The Dodge–Viewpoint SmartMarket survey found that contractors were already moving jobsite data from paper and spreadsheets into software, mobile apps and cloud systems in 2017–2018, but still struggled with field-data quality and cross-project analysis. Respondents associated better data analysis with budget control, productivity and profitability while identifying information gathering as a major improvement priority.
What the Dodge–Viewpoint report measured
Building Design + Construction’s John Caulfield reported on Improving Performance with Project Data, a 40-page SmartMarket report released by Viewpoint and Dodge Data & Analytics. The online survey ran from August 14, 2017, through September 12, 2018. It included 187 respondents: 98 general contractors, 47 specialty trade contractors, 28 construction managers, nine design-build firms and four design-contracting firms. Thirty-seven percent of the companies reported annual revenue between $50 million and $250 million.
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These results describe the participating contractors during that survey period. They are not a 2026 industry benchmark or a census of every construction company.
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The study examined how firms gathered, stored, managed, secured, analyzed and reported five categories of information:
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- Project progress
- Man hours
- Productivity
- Safety
- Equipment management
The report treated data collection as a project-performance issue. Schedule, cost, payroll and man-hour information are useful only when field teams capture them consistently and project leaders can get the information in time to act.
Why contractors said jobsite data mattered
Survey respondents connected data analysis with measurable business outcomes. In the Dodge–Viewpoint survey, as reported by Building Design + Construction in 2019:
| Benefit named by respondents | Share of respondents | What the figure means |
|---|---|---|
| Improved ability to complete projects at or under budget | 53% | Contractors identifying budget performance as a benefit of data analysis |
| Greater productivity | 47% | Contractors identifying productivity as a benefit |
| Greater profitability | 46% | Contractors identifying profitability as a benefit |
The percentages show perceived benefits, not independently audited savings or productivity gains. They indicate why firms were investing attention in data: better information was expected to improve decisions about labor, production, cost and delivery.
Where respondents saw the biggest gaps
Field information gathering
Fifty-four percent said their jobsite information gathering needed to improve over the following three years. That concern goes to the source of the data. A dashboard cannot correct incomplete daily reports, late time entries, inconsistent production quantities or safety records that are recorded differently from one project to another.
Cross-project trend analysis
Forty-five percent said cross-project trend analysis needed improvement. Comparing projects requires common definitions, consistent coding and enough historical information to distinguish a recurring problem from a one-off event. Without those foundations, an organization may have large volumes of data but little ability to identify which practices affect schedule, labor or cost performance.
Progress already reported
More than three-fifths of respondents said their data gathering and analysis had improved during the preceding three years. The report therefore depicts an industry in transition rather than one starting from zero: companies were improving, yet many still considered the process inadequate for the next stage of project control.
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From paper forms to connected field workflows
Caulfield described a shift away from paper forms, printed reports and standalone spreadsheets toward digital tools and platforms, including cloud-based systems. Mobile apps and cameras were increasingly used in the field, reflecting a need to capture information where work occurred rather than re-entering it later in an office.
Digital collection does not automatically mean reliable collection. A useful field workflow must make the required entry clear, work under site conditions, preserve who recorded the information and when, and move the record to the people responsible for action. The report’s emphasis on more accurate field data is therefore as important as its emphasis on software.
Drones, sensors and wearables were described as gaining traction, not as universally adopted technologies. Their value depends on the question a contractor is trying to answer, the quality of the resulting measurements and the effort required to integrate them with project records.
How contractors stored and secured information
Storage choices in the survey
| Practice | Share of respondents | Interpretation |
|---|---|---|
| Digital information stored on on-premise servers | 65% | Respondents retaining data on infrastructure operated by their organizations |
| Use of third-party cloud providers | 37% | Respondents using externally hosted cloud services |
These categories could overlap; a contractor may use both internal servers and cloud services. The figures show a mixed environment rather than a complete replacement of one model by the other. On-premise storage can support direct control over infrastructure, while cloud hosting can make information easier to access across offices and jobsites. Either approach still requires permissions, backup, retention and recovery procedures.
Security controls reported
| Control | Share of respondents |
|---|---|
| Anti-malware software | 86% |
| Enterprise-grade firewalls | 78% |
| Policies governing mobile-device use | 56% |
The lower figure for mobile-device policies is notable because phones, tablets and cameras were becoming routine collection tools. A policy should define approved devices and applications, authentication, updates, lost-device reporting, photography rules, offline synchronization and the handling of project or personal information. The survey reports adoption of controls; it does not assess how effectively each control was configured or enforced.
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Advanced analytics remained relatively early-stage in the survey. More than one-fifth of respondents reported experience with predictive analysis or business intelligence. By comparison, 7% reported experience with artificial intelligence and 6% with machine learning.
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Many more understood these technologies without implementing them: 39% said they understood predictive analysis but had not implemented it, compared with 47% for artificial intelligence and 33% for machine learning. The gap between awareness and deployment suggests that the limiting factor was not simply terminology. Contractors also needed usable data, defined business questions, staff capability, implementation funding and confidence that the resulting analysis would support a real decision.
Jit Kee Chin, identified in the article as Suffolk Construction’s chief data officer, was presented as seeing useful data availability as a continuing barrier to artificial intelligence and significant potential for automated monitoring. That is a reported paraphrase of her perspective, not a direct quotation or a claim that automated monitoring had become standard practice.
The report’s practical planning sequence
For a contractor deciding what to improve, the report authors’ advice can be applied as a sequence. The accessible account presents three connected actions.
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- Start with the delivery problem. Determine where better information would improve project delivery. Examples include late labor reporting, unexplained production variance, unreliable equipment utilization or an inability to see a schedule risk across projects.
- Specify the required field data and its quality. Identify the data types needed to answer that question, then set minimum requirements for completeness, accuracy and timeliness. For example, a labor analysis may require every crew’s hours, cost code, date, project location and approval status before a daily record is considered usable.
- Build a focused implementation plan. Define the analysis method, financial and human-capital investment, timeframe, responsibilities and measurable success goals. Decide who enters data, who validates it, who owns the system, who interprets the results and what operational decision will change when the target is met.
The sequence prevents a common failure mode: buying a platform first and searching for a useful question afterward. Technology should follow the decision the business needs to make.
A practical framework for choosing a jobsite-data approach
The report supports comparing approaches by operational fit rather than by a simple paper-versus-cloud label.
| Decision axis | Questions to answer |
|---|---|
| Field access | Can supervisors enter and review records at the point of work, including areas with weak connectivity? |
| Accuracy | Are required fields, validation rules and approval steps strong enough to prevent obvious errors? |
| Timeliness | How quickly does a field record reach the project manager, payroll team or safety lead? |
| Completeness | Can the organization identify missing entries and follow up before a reporting deadline? |
| Trend analysis | Do projects use common codes and definitions so results can be compared? |
| Security | Are access rights, device policies, malware protection, firewalls, backups and recovery responsibilities defined? |
| Implementation effort | What training, integration work, staffing and ongoing administration will the approach require? |
| Measurement | Which indicators will show that the change improved delivery, rather than merely increasing data volume? |
Paper can remain appropriate for a controlled fallback or a location where devices cannot be used safely, but manual re-entry creates delay and transcription risk. Spreadsheets can support a small, clearly governed process, yet they become difficult to control when many projects use different columns, formulas and versions. Custom or commercial software can improve consistency and visibility, but only if teams adopt the workflow and the organization funds administration and support.
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What the case studies add
The article says the report includes a case study of Leander Construction’s use of cloud technology to enhance productivity and another on W. Soule & Co.’s use of data. These examples illustrate that implementation is organizational as well as technical: the value comes from connecting information to work practices and decisions.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThe article also includes a question-and-answer feature with Chin. Its context reinforces the distinction between collecting data and making data useful. Automated monitoring may eventually reduce manual observation, but it still depends on available, trustworthy information and a process for acting on what the system detects.
How a contractor can apply the findings without overbuilding
Choose one decision with a measurable baseline
Select a narrow problem such as daily labor-hour completeness, equipment idle time or production quantities. Record the current state before changing the workflow so the team can tell whether performance improved.
Define the minimum viable record
List the fields that are essential, who supplies each field, when it is due and who checks it. Keep optional fields separate from the minimum needed to make the decision.
Pilot on one project
Test the process with a representative project team. Check offline behavior, device usability, approval queues, coding consistency and the time required for supervisors to complete entries.
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Measure quality as well as speed
Track completeness, error rates, time from capture to availability and the number of decisions made with the information. Faster collection is not an improvement if the records are unreliable.
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Standardize before expanding analysis
Once the pilot works, document definitions and codes before comparing projects. Cross-project analytics built on inconsistent categories can create misleading trends.
Review security and retention
Assign an owner for user access, mobile-device rules, malware protection, firewalls, backups and offboarding. Decide how long records must be retained and how quickly operations must be restored after an outage.
A rugged tablet can be one optional field-entry or review device, but the survey did not test a particular model or establish that every contractor needs one. Device selection should follow verified requirements such as battery life, connectivity, operating-system support, camera needs, protective features and integration with the chosen workflow.
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Limits readers should keep in view
- The survey had 187 respondents and was conducted online from August 2017 to September 2018.
- Its sample included several contractor types but was not presented as a statistically complete count of the construction industry.
- The figures report what respondents said about benefits, practices and priorities; they do not establish causal effects or audited financial returns.
- Technology adoption has changed since the survey, so the percentages should not be used as current 2026 benchmarks.
- The article reports case studies and expert context, but it does not provide a universal implementation design for every company or project.
What the report ultimately says about jobsite data
The Dodge–Viewpoint findings point to a practical order of operations. Contractors saw data analysis as a route to better budget performance, productivity and profitability, yet many still needed more reliable field capture and stronger cross-project analysis. Their reported technology mix was transitional: paper and spreadsheets were giving way to digital and mobile workflows, while on-premise and cloud storage operated side by side. Security controls were common, but mobile-device policies were less widespread than anti-malware and firewall protection.
The durable lesson is to begin with a project-delivery decision, define the minimum trustworthy data needed to make it, and then fund the people, technology and controls that can deliver that information consistently. Analytics, artificial intelligence and automated monitoring can follow when the underlying records are available and dependable.
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