From Data Bottlenecks to Data Products: Building for Speed and Scale
<p>Data now powers nearly every modern business. Without it, operations stall and decisions degrade. As customers become more demanding, the ability to turn raw user behavior signals into decisions – ...
Data now powers nearly every modern business. Without it, operations stall and decisions degrade. As customers become more demanding, the ability to turn raw user behavior signals into decisions – fast – has become a competitive differentiator.
In gaming ad-tech, where campaigns and in-app behavior change by the second, slow data is a liability, not just a nuisance. The performance of the next campaign or feature often hinges on the speed and reliability of the data underneath it.
Many companies have stockpiled vast datasets believing that volume is a key success driver for the business. In reality, those piles can slow you down. The sheer amount of data businesses must now process and act on delays insight generation.
As companies sift through the masses of data, the time it takes to gain actionable insights becomes disproportionate to the time it takes to make quick business decisions that create a competitive edge. Systems become overwhelmed and overcomplicated, with teams sometimes waiting weeks for simple insights. Traditional pipelines treat data as a byproduct, passing messy events from team to team. That reactive model collapses under the weight of real-time, high-volume user signals and the demands of AI-driven optimisation. This is where good DataOps becomes crucial.
While it’s all well and good for companies to be pouring money into new analytics tools and AI models to help make data processing faster, these tools alone won’t fix slow data. The root problem is a far more cultural one. To achieve true data velocity, brands need to start treating data like a product, rather than a byproduct. That shift requires measurable reliability targets, clear ownership, and paved roads that make the right thing the easy thing.
The Bottleneck Problem
Data tools can be useful, but investment in them tends to be uneven when compared to the time spent ensuring the data itself has the necessary context and is shared in a digestible format.
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Businesses have been operating on fundamentally broken assembly lines that AI tools alone cannot fix. In this assembly line, data is transferred from one team to another without consideration for how the next team will begin to understand what is shared with them. As a result of data being produced as a byproduct of operations, the centralised data team is thrown massive amounts of data, which they are left to clean up. In turn, the assembly line continues to fall apart as the team becomes overwhelmed by the sheer volume, trying to make sense of it before sending the data to analysts downstream.
This creates a bottleneck-by-design situation. The central data team works hard to sort through the mess, but because they didn’t create the data, they can’t fully understand or fix the problems created. Therefore, as one mess is cleaned up, another is quickly created. This creates a reactive cycle with teams constantly fixing broken reports after the original team changes something without warning. The result? A frustrated, burned-out team and slow, unreliable data that no one can fully trust for decision-making.
With AI now rearing its head across all operations, and likely intensifying the amount of data created, brands must rethink how they manage and use that data if they want real speed and impact. To regain control, we need two things: shared accountability for quality at the source and objective measures of pipeline health and speed. One proven approach that can be taken is the ‘data-as-a-product’ model. This approach involves decentralising ownership of data and implementing the same scrutiny applied to external-facing customer products.
Here are three core principles brands need to apply to make this operational philosophy a reality.
Principle One: Shift Responsibility Left
Software engineering learned long ago that pushing quality checks to the left (earlier) reduces cost and accelerates delivery. Do the same with data: move quality checks and testing to the earliest possible stage of the development cycle.
As it stands now, the central data team oversees data quality only at the final stage, a process that is not currently working. This is because it has resulted in the domain team, who create the data, being the only ones who have the full context necessary for proper accuracy and integrity.
If businesses shift left with their approach, app developers themselves will take responsibility for the data created by applications. By giving the producer ownership of the quality, ongoing issues can be stopped before trickling down into data dashboards or machine-learning models.
Ultimately, this is more than just a technical change. Shifting left will be a culture change that moves toward Data Mesh principles. By embedding ownership and quality within the domains that produce and use data, organisations replace central gatekeeping with shared accountability. Each domain now becomes a creator and protector of reliable data, ensuring governance is built in from the start rather than enforced later.
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The central data team turn into architects, not janitors. They stop firefighting broken dashboards and focus on platform guardrails, templates and observability, resulting in fewer surprises, faster time-to-insight, and higher trust.
Principle Two: Establish Data Contracts
Much like any contract, a data contract creates clear guidelines and definitions for everyone involved. It informs producers and consumers of a dataset’s schema, semantics, quality metrics and service-level objectives.
The data contract – an API-like agreement between data producers and data consumers – assures consumers that producers will deliver data in the right format and to agreed standards.
This simple step can massively reduce team confusion and speed up data processing. It also greatly improves trust, both in the data and across teams. If the application team wants to invoke a change to the contract, the change is proposed, versioned and approved by affected parties, preventing breakages.
In practice, these contracts often begin as Tracking Event Definitions, which are live specifications detailing how systems emit data. These can start as simple spreadsheets and mature into formal schemas. Each event defines its structure, meaning, and quality rules, ensuring stability even as products evolve. Over time, these definitions become a shared governance asset, developing into a machine-readable source of truth that embeds policy and quality checks directly into data production, echoing the Data Mesh principle of federated, code-driven governance.
Principle Three: Highways, Not Roadblocks
Understandably, giving ownership of data to the teams creating it may seem chaotic. But it isn’t about losing control over it; rather, it is about giving teams the freedom and tools to work faster and smarter. At the end stands the lighthouse vision of a self-service data platform where every consumer can independently generate insights for standard questions and only reach out for support when tackling more advanced analyses.
The central data team shouldn’t be a gatekeeper working in a silo. Instead, we must lay foundations that make speed possible: clear, accessible infrastructure that lets every team work without friction—building highways rather than roadblocks.
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This infrastructure takes the form of standardised, self-service platforms, templates, and tools that could be easily adopted by any team to produce their own products. From here, the central data team’s role then evolves into that of an enabler, helping curate top-tier solutions for data processing, monitoring, and transformation.
This model, in turn, benefits everyone. While the domain team gets the opportunity to manage their own data products, the central team is afforded the ability to manage the governance, security, and interoperability across the business.
The current, outdated model for data processing is no longer a suitable option for businesses relying so heavily on the insights produced. We are seeing data become the key driver behind every moving part of organisations, across every layer of the business, and because of this, slow data velocity must be avoided.
Engineering data as a product is how you break the bottleneck.
Data should shape the future of business – launching new ideas and meeting customer demands. While AI and automation tools will accelerate the process, they are a band-aid over a bullet wound. Technology only multiplies the success of the foundations on which it is operating. Get the logic and structure correct first, to allow for these tools to amplify what is already working.
About the Author: Andreas Paech is Head of Data Engineering at Exmox, a leader in rewarded user acquisition for mobile games and apps. Exmox is part of Aonic, a global technology and video gaming group.
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