Simplifying Data Complexity in Real Estate: The Benefits of an Independent Data Layer

Learn about the benefits offered by an independent data layer (IDL) for CRE, workplace, and facilities management.

Simplifying Data Complexity in Real Estate: The Benefits of an Independent Data Layer
Photo by Austin Distel / Unsplash

Making sense of real estate, workplace, and building data can be an overwhelming task. From office space utilization metrics and lease administration data – to energy consumption, occupant comfort measures, and qualitative survey feedback – the sheer volume and variety of data presents challenges unique to commercial real estate.

When it comes to rationalizing your organization’s approach to data management, there’s good news and bad news.

The good news: the concept of an independent data layer (IDL) offers a modern approach to simplifying the aggregation, normalization, and interpretation of data at scale. Imagine a world where clear answers to your real estate or workplace data questions are just a few clicks away. That’s the promise of an independent data layer.  

The bad news: an IDL can’t solve problems in a vacuum. Like many new technology initiatives, an independent data layer approach requires internal alignment around data governance, data privacy, data security, team collaboration, and top-level objectives. It can be difficult to herd the cats, so to speak, but the potential payoffs are often well worth the effort.  

The rest of this post outlines some of the circumstances that make operational data analysis particularly difficult in the world of commercial real estate. It then covers some of the benefits offered by an independent data layer (IDL) approach.

Why is ‘building data’ so hard?

The answer to this question depends on whom you ask. Ask building engineers and they’ll share the complexities of building automation systems, arcane protocols, and the complexities of managing on-premise systems. Ask a real estate or workplace analyst and you might hear stories about contradictory spreadsheets, massive data joins, or manual cleanup efforts.

Perspectives vary by role, and many people hold strong opinions on the matter. With this noted, we acknowledge that the following generalizations may elicit spirited feedback from our readers. (If you have thoughts, critiques, or ideas, please let us know by emailing hq [at]!)

Data Hygiene & the Snowball Effect

The realities of managing a portfolio of buildings necessitates that some information be stored in a spreadsheet. Spreadsheets have a lot of positive qualities: they’re customizable, low-cost, and pretty much anyone in the professional world can access and understand them.

The downside: it’s perhaps the worst vehicle for storing data that should ideally be immutable, auditable, and programmatically accessible.

What happens when a data error or omission is propagated – in a spreadsheet or any data system – is what we call “the snowball effect.” Over time these errors result in poor data hygiene, resulting in people losing trust in the underlying data and resulting insights. (Garbage in = garbage out.)

Heterogeneous Technology Mix

Across a portfolio, it’s not uncommon to have multiple different technology vendors deployed to measure or manage the same thing. Examples include access control, wifi/networking, air quality, and occupancy sensing.

This heterogeneity is a reality for most organizations – one that impedes cohesive global analysis, benchmarking, and insights. Far too often, teams solve this situation through (1) brute force manual analysis, or (2) homegrown aggregation efforts that are inflexible, difficult to staff with engineering talent, and costly to maintain. An IDL represents a path to a more scalable and user-friendly approach.

Lack of Interoperability

In many cases, some of the systems and sources noted above don’t talk to one another. Result: combining and collating data insights across a given portfolio requires additional work or professional services.  

Cross-Functional Alignment

CRE and workplace leaders often need office attendance data to establish baselines around utilization and workplace policy trends. Both physical security teams and IT typically have this data set, in one form or another, but getting the infrastructure and team collaboration in place to share this data consistently takes effort, alignment on objective, and a willingness to dedicate staff resources to maintain the solution.

An independent data layer can reduce the amount of effort required by teams to produce results, yielding more efficient and productive operations and analysis.

Owner/Tenant Dynamics

In some cases, tenants and occupiers would benefit massively from seeing data metrics at the building level – such as energy consumption, lobby traffic, or elevator congestion. Our industry has yet to solve for transparent data sharing between owners/managers and tenants. While easy to solve from a data infrastructure standpoint, alignment between these parties represents a necessary precursor to make any of these insights possible.

Data Governance

Many companies have policies that preclude building systems from connecting to cloud-based systems and databases. In some cases, these data governance policies are well intended and appropriate – such as not exposing the approximate location and identity of an employee via MAC address tracking.

In other cases, these data approaches are too blunt. Why can’t anonymous badge data be aggregated, stored, and analyzed in the cloud? Organizations that adopt a balanced and nuanced view on risks and governance stand to benefit when it comes to generating insights that dramatically improve portfolio management and building efficiency.  

Qualitative vs. Quantitative

There is no substitute for qualitative feedback from employees and tenants. Surveys are the primary vehicle for soliciting this valuable input and, in some cases, teams struggle to join qualitative measures with “hard data” to uncover insights. An independent data layer on its own can’t solve this – but it can provide the framework and data services required to uncover new correlations that ultimately yield smarter workplace decisions.

What is an Independent Data Layer (IDL)?

Imagine a massive library where every book – from novels to encyclopedias – is mixed up. Finding specific information in this chaotic collection would be a nightmare. An IDL is like having a skilled librarian who knows exactly where each piece of information is stored, making it easy for you to find exactly what you need, when you need it.

In technical terms, an IDL is a dedicated digital layer in a software system designed to handle all things data – from storing, retrieving, to processing. It acts independently from other parts of the system, such as the user interface or the application logic, ensuring that data management is centralized, streamlined, and efficient.

Benefits of an Independent Data Layer

While not exhaustive, the following list outlines some of the benefits of an independent data layer in pratice.

Simplifies Data Management: With an IDL, all your data handling is in one place, making it easier to manage and maintain.

Improves Data Quality and Consistency: It ensures that data across various systems and platforms is standardized – “normalized” in data speak – reducing errors and discrepancies.

Enhances Decision Making: By providing a unified view of data, an IDL makes it easier to extract insights and make informed decisions.

Increases Flexibility and Scalability: An independent data layer allows for easier integration with new technologies and can scale up or down as your data needs change. When assessing an IDL provider, ensure that their data architecture supports metadata, an approach that offers greater flexibility and customizability at scale.

Boosts Security: Centralizing data management helps enforce uniform security policies and compliance standards, protecting sensitive information.

The Importance of IDLs in Workplace and Facilities Management

In the realm of commercial real estate and workplace management, the technology landscape is often fragmented. As noted above, different offices might use different systems for access control, temperature regulation, or space booking. Moreover, a significant amount of data still resides in spreadsheets or legacy systems, making it difficult to have a holistic view of operations and performance.

An independent data layer can serve as a unifying platform, bringing together data from IoT sensors, traditional databases, and manual inputs into a single, coherent system. This integration is crucial for several reasons:

  • Efficient Operations: Streamline workplace management tasks, from maintenance scheduling to space optimization.
  • Enhanced Employee/Tenant Experience: Use data insights to improve building amenities, safety, and comfort, thereby increasing tenant satisfaction and retention.
  • Sustainability Goals: Better data leads to smarter decisions around energy use, helping achieve sustainability targets.

By adopting an IDL, businesses in the commercial real estate sector can overcome the challenges posed by a disjointed technology ecosystem. It enables a more agile, data-driven approach to managing spaces that can adapt to the changing needs of tenants and the market.


As the corporate world becomes increasingly data-driven, the importance of efficient data management cannot be overstated. An Independent Data Layer offers a robust solution to the complexities of handling diverse data sources in the commercial real estate and facilities management sectors. By embracing this approach, professionals can unlock new levels of efficiency, insight, and tenant satisfaction, paving the way for a more connected, intelligent workplace.