Does Your Martech Stack Really Need Real-Time Data?

In martech, we have long chased the false idol of real-time with the battle cry “If we only had real-time data, we could solve all of our customer engagement and experience issues!”

But this is a misnomer. Let me explain.

Martech vendors have used the term “real-time” since the 1990s. They borrowed the idea from computing vendors that used the term to describe operating systems and data processing. When CRM and web analytics vendors wanted to relay the fact that they could update data in their systems instantaneously (instead of via nightly batches) they started calling this “real-time”.

Thus, the term entered the martech lexicon.

And now we are in a pickle, with martech vendors selling “real-time” as the premium, when in actuality most organizations don’t have the infrastructure and systems to support it.

So, what does the modern marketer really need to be successful? I would argue it’s not how fast your data moves (real-time), it’s whether it arrives before the decision or engagement moment calls for it (just-in-time).

FAQ: Real-Time vs. Just-in-Time Data in Martech

Editor's note: These questions address common follow-ups on when real-time infrastructure is actually necessary versus when "fresh enough" data will do.

Why 'Fresh Enough' Data Beats Real-Time

Lots of effort has gone into architecting martech systems over the years that attempt to deliver data in sub millisecond times. But the question is if this is necessary in all instances? Or is it overengineering of sorts? What if we thought about “fresh enough” as a design principle instead and not a compromise?

We source the small amount of data that is needed in real time, and precision engineer the majority to arrive before the actual moment of use. As much as customer data can change and evolve (based on what you are collecting and at what cadence), the core question to ask and audit to perform is: “What decision does this data enable, and how stale can it be before the decision degrades?

Related Article: The New Rule of Customer Engagement: Sometimes Say Nothing

Understanding the Data Freshness Spectrum

Understanding “data freshness” is a core component of decision intelligence, which Gartner defines as “a practical discipline that advances decision-making by explicitly understanding and engineering how decisions are made, and how outcomes are evaluated, managed, and improved via feedback.” Advancing decision-making for better decision intelligence certainly involves knowing what use cases require what degree of data freshness. Refresh data too frequently and time, money and resources are wasted. Refresh data too infrequently and operational processes, customer experiences and brand equity and health all suffer.

Applying this concept to simple organizational use cases make this idea more concrete:

• Data needed in real-time. This includes cart abandonment data, fraud detection signals and web and app in-session personalization. Here a decision must be served in a matter of seconds.
• “Fresh-enough” data. This could be data used for retargeting audiences, performing lifecycle segmentation or building weekly campaigns. In this context, data can be updated hourly or daily.
• The exception: consent and suppression data. The ability to opt-out or account for compliance issues deserves special consideration. These two things must be accounted for in a nearly instant manner.

Matching data latency to the width of the decision window and the level or urgency instead of the fastest speed available optimizes both for cost-efficiency and system reliability. Brands can stop paying for real-time infrastructure that is not needed and can gain more stable, reconciled data by not forcing consistency to keep pace with unnecessary speed. If data arrives exactly when a decision requires it, context relevance becomes priority over speed, which is simply a vanity metric.

The Hidden Cost of Over-Engineering for Real-Time Data

I will be the first to tell you as a martech nerd that the idea of every data stream being real-time is cool. But it’s also expensive. Creating streaming data infrastructures, executing continuous data transformations, performing identity resolution and management on low latency data and building always-on data pipelines are expensive to both build and maintain.

If data teams build these things in, they are then forced to pay real-time prices for use cases that could be batch (and not real-time) in nature. That’s a tax on your martech that compounds over time — not to mention the data storage costs associated. One way to think about it is this: the faster you bring in data, the more difficult it is to keep it consistent and reconciled. Data that is “fresh enough” results in a more sound, reliable source of truth.

Where Martech Teams Are Overpaying for Speed They Don't Need

The following table highlights the most important lessons, actions and strategic considerations emerging from this piece's case against blanket real-time martech infrastructure.

Key Area	What Happened	Why It Matters	Recommended Action
Real-Time Terminology	Martech borrowed "real-time" from 1990s computing and CRM vendors and marketed it as a premium capability	Most organizations lack the infrastructure to support true real-time processing at scale	Audit which systems are marketed as real-time vs. which decisions actually need that speed
Data Freshness Spectrum	Only a small set of use cases — fraud detection, cart abandonment, in-session personalization, consent/suppression — need near-instant data	Treating all data as real-time wastes budget on unnecessary infrastructure	Classify use cases by required latency before architecting data pipelines
Cost of Over-Engineering	Streaming infrastructure, continuous transformations, and low-latency identity resolution carry ongoing build and maintenance costs	Real-time pricing for batch-appropriate use cases compounds into a long-term budget tax	Apply freshness SLAs at the use-case level instead of across the entire data infrastructure
Implementation Approach	Hybrid and federated architectures let teams access data where it lives instead of duplicating it	This enables fast access for time-bound decisions without forcing every pipeline to real-time speed	Work backward from each customer touchpoint to determine true latency requirements

How to Implement a Data Freshness Framework

How does your organization start to engineer for “fresh enough” in a “fastest speed available” world?

• Work backwards from channel or touchpoint to data source. If you begin at the point of customer interaction and consider how often could this data be accessed or refreshed in a certain instance (i.e. checking fraud signals vs. checking credit scores), you can determine needed data latency. It’s likely checking for fraud within a digital app will need to happen much more frequently that checking updates to a credit score.
• Apply freshness to individual use cases, not entire infrastructures. If service level agreements are applied to individual use cases for data freshness, applying a “fastest speed available” can be avoided for the entire data infrastructure.
• Use the latest, most modern data architectures to your advantage. Many vendors today provide hybrid or federated architectures, which allow you to access data where it lives versus having to duplicate or ingest it. Using these architectures deliberately will allow your organization to pull data in quickly where decisions are tightly time-bound, and access data in more of a batch/change data capture fashion when they aren’t.

The new battleground in martech is decision intelligence, and the brands who win the decision intelligence race will make the right decisions with the right insight at the right speed. Gone are the days of storing data, claiming real-time and attempting to react to every customer event instantaneously. At the end of the day, real-time is a capability that is very useful when needed. But fresh enough is a strategy.

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