“DataOps is a process-oriented approach, defined by data scientists, data analysts, and data engineers, to shorten the entire data analytics lifecycle and facilitate collaboration”.1

Following a conversation with Víctor Razzon, head of DataOps at Vinkos, we’re launching a new series of articles about DataOps. In this first installment, we’ll explore in more detail what it is, how it has evolved, and how we integrate distinct values into it.

In many organizations, the operation of data platforms begins with a seemingly simple objective: to ensure everything is “up and running.” However, when data becomes central to the business, as is the case with billing, logistics, analytics, or areas that must make strategic decisions, what is defined as “simple” falls short, and that’s where DataOps comes in. It represents a natural evolution of operations when data ceases to be merely technical input and becomes a critical asset for the organization.

Traditionally, operating a platform meant reacting when it failed: handling incidents, bringing services back online, and restoring processes. Today, that approach is no longer enough because business areas need to:

• Process information faster.
• Trust that the data is accurate and up to date.
• Guarantee continuity, even while making changes or updates.

In this context, DataOps emerges with a comprehensive approach that combines operation, control, quality, and alignment with the business.

“It’s not just about extinguishing a fire but about ensuring that the platform continues to generate value every day,” says Víctor.

DataOps as a Comprehensive Model, not a Tool

One of the biggest misconceptions surrounding DataOps is thinking it’s just another product or technological layer, because in practice, it’s an operational model that connects:

• The technical operation of the platforms.
• The real needs of the business.
• The controlled evolution of technology.

This implies operating with clear rules:

• Changes that don’t halt operations.
• Analyzed and planned updates.
• Incidents addressed quickly and with context.
• Constant communication with the areas involved.

The goal is to ensure that data is always available, reliable, and ready for use.

This is where the role of a partner like Vinkos becomes crucial —not to replace internal teams, but to complement and strengthen them. Because most companies already have their own operations and IT teams, the differentiating value emerges when that team relies on us because:

• We have in-depth knowledge of the technology being used.
• We understand how the manufacturer works.
• We are accustomed to responding in critical scenarios.
• We can assume full operational responsibility if necessary. 

In practice, on a daily basis, DataOps is built by working hand in hand with IT teams, sharing responsibilities, documentation, knowledge, and experience. The same applies to the relationship between the client, Vinkos, and the manufacturer.

On the other hand, internally, Vinkos plays a critical role: questioning the manufacturer, validating their proposals, evaluating which innovations are worth adopting and which are not, and translating all of this into concrete decisions for the client.

This balance is essential to avoid unrealistic promises and to ensure that every change makes sense from both technical and business perspectives.

And when things go wrong…

The true value of DataOps can be experienced during critical moments, because these are what truly define a mature operation. Recurring outages, constant incidents, infrastructure failures, human error, or poorly calculated updates test more than just the platform; they test trust, which has become a solid value among Vinkos, manufacturers, and customers.

Because a well-executed DataOps approach not only seeks to resolve incidents, but also to:

• Understand the root cause.
• Clearly communicate the impact.
• Propose viable solutions.
• Restore stability without improvisation.

Often, the difference between losing a customer and strengthening the relationship lies in how the crisis was handled.

Data operations are no longer measured solely by availability; other factors, such as cloud costs, processing efficiency, governance, and the lack of a data owner, come into play.

DataOps is evolving because the importance of data has shifted, placing it at a crucial level in the daily operations of businesses. It undoubtedly involves collaborative management approaches, improvements in data analysis, and the assurance of data reliability and governance throughout its lifecycle.

Don’t miss the second installment of this series, and in the meantime, follow us on LinkedIn to discover more from Vinkos.