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Deep data can help architects, engineers, contractors, developers and public agencies make better building decisions by connecting project choices to evidence about places, markets, infrastructure and public records. The opportunity is practical rather than magical: useful datasets can clarify where to build, what a project may encounter, how a contract was awarded and which assumptions deserve scrutiny. The 2015 article that frames this discussion describes possible gains in speed, cost control and market entry, but it reports no measured savings or guaranteed outcomes.
This guide explains what “deep data” meant in that historical discussion, where it can influence a project, how to judge a dataset before relying on it, and how to introduce data analysis without turning a construction workflow into an unfocused technology project.
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Deep data means finding, combining and interpreting datasets that are relevant to a real decision. It is not simply collecting the largest possible volume of information, nor using “big data” as a slogan. A small, well-maintained dataset about zoning, flood exposure, transit access or comparable leases may be more useful than millions of records with unclear dates or provenance.
The phrase was used in a June 14, 2015 Building Design + Construction article by C.C. Sullivan. The article presented data analysis as a service that building teams could offer clients. Its examples are historical and should not be read as confirmation that every named service, government portal or data feed still exists under the same terms.
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In practical terms, a deep-data assignment has four parts:
- Define the decision. For example: select a site, estimate demand, sequence construction, or evaluate a public contract.
- Identify relevant evidence. This may include location data, infrastructure plans, commercial real-estate information, permits, contracts or performance criteria.
- Check quality and limits. Record the date, geographic coverage, update cycle, missing fields, licensing terms and likely bias.
- Translate findings into an action. A map, risk register, option comparison or revised scope is more useful than an attractive dashboard with no decision attached.
Why can better information lead to better buildings?
Every project contains decisions made under uncertainty. A developer must judge whether a market can support a proposed use. A designer must understand how people reach a site and how surrounding infrastructure may change. A contractor must plan around approvals, procurement and the obligations embedded in a contract. Data cannot remove uncertainty, but it can expose assumptions early enough to test or change them.
Sullivan’s article links building decisions to wider infrastructure choices, including highways and mass transit. A station extension, road redesign or new public facility can alter access, construction logistics, land value and demand. Looking only at the parcel may therefore miss the forces that determine whether a project works.
The article describes possible benefits such as delivering projects faster, saving money or entering a geographic market. Those are proposed opportunities, not reported performance figures. No named statistic in the source proves a specific percentage improvement, and a responsible business case should model its own costs, risks and alternatives.
Where can data influence the project lifecycle?
Open data systems can provide information before, during and after a project. The value changes at each stage, so the team should define the decision and the required level of precision before gathering records.
Before investment: location, demand and context
Early analysis can combine parcel and zoning information with population or employment patterns, transport access, environmental constraints, utility capacity and planned public works. The historical article quotes architect and academic Randy Deutsch on the potential for designers and urban planners to benefit from analyzing personal location data. In this context, “personal” information must be handled lawfully and ethically; aggregated or anonymized mobility patterns may be appropriate, while identifiable movement histories may create privacy and security risks.
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A site-screening exercise should ask:
- What geographic area does each dataset actually cover, and is it current enough for the decision?
- Do the records describe existing conditions, approved projects or merely proposals?
- Can the team distinguish a temporary construction disruption from a permanent access problem?
- Are the data granular enough to compare candidate sites without implying false precision?
Infrastructure analysis is especially important for projects whose success depends on access. A planned transit connection may be delayed, a highway interchange may change traffic patterns, or a municipal investment may shift development pressure. These possibilities should be represented as scenarios, not treated as certain facts.
During feasibility and design: test assumptions
Once a project concept exists, data can help compare program options and challenge the assumptions behind them. Market records may inform a discussion of competing space, vacancy or rents; planning records may reveal approval patterns; and previous project information may help identify recurring coordination or procurement issues. The output should be a transparent set of assumptions with sources and dates, not a single unexplained score.
Design teams can also use data to communicate trade-offs. For example, a larger floor plate might improve operational efficiency but increase structural, mechanical or code complexity. A site with excellent transit access might have difficult staging conditions. A decision log can show which evidence supports each option and which uncertainties remain unresolved.
During procurement and construction: understand obligations and risk
Public records can be valuable when they include building permits, design and construction contracts, award rationales and the performance criteria used to assess them. The article reports Chris Palmisano, then a Google senior account manager, describing state agencies opening systems so the public and businesses could inspect this kind of information. That statement reflects the article’s 2015 context; access, completeness, retention and reuse rights vary by jurisdiction and must be checked directly.
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- What approval sequence is typical for comparable work?
- Which contract requirements are mandatory, and which are project-specific preferences?
- How were previous awards justified, and what performance criteria were applied?
- Are change orders, delays or closeout documents available, or is the record incomplete?
Such evidence can improve bid preparation and coordination, but it does not replace the contract, governing code or advice from qualified professionals. A public record may omit informal decisions, contain errors or describe a different scope.
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After completion: preserve information that remains useful
Post-completion data can support operations, maintenance, future alterations and public accountability. Open data systems are one route, but the project team should also establish an internal handover standard: naming conventions, responsible owners, source dates, permitted uses and a process for correcting errors. If information cannot be understood or updated after the original team leaves, its long-term value is limited.
What historical examples illustrate the idea?
The 2015 article gives three main examples.
Location and planning data
Randy Deutsch’s example connects personal location information with design and urban-planning decisions. The broader lesson is to examine how people actually move through a place, while respecting privacy and avoiding the assumption that a dataset represents every user. Mobility data may be biased toward people carrying particular devices, using particular services or traveling at particular times.
Commercial real-estate information
Jake Seid, identified in the article as president of Auction.com, describes an open and transparent real-estate marketplace as a way to level the playing field. His 2015 statement was: “By creating an open, transparent marketplace for real estate and leveling the playing field by using data, actually sellers win, buyers win, brokers win. It’s a net positive for everybody who participates in the transaction.” The quotation expresses a viewpoint about the value of transparency, not an independently measured result. The article also names RealMassive as a private-sector commercial-real-estate information provider; its current status and terms are not established by the available evidence.
Public-sector records
The article’s public-records example covers permits, design and construction contracts, reasons for awards and performance criteria. These records can improve visibility into how public work is planned and procured, but the usefulness of a portal depends on what the agency publishes, how quickly it updates, whether documents are machine-readable and what reuse restrictions apply.
How should a team evaluate a dataset before using it?
Use the following review before a dataset enters a cost plan, site recommendation, schedule or client presentation. The criteria are practical extensions of the article’s emphasis on broad datasets and project use.
| Check | Questions to ask | Why it matters |
|---|---|---|
| Geographic coverage | Does it cover the site, its neighborhood and the comparison areas? | A national dataset may be too coarse for a parcel-level decision. |
| Update frequency | When was it last updated, and is the date visible for each record? | Old permits, prices or transit plans can produce a false picture of current conditions. |
| Completeness and provenance | Who collected it, by what method, and which fields are missing? | Unknown origins make errors difficult to detect or explain. |
| Access and permitted use | Is access open, paid, restricted or revocable? May the team redistribute derived outputs? | Licensing and privacy obligations can limit client deliverables. |
| Decision-level granularity | Does the resolution match the question, without implying unsupported precision? | Street-level detail cannot automatically predict building-level demand. |
| Integration effort | Can the information be exported, joined and maintained with existing workflows? | A theoretically valuable feed may cost more to clean than the decision warrants. |
Keep a short data register for every source. Record the owner, retrieval date, geographic extent, definitions, known exclusions, license, transformation steps and the person responsible for review. This makes later updates and client questions manageable.
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How can a construction business turn analysis into a repeatable service?
Start with a narrowly defined client decision rather than a general promise to “use big data.” Universities, commercial developers, regional health providers and municipal or regional planning departments are among the client types named in the article. Each has different decisions, permissions and success criteria.
- Write the decision brief. State the choice, deadline, alternatives, acceptable uncertainty and the consequence of being wrong.
- Map stakeholders and rights. Identify the client, public agencies, data owners, affected communities and anyone whose personal information could be involved.
- Build a source inventory. Separate authoritative records from estimates, commentary and proposed projects. Capture dates and access conditions.
- Clean and document the data. Standardize addresses, units, geographic boundaries and terminology. Preserve the original files so transformations can be audited.
- Analyze alternatives. Use maps, scenario ranges, sensitivity checks and a written explanation of assumptions. Avoid presenting a model output as a fact.
- Review with subject-matter experts. A planner, estimator, engineer, lawyer or community representative may identify context that the records miss.
- Deliver an action-oriented result. This might be a site shortlist, a risk register, a procurement recommendation, a revised phasing plan or a list of questions for the authority having jurisdiction.
- Plan for refresh and closeout. Specify when the analysis expires, what triggers an update and which project records must be handed over.
What can go wrong with deep-data projects?
The most common failure is confusing availability with usefulness. A large public portal may have gaps, inconsistent definitions or a long publication delay. Commercial data may be easier to use but may restrict redistribution or change its methodology without notice.
Other risks include:
- False precision: a finely colored map can make uncertain estimates look exact.
- Selection bias: mobility, market or survey data may exclude groups who are less visible in the source.
- Boundary errors: administrative areas, postal addresses and transport zones may not align with the project boundary.
- Version drift: a later refresh can change historic values, making an old recommendation hard to reproduce.
- Privacy harm: combining supposedly anonymous location records can make individuals identifiable.
- Automation without accountability: a model may rank options, but a named professional still needs to explain and approve the decision.
Mitigate these risks by showing uncertainty, retaining source snapshots, limiting access to sensitive information, documenting assumptions and giving decision-makers a way to challenge the result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What did the 2015 article establish—and what remains uncertain?
The article established a useful proposition: building teams can add value by finding and applying information that improves project, investment, planning and market decisions. It identified location information, commercial real-estate information and public records as relevant categories, and it placed open data systems across the period before, during and after construction.
It did not establish a universal business case, a quantified return, a current data marketplace or continuing access to any named agency system. The article was available through a third-party mirror, while a 2022 University of Illinois CV for Randy Deutsch corroborates the article’s title, publication month and attribution. That CV does not independently verify every detail in the mirrored text.
The article also named Deutsch’s Data-Driven Design and Construction: 25 Strategies for Capturing, Analyzing and Applying Building Data as a forthcoming book in 2015. Readers should confirm the current edition and availability before treating it as a present-day reference.
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What is the practical takeaway for project leaders?
Use deep data to make a specific decision more transparent, not to decorate a project with technology. Define the question, choose sources whose coverage and date fit that question, document uncertainty, protect personal information and connect the result to an action that the project team can own. When those disciplines are in place, data analysis can strengthen site selection, design choices, procurement review and long-term information management without promising outcomes the evidence cannot support.
Frequently Asked Questions
How can a small contractor start using project data?
Choose one recurring decision, such as bid-risk review or permit planning, and create a simple source register for it. Start with records the business can legally access and maintain, then expand only when the first workflow produces a clear operational benefit.
Is public building data always reliable?
No. Publication practices differ by jurisdiction, and records may be incomplete, delayed, incorrectly classified or unavailable in machine-readable form. Treat a public portal as evidence to verify, not as an automatic substitute for the governing permit, contract or code.
How should teams handle personal location data?
Use the least detailed information that can answer the planning question, prefer aggregation or anonymization, restrict access and document the legal basis for processing. A privacy review should occur before combining datasets that could reveal an individual’s movements.
Does deep data replace professional judgment in design and construction?
No. Data can reveal patterns and test assumptions, but professionals must interpret context, code requirements, constructability, community impacts and contractual obligations. A documented human review should remain part of the approval path.
What should be included in a client-facing data report?
Include the decision being supported, source dates and owners, definitions, limitations, uncertainty, alternatives considered and the recommended action. Also state when the analysis should be refreshed so the client does not treat a time-limited snapshot as permanent truth.
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