Turning data into a strategic advantage: How leading CRE firms are making intelligence actionable

The data strategy conversation in institutional real estate has changed. A few years ago, getting executive buy-in meant making the case from scratch; explaining why fragmented spreadsheets were a liability, why governance mattered, why the investment was worth it.

But as Pete Schow, Global Head of Data and Technology Infrastructure for UBS, put it during a breakout session at this year’s Altus Connect, "You don't have to sell data strategy anymore. Those days have passed."

What hasn't changed is how difficult it is to actually execute one.

At Altus Connect, Schow and Dipesh Shah, CTO at Stockbridge Capital Group, sat down for a candid conversation about what separates firms building a genuine data advantage from those still mired in the implementation gap. With approximately 40 years of combined experience between them, they've worked behind the scenes at some of the biggest names in real estate. Their perspective is grounded less in theory than in the specific, unglamorous work of getting data to cooperate at scale.

The wins are significant, but they don't happen overnight

In commercial real estate, a strong data strategy can produce career-defining wins. But those wins don’t materialize quickly and are never the product of a single technology decision.

For Shah, a breakthrough came on the heels of an operational integration project early in his career at a global brokerage firm. His team connected a client's work order system to the building automation infrastructure across three warehouses and discovered the client was paying for utilities around the clock despite running only two shifts. Within three months, the cost of implementation had been recovered. "Based on the data, they were paying for all this utility and nobody was there," Shah said. The fix was only possible because the data was finally connected and readable.

Schow's version came at a previous global investment company, where his team spent just over a year on foundational data work — cleaning up investment structures, mapping tenancy information, standardizing fund, JV, and asset-level codes — before ever building a visualization layer. When the moment came to demonstrate the results to the leadership team, the system ran in the background for five minutes before delivering something no one had seen before: fund performance, investment performance, JV performance, and individual asset performance, visible at the click of a button. "That's a game changer," the head of the leadership team said when it loaded. "Everybody get on that now."

For Schow, this highlighted how much diligence it takes to put these things in place. But it's doable, and when it does come together, the impact is undeniable.

Why CRE data is harder to tame than most

The CRE data problem is structural. It isn't just that the industry adopted technology slowly, it's that a significant portion of commercially relevant data was never captured in transactional systems to begin with.

Shah frames this as the ‘dark data’ problem. "A lot of our data is dark data," he said. "It sits on a C drive. It's in an Excel spreadsheet. It's not in a transactional system." Unfortunately, this has long been the operating condition across CRE.

Fragmentation compounds when you account for the external parties in any institutional portfolio's data value chain, including property managers, JV partners, brokers, and auditors. Each one is a potential source of inconsistency, and if their contributions aren't governed contractually from the outset, the problem becomes structurally unsolvable as the portfolio grows.

Schow illustrated the cascading effect of this clearly. "If you have an offline Excel spreadsheet you don't trust, a source system you don't trust, and a data warehouse you don't trust, now you're three X in" — with each unresolved layer compounding the uncertainty of the last.

Governance isn't a framework, it's a discipline

Both Shah and Schow are emphatic on one point: governance cannot be an artifact. It has to be a living discipline that extends to every party in the data value chain, and it has to be built around a clear business use case from the start, not retrofitted onto existing infrastructure.

The principle both executives return to is simple: begin with the end in mind. Reverse-engineer from the output you need; the report, the portfolio view, or the investor deliverable, rather than from the data you happen to have.

For Shah, this was best illustrated by a bi-weekly executive meeting that he watched devolve into the same argument, every time, at a prior firm. The head of leasing and the head of accounting each presented a lease executed report that used the same title but different numbers, because they had defined the triggering event differently. For leasing, a lease was executed when it was signed. For accounting, it was when rent was collected. This led to frustration at the leadership level every time, until the conflict was finally resolved with a shared data dictionary.

Schow takes governance a step further, noting that external partners need to be held to objective standards. At a previous investment firm, his team built a scorecard mechanism for property manager data quality. When contract negotiations came up, asset managers went in with empirical evidence on who was playing by the rules and who wasn't. "Going back to the competitive landscape," he said, "if that contract negotiation is back up and someone's providing data fifty percent of the time, and I have another firm that can replace them, I have objective data to do that." The soft ask had become a business standard. Schow also found that the same competitive instinct could be turned inward: making data wins visible at management forums, letting peers see what was possible, and letting the organization's natural drive do the rest.

Governance also has to be tied to outcomes the business actually cares about, a point Shah is blunt on. "Don't try to boil the ocean," he said. "Focus on what you're required to do from a compliance and investor standpoint. Don't try to gather a thousand data points if a hundred is good enough." The same logic applies to how data wins get framed upward. "If you just say I ingested 12 applications, they'll look at you and say we're not an IT company." The business case has to be made in business terms: time saved, dashboards eliminated, analysts freed up for higher-level work.

Where the market is heading, and what to get ahead of

For CRE, the next competitive frontier is speed. "If I can shorten due diligence from 60 days to 45 days to 30 days to 15 days, I’m going to win," Schow said, "I'm going to be the one that can go in and take those deals down the quickest. "Stockbridge is already experimenting at this edge. Shah's team recently deployed an agentic AI tool that pulls together broker comps, and pipeline data from their deal management system. That shift, both executives argue, requires data and technology professionals to earn a seat at or near the investment committee table. Not to make decisions, but to understand what's coming before it arrives.

Emerging asset classes have a way of exposing data gaps that nobody knew existed. Consider the scramble that occurred when senior housing and student housing took off as asset classes, and investment decisions were made without the data infrastructure in place to support them. At the time, core solutions didn’t have a native way to calculate beds instead of rooms, and Schow recalled his team having to retrofit systems until those solution providers ultimately resolved the challenge.

The lesson is a practical one: know what your business is doing before it does it. Sit in the deal screening room. Read the brief. Understand the property types and structures coming down the pipeline before they hit your data infrastructure. "You'll learn a lot about what happens in industry," Schow said. "You'll learn a lot about where your data gaps are gonna be and how you need to fill them."

The firms that get there first will do more than report better. They'll act faster, identify opportunities earlier, and close deals with more confidence; not because they adopted the most sophisticated technology, but because they did the disciplined, often thankless work of building the foundation first.

What Altus is building toward

The challenges Schow and Shah describe are ones Altus has been working against from the inside. ARGUS plays an important role in how many institutional CRE firms model, value, and manage their assets, and the evolution from the legacy ARGUS solutions to ARGUS Intelligence reflects the same conviction that runs through this entire conversation: that data compounds in value when it is connected and organized around the asset instead of the model.

That evolution has come with a focus on the integration problem Schow describes. ARGUS Intelligence is built to connect to the broader data ecosystem a firm already operates in, whether that means property management systems, downstream reporting tools, or the kind of cross-system environment that has historically taken significant custom engineering to piece together.

On the AI side, ARGUS Assist is where the foundational work Shah and Schow describe starts to pay off. It draws on the benchmarks, assumptions, and historical models organized around each asset to surface answers based on the same data that powers the valuation, the portfolio review, and the investment committee presentation. The goal is the same one both executives articulate throughout: not more data, but the right data, in the right hands, at the right moment.

The firms that do the unglamorous work first are the ones best positioned to use it. The technology is ready. The question, as it has always been, is whether the discipline is in place.

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