As a CDO, I live in the tension between speed and trust. On one hand, there’s the relentless push for fast, data-driven decisions, from product leads wanting to experiment today, to marketing teams needing immediate insights to optimize campaigns, to leadership asking for strategic dashboards yesterday. On the other hand, there’s the deep responsibility I carry to ensure that the data we deliver is accurate, interpretable, and trustworthy.

This is the modern data dilemma: how do we deliver fast, without compromising quality?

I’ve wrestled with this challenge every day in my role. We operate in a hyper-competitive, fast-paced environment where time-to-market (TTM) isn’t a buzzword; it’s survival. But data isn’t like code. It’s not just about pushing something to production. It’s messy. It needs context. It needs governance. And most importantly, it needs people to trust it. That trust, once broken, is nearly impossible to rebuild.

Why time-to-market matters so much

Let me be clear. I’m not against speed. I’m for it. In fact, my team is measured, in part, by how quickly we can empower the business with insights and products. TTM is critical for everything from experimentation velocity to monetization strategies. If we wait for perfect data, the opportunity is often gone. Business doesn’t slow down because the data isn’t clean.

But, and it’s a big but, if we sacrifice rigor for speed, we lose in the long term. We create noise instead of signals. We breed mistrust in our data assets. We waste cycles correcting bad assumptions, sometimes after decisions have already been made. I’ve seen it happen, and the price is always higher than the cost of doing it right the first time.

The cost of cutting corners

I remember a case, in a previous role at a large communications company, where we pushed out a data product too quickly. It was a churn prediction model designed to flag high-risk customers for proactive retention campaigns. It was developed in record time, plugged into the CRM, and rolled out to the customer success teams before we fully validated the signals.

It didn’t take long before the account managers started asking tough questions. “Why are some long-term loyal customers showing up as high risk?” “Why did we miss customers who churned last month?”

We skipped the crucial step of aligning business context with behavioral and billing data, and hadn’t sanity-checked the model’s assumptions with retention experts.

The result? A pause on the rollout, a wave of mistrust in the data science team, and a scramble to repair both the model and the relationship with the field teams. It was a difficult but important lesson: there’s a line where speed becomes recklessness.

Building the right balance

After years of experience, I’ve developed a few key practices to help my team balance speed with trust, and it’s an ongoing journey.

First, we distinguish between prototypes and products. We’re not afraid to deliver early insights fast, but we label them clearly as “first cuts” or “exploratory views.” If something isn’t production-grade, we say so upfront. This protects trust while still enabling iteration.

Second, we’ve implemented internal SLAs for key data products and pipelines. This helps align expectations across the org: when something needs to be fast, we plan for it, including the validation, documentation, and maintenance it will require. When something can be more considered, we build in room for interpretation and refinement.

We also run what we call “data readiness sprints,” short, focused efforts where cross-functional teams (analysts, data engineers, domain experts) come together to bring a dataset or model to a level that’s both usable and explainable. These sprints have become a cornerstone of our ability to deliver quickly without flying blind.

Saying “no” and explaining why

One of the hardest, but most important, things I’ve had to learn as a data leader is how to say “no”. Or rather, how to say “not yet”, and mean it.

When stakeholders come with urgent requests: “Can you just pull this data real quick?” or “Can we launch this model today?”, my instinct is to help. But part of my job is protecting the long-term health of our data ecosystem. That sometimes means pushing back.

The key is not just saying no, it’s bringing people into the why. Explaining the tradeoffs. Showing what could go wrong. Sharing real examples of previous missteps. When we do that transparently and respectfully, we don’t just delay requests, we build advocates.

Investing in relationships and literacy

Ultimately, the data team isn’t a service desk. We’re a strategic partner. That means investing in relationships across the org, and in raising the data literacy of our stakeholders.

At Yad2, my current company, we’ve made a concerted effort to move from being seen as “data providers” to becoming “data collaborators”. We run onboarding sessions, host internal data talks, and build self-service tools that empower others to work with data responsibly.

It’s not just about speed or quality, it’s about shared ownership.

Measuring What Matters

We track ourselves against dual KPIs: velocity and reliability. That means measuring how quickly we ship, but also how often our products are used, how often they’re questioned, and how often they break. We conduct regular retros to learn from both successes and failures.

We’ve also started asking a simple question after each delivery: Would you bet your budget on this data? If the answer isn’t a clear yes, we go back and fix it.

A culture shift

The biggest transformation, though, isn’t technical. It’s cultural. We’ve worked hard to create a culture where “fast” doesn’t mean “dirty”, and “quality” doesn’t mean “slow”. Where people understand that data is an asset, and like any asset, it requires care, intention, and accountability.

This culture is still evolving. But I see signs that it is improving product teams who are asking better questions, analysts challenging assumptions, and data engineers thinking about data contracts as part of their workflow.

Speed and reliability are not opposites. They’re two sides of the same coin. If we want to build data products that actually move the business forward, we can’t afford to pick one at the expense of the other.

At Yad2, we’ve chosen not to compromise, and that choice guides everything we do.

About the Author:

Claudia Gendler is an executive leader in Data and Analytics with extensive experience in telecom, finance, and startups. As VP – Chief Data & Analytics Officer at yad2, she drives data strategy and monetization, and fosters a data-driven culture.

She has led large teams in developing machine learning models and implementing AI/BI products at major telecommunications, technology, and insurance companies, managing digital transformations and cloud migrations. Gendler is also a PhD researcher in Advanced Analytics & AI at Bar Ilan University.