Every company wants to be a data-driven enterprise today. Customers have become more demanding, so understanding their behaviors, and then making critical business decisions faster than a competitor, has never been more important.
This is where good DataOps is crucial, but the fly in the ointment is that many businesses now have billions of data points to act on, sometimes in real-time. That’s a lot of data, creating a perpetual traffic jam grinding decision-making to a halt, forcing teams to wait weeks for simple insights. Somehow, data-driven is starting to feel more like a buzzword than an advantage.
3 Principles for Faster Data Velocity
- Shift responsibility left.
- Establish data contracts.
- Build paved roads, not walled gardens for data.
Slow data may be the most critical and overlooked bottleneck holding businesses back. It’s why businesses are investing millions in tools to ingest and action customer insights faster, from powerful analytics tools, customer data platforms, to cloud warehouses and AI models.
But in reality, the problem has less to do with the tools and more with approach. If brands want better data velocity today, they need to start treating their data like a product, not a byproduct.
How to Improve Data Velocity
For decades, many businesses have operated on a fundamentally broken, industrial-era assembly line. Business teams — including app developers, marketers and sales teams — have created messy data as a byproduct of operations. This data gets tossed over a wall to a centralized data team, who are expected to act as a janitorial service, cleaning up and trying to make sense of the data before handing it to an analyst, who in turn can only hope it’s what they asked for.
It’s a bottleneck-by-design situation. No matter how talented the central team is, they need context about the source. Otherwise, they’re stuck in a reactive loop of firefighting, fixing downstream dashboards that broke because an upstream team changed a field without warning. It creates distrust, burnout, and worst of all, slow and unreliable data that no one trusts enough to use for steering a company’s strategy.
For true data velocity, especially with AI bound to exponentially inflate data volumes, brands should reorganize their thinking around a data-as-a-product model, instead. This means decentralizing ownership and treating data with the same rigor they apply to customer-facing products.
Here are three principles brands need to apply.
1. Shift Responsibility Left
Software developers often talk about shifting left when referring to moving quality and testing to the earliest possible point in the development cycle. In DataOps, we must apply the same logic to data.
The traditional model places data quality ownership at the end of the line, with the central data team, but that’s woefully inefficient. It means the team that creates the data (i.e the domain team) is the only one with full context to ensure the data’s accuracy and integrity from the start.
Shifting left means the app development team is responsible for the app’s features, but also for the quality of data events produced by the app. The marketing team then becomes responsible for the integrity of the campaign data they generate. When the producer owns the quality, problems are stopped at the source, before polluting a dashboard or corrupting a machine-learning model. The central data team becomes an architect instead of a janitor.
It’s as much a cultural optimization as a technical one. Shifting left moves a company from centralized gatekeeping to decentralization that fosters autonomy without sacrificing consistency. Data governance becomes embedded by design, not enforced after the fact. Shifting left, in other words, becomes the operational expression of treating data as a product, and quality as a shared responsibility.
2. Establish Data Contracts
The next thing you need is a mechanism for alignment, and that’s where a data contract comes in handy.
A data contract is a formal, API-like agreement between a data producer (e.g., the app team) and a data consumer (e.g., the analytics team). The agreement clearly defines the data’s schema, semantics, quality metrics, and service-level objectives. It’s effectively a guarantee in which the producer promises to deliver data in a specific format to a specific standard, and the consumer agrees to use it as specified.
Such a simple governance tool eliminates the guesswork and rework plaguing the traditional model. If the producing team wants to change something, the contract forces a discussion with downstream consumers before the change is made, which prevents breakage. The point is to build up trust, which is arguably the most important asset in any data culture.
In practice, data contracts often manifest as a tracking event definition, i.e., the living specification of how web, app or back end systems emit data into the ecosystem. It can be managed as a simple spreadsheet during early maturity, or formalized through schemas in Apache Avro, Protobuf, or JSON Schema. Each event is defined with its payload structure, field semantics, versioning rules, and data-quality expectations. By enforcing these contracts at the point of emission, teams ensure that analytics, attribution models, and downstream transformations remain stable even as products evolve.
Over time, these tracking definitions evolve into a shared, machine-readable catalog of the company’s operational vocabulary. This shifts data governance from reactive documentation to proactive design.
3. Build Paved Roads, not Walled Gardens
Shifting responsibility to domain teams creating data might sound like it invites chaos, but it’s about empowering them with the right tools. Central data teams can’t exist in a walled garden, where you queue up to beg for resources. They need to become builders of paved roads.
As for what that paved road looks like, it should be a set of standardized, self-service tools, templates and platforms that any team could use to build and deliver products fast. The central team’s job is to curate the best-in-class tools for ingestion, transformation, visualisation and monitoring — and then provide them as a service. This federated model kills two birds with one stone: the domain teams get autonomy and speed to manage their data products, while the central team handles governance, security, and interoperability across the organization.
The digital assembly line approach is no longer a viable strategy. When data underpins so much of what businesses do in the modern world, slow data velocity is a choice. But so is breaking the bottleneck, by going from data as an accidental and messy byproduct, to engineering it as a core product. It’s the difference between navigating with a coffee-stained, months-old map versus steering with a real-time, high-definition satellite feed.
To put it another way, companies shouldn’t be buried in data by always cleaning up the past. They should feel actively empowered by it to launch new ideas that anticipate customer needs, and make decisions at whatever speed the market is running at.
And before you rush off to buy automation tools, remember: no technology is a replacement for sound engineering and process. They are multipliers that deliver the greatest impact once core logic and data structure has already been optimized. Get the fundamentals right first, then enable automation where it compounds value most effectively.
Because at the end of the day, speed doesn’t come from more dashboards or bigger clouds. It comes from teams who trust their data enough to move fast.
