Stop wasting millions: practical steps for real estate data infrastructure with software platforms

Written by Kate Kupriienko

If you've been following trends in real estate technology lately, you've likely encountered two buzzwords that seem to be everywhere: data platforms and AI. But here's the thing — many organizations are still struggling to bridge the gap between the two. They're investing in AI initiatives, yet the results fall short of expectations. Why? Because they're skipping the foundational step: building a solid, unified data infrastructure.

In this post, we'll outline a practical, cost-effective roadmap for building the data foundation your business needs — not just to use AI but to thrive with it. These insights emerged from a roundtable discussion organized by Proxet and Cherre, and draws on our extensive experience helping real estate firms modernize their data ecosystems.

Why data platforms come before AI

Let's start with a reality check. AI models are only as good as the data they're trained on. If your data is siloed, inconsistent, or incomplete, your AI outputs will be unreliable — and that's a waste of time and resources.

Modern data platforms solve this by:

• Centralizing disparate data sources — from property management systems to market feeds — into a unified view.
• Standardizing and enriching data so it's consistent, accurate, and analysis-ready.
• Enabling advanced analytics that turn raw numbers into actionable insights.

Simply put: a data platform is the infrastructure; AI is what runs on top of it. You can't skip the foundation.

A practical roadmap: start small, think big

One of the biggest mistakes companies make is trying to boil the ocean — launching massive, multi-year transformation projects that are hard to fund, harder to execute, and almost impossible to prove value from quickly.

Instead, we advocate for a use-case driven approach that prioritizes impact and feasibility. Here's how it works:

Step 1: Identify 2–3 high-value use cases

Start by asking: What specific business problem could data or AI solve for us right now? Common starting points in real estate include:

• Predictive maintenance (reducing equipment failure costs)
• Lease abstraction automation (speeding up document processing)
• Portfolio performance dashboards (real-time visibility into KPIs)

Choose use cases where the data already exists (even if it's messy), the business impact is clear, and the implementation timeline is short (think weeks, not years).

Step 2: Build only what you need — at first

Here's the key insight: you don't need a perfect, enterprise-wide data platform on day one. You just need enough infrastructure to support your priority use case.

That might mean:

• A lightweight data pipeline connecting two or three key systems
• A basic data warehouse with clean, query-ready tables
• A simple dashboard that surfaces the metrics that matter most

This targeted approach reduces cost and complexity while delivering value fast — which, in turn, builds internal support for the next phase.

Step 3: Choose the right software platforms

The platform you choose matters — but it doesn't have to be the most expensive or complex option. There are excellent tools for every stage of data maturity, and the right choice depends on your:

• Current data volume and complexity
• Team's technical skills
• Budget and timeline
• Long-term scalability needs

Some organizations thrive with cloud-native platforms. Others benefit from specialized real estate data tools like Cherre. And some find that a combination works best. The key is to match the platform to the problem — not the other way around.

Step 4: Build for scale from the start

Even if you're starting small, design your architecture with growth in mind. That means:

• Using modular, interoperable tools that can plug into a larger ecosystem later
• Establishing data governance practices early (naming conventions, access controls, documentation)
• Creating reusable data pipelines that can be extended to new use cases

This prevents the all-too-common scenario where a quick win becomes a technical debt nightmare.

Real-world example: from chaos to clarity

One of our clients, a mid-sized commercial real estate firm, came to us with a familiar challenge: their data was spread across six different systems, and it took their analysts days to compile a single performance report.

We helped them:

1. Identify their top priority use case: automating weekly performance reporting
2. Build a targeted data pipeline connecting their three most critical systems
3. Create a dashboard that auto-refreshed daily, giving leadership real-time visibility

The result? Reporting time dropped from days to minutes. And once the infrastructure was in place, they were able to layer in AI-powered forecasting — something that would have been impossible without the clean data foundation.

The bottom line

You don't need to spend millions or wait years to see results from your data investments. By starting with a focused use case, building just enough infrastructure to support it, and choosing platforms that scale, you can deliver real value — fast.

The organizations that will win in the AI era aren't necessarily the ones with the biggest budgets. They're the ones that move deliberately, build smart, and iterate quickly.