Unpacking the Increasing Value and Importance of Structured Datasets

Data is no longer just an asset—it’s the backbone of modern business operations. By 2025, the global data sphere is projected to reach 175 zettabytes, with structured data playing a dominant role in automation, AI, and decision-making.

A McKinsey report reveals that companies leveraging structured datasets are 19% more profitable and 23 times more likely to acquire customers than their competitors. Meanwhile, poor data management costs businesses an average of $12.9 million annually, according to Gartner.

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In the real estate and Proptech sectors, structured data is transforming operations. AI-powered analytics have already reduced operational costs by 10–15% in large commercial portfolios, while smart buildings leveraging structured IoT data can cut energy consumption by up to 30%, according to the U.S. Department of Energy. With 80% of real estate companies increasing investments in cloud and AI-driven solutions, structured datasets are no longer optional—they’re essential.

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However, with increasing volumes of data come challenges in storage, governance, and infrastructure. Organizations must navigate cloud, on-prem, edge computing, and colocation to ensure data integrity, security, and scalability. As AI, blockchain, and digital twins continue to reshape Proptech, structured datasets will be the foundation of the industry's next evolution.

This article explores why structured datasets are critical, how they shape data capture, governance, and storage, and what businesses must do to future-proof their tech infrastructure in an increasingly data-driven world.

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The Growing Value of Structured Datasets

What Are Structured Datasets?

Structured data refers to information that is organized in a predefined format, typically stored in relational databases, spreadsheets, and enterprise resource planning (ERP) systems. Unlike unstructured data—such as emails, images, and videos—structured data is highly searchable, categorized, and easily analyzed.

According to Forrester Research, structured data accounts for 30% of all business data, yet it drives nearly 80% of analytics and decision-making processes. This is because structured datasets enable organizations to harness AI, improve automation, and enhance operational efficiencies.

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Key Benefits of Structured Datasets

Improved Data Accessibility and Decision-Making

73% of companies that use data-driven decision-making outperform competitors in revenue growth (MIT Sloan Management Review). Structured data allows organizations to query, sort, and analyze information in real-time, enabling faster, more informed business decisions.

Enhanced Data Quality and Accuracy

Bad data costs companies an average of $12.9 million annually due to inefficiencies, incorrect insights, and compliance failures (Gartner). Data validation techniques in structured datasets reduce errors by up to 40%, improving operational consistency (IBM).

Optimized AI and Machine Learning Applications

AI models trained on structured data achieve up to 25% higher accuracy compared to those trained on raw, unstructured data (Deloitte). In real estate, AI-powered property analytics based on structured data can improve property valuation models by 15–20% (PwC).

Better Compliance with Regulations

GDPR and CCPA require businesses to maintain well-structured, easily retrievable records of personal data. Companies using structured data management systems reduce compliance risks by 35%, avoiding hefty fines (International Association of Privacy Professionals).

Scalability and Integration

Structured data seamlessly integrates with CRM, ERP, and cloud analytics platforms, enabling organizations to scale operations without losing data integrity.

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Why This Matters for Proptech

The real estate industry increasingly relies on structured datasets for automating transactions, predictive analytics, and smart building management. Proptech investments reached $32 billion globally in 2023, with 68% of firms prioritizing structured data integration (CREtech).

AI-driven lease management systems using structured datasets reduce administrative costs by 30%, streamlining contract negotiations and compliance (JLL Research). Digital twins powered by structured data are expected to increase building efficiency by 35% while reducing maintenance costs by 20% (Harvard Real Estate Lab).

With structured datasets forming the backbone of automation, AI, and real estate innovations, companies must adopt a robust data capture and governance strategy to stay competitive.

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Data Capture Approach: Building a Robust Framework

1. Defining Data Collection Methods

To maximize the benefits of structured data, businesses need precise and automated data collection methods:

APIs and IoT Sensors:

50% of commercial real estate firms now use IoT sensors to optimize building performance (Deloitte). Smart meters collect structured data on energy usage, HVAC efficiency, and occupancy trends, allowing companies to reduce energy costs by 30% (U.S. Department of Energy).

CRM and ERP Integration:

90% of large enterprises integrate CRM and ERP systems for structured data capture (Gartner).

This enables real-time updates on tenant preferences, leasing terms, and maintenance needs.

Automated Data Entry Tools:

Companies using AI-powered data entry reduce human errors by 80% and processing time by 60% (Forbes).

2. Ensuring Data Integrity

Data Normalization: Standardizing formats to ensure consistency. Automated Data Validation: Eliminating duplicates and incorrect entries—businesses lose $3.1 trillion annually due to poor data quality (Harvard Business Review).

3. Balancing Granularity and Usability

60% of enterprises struggle with data overload, emphasizing the need for selective data collection (Forrester). Structured datasets must be granular enough for insights but concise enough to prevent data bloat.

Governance: Managing Data Responsibly

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1. Regulatory Compliance and Security

GDPR and CCPA require structured record-keeping, with fines reaching €20 million per violation (EU Commission). IBM estimates that businesses using encryption and access control reduce data breach risks by 40%.

2. Data Ownership and Accountability

80% of organizations have designated Data Stewards to ensure accuracy and governance (Gartner).

3. Standardization for Interoperability

OSCRE and ISO 8000 ensure global consistency in real estate data formats, improving system integrations by 25%.

Storage Needs: Choosing the Right Infrastructure

The choice between cloud, on-prem, edge computing, and colocation depends on:

Cloud Storage:

85% of enterprises now use cloud-based structured storage (IDC).

Scalable and cost-effective but requires strong cybersecurity.

On-Prem Storage:

Preferred by banks and real estate firms handling sensitive financial data.

Offers control but higher costs and maintenance needs.

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Edge Computing:

Ideal for smart buildings, reducing data latency by 60% in IoT applications.

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Colocation:

50% of global enterprises are shifting to hybrid colocation for high-performance, managed infrastructure (451 Research).

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Conclusion

With global data volumes surpassing 181 zettabytes annually by 2025, structured datasets will define the next era of Proptech innovation. Companies investing in structured data strategies, governance, and scalable storage will lead the market in efficiency, compliance, and AI-driven insights.

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