Definition, examples & importance of data completeness

In the age of data-driven decision-making, ensuring the completeness of data has never been more crucial. Incomplete or patchy data leaves blind spots that can undermine deals, valuations, investments, and strategic initiatives. To extract meaningful insights from data and ultimately drive success, commercial real estate (CRE) firms require comprehensive data that provides a full picture.

What is data completeness?

Data completeness refers to the extent to which the data required is present within a data set. For data to be useful for analysis, it must have sufficient completeness – all necessary data fields should be populated without significant gaps. Incomplete data can lack key information, limiting its utility.

Examples of incomplete data and its consequences

In CRE, an incomplete data set may miss key property details like square footage, units, or occupancy rates. Without this information, analysts cannot accurately evaluate property performance and value. Trying to forecast demand or make investment decisions based on incomplete data leads to poor, misinformed choices. It can also lead to inaccurate building information modeling, which can cause delays and issues during construction and maintenance.

Navigating data: accuracy, quality, and completeness

To collect valuable data, it needs accuracy, quality, and completeness. These three characteristics are interconnected and often feed into one another, but each has its own distinct importance:

• Data accuracy refers to the extent to which data is correct, precise, and error-free. Accurate data precisely reflects the real-world parameters it represents.
• Data quality refers to how useful, reliable, and valuable data is for business purposes. High-quality data meets consistency, integrity, and accessibility requirements.
• Data completeness refers to whether all necessary data is present. Complete data has all fields populated without gaps that would limit analysis.

How do they differ and interrelate?

Data requires accuracy, quality, and completeness for a full and accurate picture of a process or system. Deficiencies in any of these areas undermine the usefulness of data. Incomplete or low-quality data leads to overlooking crucial factors, even if accurate. Inaccurate data misguides decisions regardless of completeness.

For example, a dataset might be complete (every field is filled out) but inaccurate (the information is incorrect). Conversely, data might be accurate (every entry is correct) but incomplete (some fields are missing). And even if data is both accurate and complete, it might still not meet the quality standards if it is outdated or inconsistent.

For CRE firms, flawed data can misinform deals, investments, valuations, and strategy. On the operational side, it can interrupt maintenance workflows through unnecessary alerts and failing equipment going unnoticed. Excellence in accuracy, quality, and completeness is imperative for data to deliver robust insights that guide growth.

How complete is your data set?

It is crucial to assess and measure the completeness of data sets before relying on them for analytics and decisions. By measuring completeness against analytical and business needs, CRE firms can check that data can support robust analytics. This assessment is an important part of developing high-quality, trustworthy data sets.

Measuring and determining data completeness

To assess completeness, data is tested against business requirements and use cases. What fields are necessary for the intended analysis? Are those fields consistently filled across records? For commercial real estate, key metrics around properties, locations, financials, tenancy, and valuations may be evaluated. Statistical methods can check for blank fields and gaps across data sets.

The evolving standards for data quality

As CRE firms increasingly rely on data-driven strategies, the standards for data quality also keep becoming stricter. This increasing strictness allows CRE professionals to rely on the insights they gather from data.

But why are standards becoming more stringent?

1. Complex decision-making: With decisions spanning from investment strategies to property valuations, the need for comprehensive and accurate data is paramount. Data lacking accuracy, quality, or completeness can result in costly mistakes.

2. Technological advancements: As technologies such as AI become more advanced (with the possibility of reshaping the CRE landscape), so does the capacity to collect, analyze, and interpret larger volumes of data quickly. With these advancements come heightened expectations for data quality.

3. Regulatory and compliance pressures: There is a growing emphasis on maintaining high-quality data to ensure compliance and avoid potential legal repercussions from regulatory bodies.

4. Stakeholder expectations: Many investors, partners, and clients demand transparency and accuracy in statistical reporting. Meeting these expectations is important for maintaining trust and credibility within the industry.

How can CRE firms stay ahead?

1. Invest in advanced analytics tools: Modern analytics tools can automatically detect inconsistencies, gaps, or anomalies in data sets, allowing firms to address issues before they escalate into costly errors.

2. Regular data audits: This involves periodically reviewing and cleaning data to ensure it remains up-to-date, accurate, and complete.

3. Ongoing training: Make sure team members are regularly educated on the latest industry standards and best practices for data collection and analysis.

4. Collaborate with industry bodies: Actively participating in industry forums, events, and associations not only keeps CRE firms updated on evolving standards but also allows them to contribute to shaping those standards.

5. Adopt a proactive approach: Instead of reacting to changes in data standards, forward-thinking businesses should aim to set the benchmarks, establishing themselves as industry leaders in data quality.

Why data completeness is so crucial in CRE analytics

Commercial real estate professionals use data analytics to guide deals, pricing, valuations, development, and investments. It can also prove compliance with certifications such as the WELL building standard. But, without robust, complete data, these applications are severely limited. Important factors get overlooked if underlying data is deficient.

How incomplete data can hinder business growth

Limited information restricts strategic decisions. Trying to operate, analyze, and grow with incomplete data leaves blind spots resulting in missed opportunities. Even basic tasks like updating listings and inventory are difficult without comprehensive data. In CRE, incomplete data directly impacts the bottom line.

Make full use of your real-time data insights with Proptech

By integrating disparate data sources across Proptech solutions, you can enable comprehensive, real-time analytics. ProptechOS acts as the operating system to unify all of your Proptech solutions, data sources, and analytics software. This maintains completeness across systems to elevate the value of data, turning it into actionable insights. You can try ProptechOS for free today to see how real-time data insights can improve your decision-making.