for business-critical decision-making at a fraction of the cost. Plus, real-time capabilities can’t fix underlying problems such as poor data quality, clunky legacy systems, or misaligned business processes—all of which can negatively impact the efficacy of real-time data. Despite what they’d like to think, most organizations operate on decision-making timescales that don’t actually require real-time updates. And that makes the investment unnecessary in a lot of cases.
The law of diminishing returns rules data processing
It’s easy to believe that reducing data processing latency will always deliver incremental business value. However, that’s only somewhat true. While moving from daily to hourly data refresh cycles often significantly improves decision-making speed, pushing beyond that threshold to real-time processing can result in minimal gains that aren’t worth the investment.
The other thing to keep in mind is that infrastructure costs rise non-linearly with reduced latency requirements. In other words, the cost of reducing latency from minutes to milliseconds rises exponentially, leading to higher infrastructure spend, operational complexity, and technical debt.
Smart timing choices start with honest assessment
Rather than optimizing for speed at all costs, organizations should assess their true business needs to get the data they need at the time they need it (which won’t always be in real time).
Specifically, effective data timing strategies should account for:
- Business outcomes: Timing decisions should align with your business goals around revenue impact, operational efficiency, and customer experience.
- Data type and use case: Not all data requires the same refresh rate. Even within the same system, transactional data may need real-time updates, while analytics and reporting data can operate on batch cycles.
- Value-based categorization: Prioritizing “right-time” data delivery ensures that critical information is updated as needed without unnecessary infrastructure overhead.
Optimizing for business value over speed
Some critical use cases require real-time data to be effective. Fraud detection and algorithmic trading in the finance sector, real-time personalization in e-commerce, and dynamic pricing in travel and hospitality are all examples where real-time processing is worth the investment. That said, most businesses will benefit from a hybrid approach that balances speed with sustainable business practices.
A strategic data processing framework should:
- Identify critical real-time use cases and limit real-time investments to where they deliver a competitive advantage.
- Implement hybrid models that blend batch and real-time processing to optimize cost and performance.
- Preserve future flexibility by designing architectures that evolve with changing business needs rather than locking into rigid real-time structures.
Success will come from matching speed requirements to the actual need.
Real time isn’t always the right time
In the race for data speed, organizations must prioritize business value over technical ambition. By assessing true data timing needs and balancing real-time and batch processing strategically, businesses can optimize costs, reduce operational complexity, and maximize long-term agility. The goal is not real time for its own sake, but “right-time” data that aligns with business impact and helps your business achieve its next milestones.
