Valeria LedezmaIn today’s data-driven world, collecting and storing massive amounts of data is no longer the bottleneck it once was. What truly separates high-performing organizations from the rest is their ability to act on that data in real time. In this podcast, Mickey Alon and Local Data Exchange dive deep into the growing demand for real-time data solutions and the engineering discipline it takes to build systems that deliver meaningful insights in milliseconds.
The Evolution from Big Data to Real-Time Decisioning
Not long ago, big data was the ultimate goal. Businesses strived to accumulate large volumes of information and generate insights over time—be it minutes, hours, or even days. While that model still works for long-term analysis and reporting, today’s competitive edge lies in real-time responsiveness.
Alon highlights a critical shift: storing data is easy; acting on it in milliseconds is not. Whether it’s sending an ad bid, recommending a product, or responding to a user’s in-app behavior, organizations now need to operate within strict timeframes measured in milliseconds, not seconds.
This transition demands more than faster infrastructure—it requires a change in how products, systems, and even engineers themselves are designed and trained.
Real-Time Use Cases Across Industries
Alon brings real-world experience to the table, citing examples from both financial markets and digital advertising. In finance, a delay of even a second can mean a missed opportunity or a financial loss. In marketing and product-led growth, the ability to personalize content and offers in real time can dramatically boost engagement and conversion rates.
In these scenarios, real-time action isn’t just about fast response—it’s about delivering the right response at the right time, reliably. If your system breaches the milliseconds threshold and drifts into seconds, the business value can be lost, even if technically everything is “still working.”
What Makes Real-Time So Challenging?
Real-time systems operate under a completely different set of rules. Traditional data pipelines and analytics models are built for scale, not speed. With real-time data, there are multiple constraints to consider simultaneously:
- Speed of Analysis: You can’t afford to scan through terabytes of data. You need a condensed, optimized model that can return results instantly.
- Predictability and SLAs: Every action needs to happen within a guaranteed time window—typically under 500 milliseconds.
- Decision Accuracy: Despite limited processing time, decisions must be smart and personalized. That’s a tall order when dealing with thousands of users simultaneously.
As Alon explains, the key is not just faster hardware or better memory management, though those help. It’s about deeply rethinking how data models are built, how execution rules are structured, and how engineers write code for concurrency, memory efficiency, and smart load balancing.
Building Systems for Real-Time Execution
At Gainsight PX, Alon and his team run tens of thousands of execution rules per second. Each rule needs to decide what message to show, when to show it, and to whom—all within milliseconds. The user is already on the website or app, and any delay means the opportunity is lost.
To meet this challenge, they optimize at multiple levels:
- Data Modeling: Smaller, leaner models tailored for speed.
- Execution Rules: Efficient logic that can evaluate user context and behavior at lightning speed.
- Smart Threading: Engineers must embrace multithreading and parallel execution—a step beyond typical CRUD application development.
This isn’t just about shaving off a few milliseconds for fun. The SLA (Service Level Agreement) is a contract: if a system promises 500ms and delivers in 1 second, you’ve failed—no matter how great the insight is.
Why Engineering Mindset Matters
One of the most overlooked aspects of building real-time systems is the human factor: how engineers think. Most software engineers are trained in environments where response times of 2–3 seconds are acceptable. The reality of real-time development, however, demands a mindset shift.
“You need to think differently about everything you do,” Alon emphasizes. This includes understanding memory allocation, object size, and writing clean, performant code from the ground up. Poorly optimized code that might be fine for batch processing or traditional web apps simply won’t scale under real-time conditions.
Engineers working on real-time platforms need to revisit principles they may have only lightly touched in university—threading, data structures, system architecture—and apply them rigorously.
Memory and Infrastructure: Not the Whole Story
It’s easy to assume that speed problems can be solved by throwing more memory or CPU at the issue. While in-memory processing is indeed part of the solution, it’s not a silver bullet.
In fact, Alon notes that the most significant gains come from architectural decisions and strategic simplifications in how the data and application logic are designed. It’s not about how much you process, but how quickly you can process just enough to make a smart decision.
Every optimization—from data structure size to rule complexity—feeds into the goal of making actionable decisions within a 500ms window. And as your system scales, the challenge only increases.
The Competitive Advantage of Real-Time Intelligence
So why go to all this trouble?
Because real-time data systems drive engagement, conversions, and loyalty. When users get relevant messages, recommendations, or interventions exactly when they need them, they’re far more likely to stay engaged. Whether you’re building a B2B SaaS product, a mobile app, or an e-commerce platform, real-time intelligence is now table stakes.
Moreover, companies that master this will create differentiated experiences that are nearly impossible to replicate by competitors still stuck in batch-processing mindsets.
Final Thoughts: Milliseconds Are the New Seconds
Real-time data processing is no longer optional—it’s a strategic imperative for businesses that want to lead in customer experience and operational efficiency. Mickey Alon’s insights show that the future lies not in simply collecting more data, but in turning that data into action—immediately.
For product teams, this means investing in architecture, tooling, and engineering skills that support real-time responsiveness. For engineers, it means rethinking the very foundation of how applications are built.
And for businesses? It means getting serious about making milliseconds matter and unlocking the power of data.
Real-Time Data Solutions with Gainsight PX’s Mickey Alon
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Podcast Transcription:
Transcript:
On the side of scale, it’s really interesting because we talk to a lot of companies, and, you know, a lot of companies struggle with the scale and the reality of the world today where there’s much more data than there was in the past.
But the next layer that we’re starting to hear a lot more about is the real-time aspect.
So, it’s one thing to be able to store a lot of data and put it in a data warehouse somewhere; it’s a whole other thing to be able to make decisions based on data that’s streaming or in a live environment.
I’d love to hear more about how you’ve dealt with the streaming or live data challenges.
Yeah, absolutely. I think when you go into this, there’s two paradigms: one is the big data analytics where customers expect to see data in seconds, minutes, sometimes hours and the aspiration is to be in the seconds space I would say, but on the real-time side, because as a solution sometimes you want to trigger real-time action, it might be just if you’re optimizing your ads. For example, it might be that you need to send a bid in real-time.
Back then when I dealt with financial markets, it was the real-time decision of “buy” and “sell.” And
today the real-time might be just the right campaign you want to serve for the users. And real-time is in the space, in the range of the millisecond, real-time.
What you have to happen with real-time is predictability. Because you have just a minimal window to take action,
and it has to be sustainable. If suddenly one mistake there or one logical error, suddenly the entire real-time just becomes seconds, so from a monetary perspective everything is green, nothing actually is not functioning, but from the business perspective you’re not delivering the value.
So as soon as you breach that milliseconds range into seconds, that is a breach in your SLA, so there’s many things to think about when you think about real-time action.
How much data can you analyze in real-time? In theory, for example, if we have unlimited time we can make great decisions, we can skim a ton of data. In real-time you can just not afford.
You have to optimize for the best decision within the time so you can close the loop and do something about that versus spending a lot of time, but the customer’s already gone so your answer’s great but there’s not anyone to listen.
So, real-time has a lot of challenges and it starts from how you model the data, how you process that, how you guarantee that response time, SLA, for example, the predictability, and how can you increase all the time, the amount of data you can trigger, you can actually analyze before you come back.
Technology’s always evolving so obviously using e-memory is giving us the fastest speed to analyze, but there’s a lot to do with the way you model and architect your solution to allow for the best decision to be made within a very very short time and their very strict SLA.
So it’s about modeling the product, you know, it’s about optimizing the size of your data model, optimizing the execution rules. We’re, for example, running 20, 30 thousand rules per second in many many cases. And it obviously has to be smart execution because eventually every user wants to get the right message at the right time within milliseconds.
Because they’re right now on the website or in your product and you have just less than 500 milliseconds to close the loop.
So that’s the world we live in, and the fact that you need to think about the way you build the model, the run time, is critical. Many engineers are usually at, are kind of focusing on the area where seconds is fine.
You know, even 3-5 seconds, so to become an engineer that knows how to build in real-time you need to shift the way you think about everything you do, starting from coding, the size of object you’re building, the way you understand multi-threading, all those elements actually you learn in university but scarcely apply in our day-to-day because most application are fine with 2-3 seconds and your code can be very inefficient and will still work as opposed to real-time when, if your code is inefficient you’re not going to be able to deliver that at scale.
