Large Building Insights Data

Large Building Insights Data
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At Nomad Data we help you find the right dataset to address these types of needs and more. Sign up today and describe your business use case and you'll be connected with data vendors from our nearly 3000 partners who can address your exact need.


Understanding the landscape of large business buildings, including their categorization, location, and usage, has historically been a complex and time-consuming process. Before the digital age, insights into such topics were primarily derived from manual surveys, public records, and anecdotal evidence. These methods were not only labor-intensive but also prone to inaccuracies and delays. For instance, real estate professionals and urban planners had to rely on physical site visits and paper-based documentation to gather information about large buildings and their purposes. This often meant that by the time data was collected and analyzed, it could already be outdated.

The advent of sensors, the internet, and connected devices has revolutionized the way we gather and analyze data. The proliferation of software and the trend towards digitizing every event have made it possible to collect vast amounts of data in real-time. This shift has significantly improved the accuracy and timeliness of insights into large business buildings, enabling stakeholders to make more informed decisions.

Data now plays a crucial role in understanding the characteristics and uses of large buildings. With real-time data, professionals can now track changes and trends as they happen, rather than waiting weeks or months for updates. This immediacy of information has transformed industries, allowing for more dynamic and responsive planning and development strategies.

However, the challenge remains in identifying and utilizing the right types of data to gain meaningful insights. This article will explore various categories of data that can shed light on large business buildings, their categorization, and usage. By examining the historical context, technological advancements, and current applications of these data types, we aim to provide a comprehensive understanding of how data can be leveraged to better understand large building landscapes.

Location Data

Location data has been instrumental in identifying and categorizing large business buildings. Historically, the availability of detailed location data was limited, often restricted to basic address information and perhaps some zoning details. The evolution of geospatial technology and the introduction of sophisticated mapping tools have dramatically expanded the scope and accuracy of location data.

Today, location data providers offer comprehensive datasets that include building footprints, geocoordinates, zoning/land use codes, and business occupancy information. This data can be filtered by square footage, allowing for the identification of large buildings and their uses, whether commercial or residential. The ability to customize delivery formats, from shapefiles to custom viewers, has made location data more accessible and useful for a wide range of applications.

Examples of Location Data Usage:

  • Urban Planning: City planners can use location data to identify areas with a high concentration of large commercial buildings, aiding in infrastructure development and zoning decisions.
  • Real Estate Development: Developers can leverage location data to find potential sites for new projects or to understand the competitive landscape of large buildings in a given area.
  • Market Analysis: Businesses can analyze location data to identify trends in the usage of large buildings, informing investment and expansion strategies.

Geolocation Data

Geolocation data takes location information a step further by providing detailed insights into the characteristics of specific buildings and their surroundings. This type of data includes not only the physical footprint of a building but also key attributes such as business name, NAICS codes, and operational details like open hours and contact information.

Advancements in data collection and processing have enabled geolocation data providers to offer highly accurate and up-to-date datasets. These datasets are invaluable for identifying business buildings, their square footage, and their specific uses. The inclusion of geospatial footprints and geo-coordinates enhances the precision of location-based analyses.

Examples of Geolocation Data Usage:

  • Asset Management: Property managers can use geolocation data to maintain accurate records of their assets, including detailed information about tenants and building usage.
  • Logistics and Supply Chain: Companies can optimize their logistics operations by analyzing geolocation data to identify strategically located warehouses and distribution centers.
  • Emergency Response Planning: Emergency services can utilize geolocation data to plan responses based on the types and locations of large buildings in an area.

Real Estate Data

Real estate data encompasses a wide range of information related to properties, including size, usage, and occupancy. The historical reliance on public records and manual data collection limited the scope and timeliness of real estate insights. The digital transformation of the real estate industry has led to the creation of comprehensive datasets that cover every aspect of commercial properties.

Real estate data providers now offer datasets that include detailed information on properties over a certain square footage, with varying levels of detail on usage availability. This data is crucial for understanding the landscape of large business buildings, enabling stakeholders to make informed decisions about development, investment, and management.

Examples of Real Estate Data Usage:

  • Investment Analysis: Investors can assess the potential of large commercial properties by analyzing real estate data, including tenant information and building usage.
  • Market Research: Market researchers can use real estate data to track trends in the development and utilization of large buildings, informing industry forecasts and reports.
  • Property Valuation: Appraisers can leverage real estate data to accurately value large commercial properties, taking into account factors like location, usage, and occupancy.


The importance of data in understanding large business buildings cannot be overstated. The evolution of data collection and analysis technologies has transformed the way we gather, process, and utilize information about large buildings and their uses. Access to diverse types of data, including location, geolocation, and real estate data, has empowered business professionals to gain deeper insights and make better-informed decisions.

As organizations become more data-driven, the ability to discover and leverage relevant data will be critical to success. The trend towards monetizing valuable data assets is also gaining momentum, with companies increasingly looking to sell datasets that provide insights into various aspects of large buildings. This opens up new opportunities for data-driven decision-making and strategic planning.

The future of data in understanding large business buildings is bright, with potential advancements in AI and machine learning poised to unlock even more value from existing datasets. The ability to analyze decades-old documents or modern government filings with AI could revolutionize the way we understand and categorize large buildings, leading to more efficient and effective use of space in urban environments.


Industries and roles that could benefit from access to data on large business buildings include investors, consultants, insurance companies, and market researchers. These stakeholders face challenges in accurately assessing and managing large properties, and data has the potential to transform their approaches.

For example, investors can use data to identify high-potential properties, consultants can advise clients on strategic development based on real-time insights, and insurance companies can assess risks more accurately. Market researchers can track trends and forecast developments in the commercial real estate market.

The integration of AI and machine learning technologies could further enhance the value of data, enabling more sophisticated analyses and predictions. As the availability and quality of data continue to improve, the potential for innovation and efficiency in managing large business buildings is limitless.

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