The data infrastructure race in LATAM has already started: where do you stand?

Databricks crossed $1.5B in annual revenue. Deloitte flags data as a redefining force in LATAM. What separates mid-size companies pulling ahead from those still debating whether it's worth it.

This week Databricks announced that its data warehouse business crossed $1.5 billion in annual revenue. Around the same time, Deloitte published an analysis identifying infrastructure, data, and trust as the three forces that will redefine competitiveness in Latin America over the next several years.

Both signals point in the same direction: companies that have been investing in their data layer are generating enough value to sustain an entire industry around them. And companies still deliberating whether it’s worth it are losing ground every month they wait.

The challenge is that most of that analysis focuses on banks and large corporations. Mid-size businesses — 50 to 500 employees, operating across LATAM — are still in the meeting where someone asks whether “the data thing” is a priority or can wait.

It can’t wait. This article explains why, what the companies pulling ahead have in common, and what the first real step looks like.

Why data infrastructure became urgent now

For years, “organizing data” was a continuous improvement project — something done when time and budget were available, which almost never happened simultaneously.

What changed isn’t the importance of data — it was always important. What changed is that nearly every productivity and automation tool companies are buying today (AI-enabled ERPs, BI platforms, workflow automation, AI agents) requires clean, centralized, accessible data to function.

Without that foundation, every new tool adds complexity instead of capability. And the cost of that complexity accumulates quietly: in manual work hours, in decisions made on stale information, in projects that never reach production.

What do the companies pulling ahead have in common?

It’s not budget. In most cases we’ve seen, mid-size companies with functioning data infrastructure made three relatively simple decisions before others did.

They centralized data before trying to automate it

The companies pulling ahead didn’t start with the most visible project — the executive dashboard, the chatbot, the ERP’s AI module. They started with the most boring question: where does each piece of data live, and who is responsible for it?

That question leads to building a minimal integration layer: operational systems (ERP, CRM, management tools) connected to a central location where data can be queried without requiring someone to export it manually.

This isn’t a six-month project. A functional initial integration can be built in weeks. What takes time is the decision to start.

They have a clear data owner

Not necessarily a Chief Data Officer with a dedicated team. In companies of 50 to 200 people, it might be the IT Director, the operations lead, or in some cases someone from finance who has historically been the informal custodian of reports.

What’s consistent in companies that work well: there’s one person who can answer “what’s the official source for that number?” without asking three different teams.

In companies falling behind, that question triggers a discussion that outlasts the meeting where it came up.

They built infrastructure that can grow without a complete rebuild

This is the most technical point, but also the most relevant for decision-makers. Companies that cut corners early — a shared spreadsheet that became the reporting foundation, an undocumented direct connection between two systems, a script that runs on someone’s laptop — end up rebuilding everything when they want to scale.

Companies ahead invested a bit more upfront to do it in a way that extends: a data warehouse with defined structure, documented transformations, controlled access. It doesn’t need to be Snowflake or Databricks from day one. But it needs to be something the next engineer can understand without the previous one having to explain it.

What do the companies falling behind have in common?

There are clear patterns on the other side too.

Multiple sources of truth for the same number. The CRM says one thing, the ERP says another, and the sales team has their own spreadsheet. Every time someone needs a figure, an investigation begins.

Reports that consume days of manual work per month. In several companies we’ve worked with, the monthly close process involves two or three people spending two to five days cross-referencing data from different systems manually. That time has a real cost that rarely shows up in the “how much does a data project cost” analysis.

Technology projects that stall before they start. A company contracts a BI system, activates the AI module on their ERP, or starts an automation project. Two months in, the project is stuck because the input data is dirty, incomplete, or in a format the tool can’t read. The project gets abandoned or reduced to a fraction of its original scope.

When is it not urgent?

There are cases where investment in data infrastructure can wait. It’s worth being honest about this.

If your company operates with a single central system (an ERP that concentrates everything) and the reports you need come directly from that system without manual intervention, you probably don’t have an infrastructure problem yet. You will when you start growing or integrating new tools, but today it’s not urgent.

If your company has fewer than 20 employees and operational decisions are made with information one person can hold in their head, the overhead of building formal infrastructure outweighs the benefit. Scale first.

If you already have an internal data team with the capacity to build and maintain infrastructure, this article probably isn’t telling you anything new. The problem described here is for companies without that installed capability.

What’s the first real step?

The most honest answer we can give: before buying a tool, before launching a BI project, before activating anything related to AI or automation, answer three questions:

1. What are the three indicators the business needs to see each week to make operational decisions?
2. Where does that data live today, and how reliable is it?
3. How long does it take to be available when someone needs it?

If the answers are clear, you have a foundation to build on. If not, the first step is to clarify them — before investing in any tool.

That’s exactly what we do in the Smart Blueprint: a 10-hour diagnostic that answers those questions with the internal team and produces a concrete priority map.

Actionable takeaway

This week, identify the most critical report in your business — the one that drives the most decisions — and map where each data point in it comes from. How many systems it touches, how long it takes to be ready, and who is responsible for its accuracy.

If that exercise takes more than an hour or reveals more than three distinct sources, you have an infrastructure problem that’s already costing something every month.

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FAQ

What size company needs formal data infrastructure?

Generally, the inflection point is around 50 employees, or when the company starts operating with more than two systems generating relevant business data. Below that threshold, the manual solution is manageable. Above it, the cost of disorganization quickly exceeds the cost of fixing it.

How long until you see impact?

It depends on the scope of the first project, but in most cases a functional initial integration — enabling reliable, automatic reporting for key indicators — can be operational within 4 to 8 weeks. The impact on manual work hours is immediate.

Do you need to migrate all systems, or can you start with one?

You always start with one — the most painful one. The right architecture allows data sources to be added progressively without rebuilding what already works. The key is that the initial design accounts for that expansion, not that it solves everything from day one.

How do I know if my company is ready for a data project?

If you have clarity on which business decisions you want to improve and who internally will use the data, you’re ready. Technical readiness (systems, access, team) is an input that gets mapped during the diagnostic, not a prerequisite.

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Related:

• Your company doesn’t have an AI problem. It has a data problem. — why most AI projects fail before they start
• When two reports from the same business give different numbers — how to identify and resolve data inconsistency