From Data Streams to Revenue Streams: Your Data is an asset
Your streaming data is an asset. Don’t keep it for yourself, always seek new ways to generate value. Treating it as an AI asset for others to use is key.
I’m a CTO and have loved data streaming for years. Too often, I see Kafka being used just as a technical tool, aiming for a complex “central nervous system” that focuses too much on infrastructure. Data infrastructures should do more than just look good — they should constantly generate new value and revenue. I care about outcomes, not just the details of how it works. Finding ways to monetize data is important: data is an asset. Getting lost in the technical side of streaming is not.
Every data infrastructure can follow a maturity level to transform data from a raw asset into something that drives growth and profit:
1. CREATE: Data only has value when it drives decisions; streaming tech like Kafka helps activate it in real-time.
2. SCALE: Scaling requires high-quality, fresh, exclusive data for AI-driven businesses — real-time access is key.
3. MONETIZE: Monetization is about selling real-time streams and AI-ready data; compliance and first-party data will be crucial.
Stage 1: Create (Valuable) Data
Data has zero value until it’s used. Let me repeat: Data has zero value until it’s used. How much of their data do enterprises actually use? 20%. 20%. We’re creating some kind of digital trash.
If data isn’t driving a change in behavior, it’s not even useless, it’s dead weight. If you have no data strategy, you’ll fall into this trap, collecting data with no real plan for how to use it.
Data is valuable when you have use-cases that make it actionable, with users who derive insights or power decisions from it. The value of data decays rapidly if not used in real time. “The value of data is the value of the marginal change in actions taken after adding the data to your business process.” (source)
Data Value = f(use_case, consumer_relevance, volume, quality, AI_purity, uniqueness, freshness, exclusivity)
Data Vision? Do you have it?
Most organizations start by collecting data from internal sources like customer interactions, product usage, and business processes. The problem? Most companies get stuck just accumulating data with no clear vision.
At this stage, the key is to avoid the trap of thinking you’ve succeeded just because you have plenty of data in your datalake. How actionable is that data right now? The companies that win in the long run are already moving data in real-time from the get-go, because it sets them up for quicker scaling (Step 2) and because they are focused on what matters.
Make data useful and immediate
This is where streaming technology like Kafka changes everything. Collecting data isn’t enough — it needs to be made useful in real time. You don’t want to work with stale data. Real-time data allows for immediate decisions, and immediate decisions lead to immediate profit. But not all data is valuable — clean data drives meaningful actions, while dirty data (poor quality) just create noise or issues. The question you should be asking is: how much of your data is truly useful, can be stream and used, and how much is just clutter?
Instead of letting data sit idle in databases, it flows directly into processes and pipelines, making it ready for use the moment it’s generated. This enables even the earliest stages of data maturity to be responsive. If your data isn’t being activated as it’s collected, you probably have an issue.
Kafka is more than just a collection tool — it’s the backbone of modern data-driven organizations. In industries like finance, retail, and healthcare, batch processing is obsolete. Real-time data is now the standard for staying competitive, whether it’s powering instant fraud detection in banking, personalized offers in retail, or patient monitoring in healthcare.
The need to build a Data-Driven Culture
Finally, none of this matters without the right organizational mindset. Data culture is essential. You need embed data-driven thinking into the core of all teams decision-making process. With tools like Kafka, this becomes possible — data becomes the lifeblood of modern organizations, flowing continuously and feeding every operational applications to make decision.
In summary, the “Create” stage isn’t just about collecting data — it’s about collecting the right data, in real time, with a clear strategy for how it’s going to drive decisions and ultimately impact your bottom line.
Fact: Close to 75% of organizations have failed to create a data-driven organization (source: MIT).
Stage Two: Scale Data
Bravo, you are creating some useful data! Now, it’s time to scale.
Scaling is not just about handling more data, but handling more smarter data. The businesses that can scale both infrastructure and intelligence can act on data as it flows in, and reach maturity faster.
Quality over Quantity
As you scale, not all data is created equal. Data quality and freshness start to matter more than raw volume. Scaling isn’t just about storing more data; it’s about activating smarter, real-time data.
If you’re scaling low-quality, contaminated, or outdated data, all you’re doing is creating a bigger mess. AI, for instance, is only as smart as the quality of the data it ingests — garbage in, garbage out.
For instance, think of e-commerce platforms using real-time customer behavior data to personalize recommendations instantly. If you’re relying on old or low-quality/generic data, your customers won’t find any meaningful items in your recommendations, meaning missed opportunities.
The value of data decays rapidly if not used in real time. It will be too old or just replaced by something else later. “Fresh” data can be the difference between winning and losing a customer, market, or entire quarter.
This is where streaming technology like Kafka steps up, enabling real-time data flow that keeps your operations nimble and reactive, like how ride-sharing apps adjust pricing dynamically based on real-time demand (and surge periods…).
Infrastructure Scaling vs. Data Scaling
Most organizations treat data scaling as an infrastructure problem — cloud storage, data warehouses, managing silos, etc. Sure, scaling data isn’t free: storage, compute, and networking costs will skyrocket.
There is this dream of “Central Nervous System”— a technical organization where Kafka connects everything and everyone, to allow data to flow seamlessly in large enterprises. But here’s the problem: scaling data isn’t just about building better and larger infrastructure. A tech-heavy focus on the nervous system might help you collect and process data, but it doesn’t automatically create business value. It’s only a means to an end.
Scaling data is about outcomes, not just infrastructure. The real challenge isn’t just having a more sophisticated tech stack — it’s using that data to drive business-critical results. Are you empowering your teams with real-time data that helps them make faster decisions, build better products, or serve customers more effectively? It’s not enough to push data through your systems, it needs to be translated into a competitive advantage.
Data Governance & Compliance
Regulations: where dreams of scaling data go to die. As you scale, you can’t ignore the compliance complexity coming your way GDPR, CCPA — they’re not optional, and the larger your data infrastructure, the bigger the target on your back. Data governance becomes critical here.
Who owns the data? Is it clean, compliant, and “ethically” sourced? You don’t want your scaling success to turn into a AI or regulatory nightmare. Missteps at scale are expensive.
While scaling, data streaming opens the door to external monetization. For instance, Financial Institutions can offer live data feeds to hedge funds looking for an edge in the market. As your data matures, it becomes a commodity in its own right — partners, clients, and even competitors will pay for access to exclusive, real-time data streams.
Data is a product: you can create entirely new revenue streams by selling live data feeds. Exclusive, hard-to-find, qualitative data becomes a competitive moat.
Fact: 57% of Data Leaders say that data users fall back on quick fixes to access data outside of proper data access governance practices. (source: Immuta)
Stage 3: Monetize Data / 3rd-party data sharing
The organizations that figure out how to use data streams to build external partnerships and create new revenue models reach the final stage of data maturity: monetization.
Data isn’t just a byproduct of business — it’s a strategic asset and your most powerful competitive moat. However, data only becomes valuable when it starts generating revenue. The real opportunity comes from moving beyond internal use and monetizing your data streams. By offering live, AI-ready datasets and exclusive, real-time access, you can transform data into a premium product.
As 3rd-party data sharing skyrockets and the demand for clean, compliant, and exclusive data grows, those who control high-quality data streams will dominate the market. In the age of AI, data is the new currency, and those who own it hold the advantage.
Data Value = f(use_case, consumer_relevance, volume, quality, AI_purity, uniqueness, freshness, exclusivity)
Streaming Data becomes the Product
This shift allows businesses to sell instant access to their most valuable data streams.
- Financial Services can sell real-time market data and insights for others to make better predictions and decisions
- Retailers can share live inventory updates, live trends, or get intelligence from their deliveries with partners
With technologies like Kafka, data streaming becomes a monetization engine, not just a backend process. Streaming Data becomes the product. Whether you’re streaming market data, customer behavior, or operational metrics, real-time data is infinitely more valuable than anything batch-processed or static. Organizations that realize this early will dominate their industries.
Conduktor offers a solution to expose data streams without duplicating it from core systems, with security and encryption seamlessly integrated:
Data is the new gold: AI is the pickaxe
As AI continues to reshape industries, data has become the energy powering the systems. First-party, high-quality, compliant data is the new gold (no GenAI garbage). AI-driven businesses need exclusive, clean datasets to train their models and make smarter decisions. This is why companies that control their own real-time data streams are sitting on a goldmine.
With AI regulations tightening, the creators of data are the ones in charge. Data sourced ethically, cleanly, and legally is now a premium asset because AI models are built on this “golden data” (like ScaleAI or defined.ai). This is why companies that own rich unique datasets and control its sharing are at an advantage.
Imagine autonomous vehicle companies buying live traffic data streams, or investment firms paying for real-time market behavior feeds. The more valuable and exclusive data you collect and stream, the better your AI gets. The better your AI, the more valuable your data insights become, creating a self-perpetuating cycle of value. (i.e. the AI-Data flywheel)
The 3rd Party Data Sharing Explosion
The alternative data market (3PD) is set to become one of the most lucrative areas for monetization, with a projected CAGR of 50.6% from 2024 to 2030, starting at $7.20 billion in 2023 (source). Companies that shift from internal data use to selling externally will get significant new revenue streams.
Buyers are hungry for fresh, clean, unique, and compliant data to power alternative investment strategies, market research, and AI models. With real-time data streaming (using Kafka), businesses can offer live exclusive data streams as premium services, providing valuable, timely information to industries like finance, logistics, and retail.
Clean data will be essential in the AI economy, making third-party data sharing a rapidly growing and profitable opportunity for those ready to meet this demand.
Conclusion: Are you ready to sell your gold to the AI world?
The path to data streaming maturity is simple: create, scale, and monetize. First, focus on creating real-time, actionable data that drives decisions.
As you scale, it’s not about handling more data — it’s about using it smarter and faster. Real-time streaming allows every part of your organization to stay agile and aligned.
Finally, the real payoff comes with monetization. Exclusive, high-quality data streams are gold in today’s AI-driven economy. Companies that can monetize their data effectively — by offering real-time access to external partners — will dominate the market. Data is the new currency, and those who manage it strategically will lead the future.
