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Engineering and construction (EC) firms can compete more effectively for infrastructure work when they turn data from jobsites, designs, financial systems, equipment and external sources into decisions. The essential capability is not collecting more records; it is connecting information outside traditional enterprise-resource-planning (ERP) systems to project selection, estimating, delivery, margin control, safety and workforce planning.
This article explains the case made in a December 18, 2018 Building Design + Construction article summarizing Deloitte Consulting’s 2019 Engineering and Construction Industry Outlook, then translates that thesis into a practical operating framework. Its market figures and forecasts are historical and should not be read as current conditions.
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Why analytics became an infrastructure-competition issue
Infrastructure projects are becoming more connected, data-intensive and difficult to manage. Owners increasingly expect teams to coordinate design, construction, operations, sustainability, safety and long-term asset performance. Firms that can interpret those information flows have a better basis for deciding which opportunities to pursue, pricing risk, coordinating delivery and demonstrating value after handover.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThe 2018 Building Design + Construction account of Deloitte’s 2019 outlook argued that engineering and construction companies would need to organize and analyze data generated beyond their traditional ERP platforms. That includes information from building-information-modeling (BIM) systems, project controls, sensors, equipment, drones, connected infrastructure, documents, field observations and external market sources. Analytics supplies the layer that turns those disparate signals into actions.
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The point is strategic: digital capability can determine whether a firm is credible for technology-intensive infrastructure work. It does not mean every contractor needs every emerging tool. It means leadership must know which decisions matter, which data supports them, and how to make the resulting insight usable for project teams.
What the 2018 outlook actually reported
The following figures belong to the period covered by the December 18, 2018 article. They are included for historical context, with the attribution and qualification given in that article.
| Figure | How it was presented | Qualification |
|---|---|---|
| Approximately 5% U.S. EC-industry growth in 2018 | Deloitte Consulting estimate reported by Building Design + Construction | A 2018 estimate, not a current growth rate |
| At least 344 mergers and acquisitions valued above $20 billion in 2018 | Activity reported in the December 18, 2018 article | The indexed account does not identify a primary deal-count publisher |
| $158 billion in global smart-city spending by 2022 | IDC forecast reported by the article | A forecast made before 2022, not a current spending figure |
| About 7.2 million U.S. construction workers | Workforce figure reported by the article | The article’s comparison describing both a post-recession high and an 18-year low is internally unclear and should not be repeated without the original context |
Deloitte principal Michelle Meisels, identified as the outlook’s author and Engineering and Construction Leader, was reported as predicting that labor shortages would continue through 2019. That was a dated prediction, not a present-day forecast. The article also cited an alliance between AECOM and Arrow Electronics as an example of scale meeting digital capability; the alliance’s status and scope should not be inferred today from that historical mention.
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Where analytics creates value for an EC firm
Choose projects with evidence, not just volume
Business-development teams can combine owner requirements, geography, financing signals, schedule constraints, sector experience, labor availability and historical project performance before committing pursuit resources. A simple opportunity model can score strategic fit, expected margin, delivery complexity, contractual exposure and the firm’s ability to staff the work.
The useful output is not an automated yes-or-no decision. It is a documented view of assumptions: which conditions must hold for the project to succeed, which risks are uncertain, and what additional information would change the recommendation. That makes go/no-go reviews more consistent and gives executives a way to compare opportunities that otherwise look attractive for different reasons.
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Protect shrinking margins earlier
Construction margins can erode through small, repeated variances: unproductive hours, material escalation, rework, late design information, change-order delays, equipment downtime or subcontractor performance. Analytics can compare the estimate, budget, commitments, progress quantities, cost-to-complete and cash position in one operating view.
Early-warning rules should focus on leading indicators rather than waiting for a monthly loss. Examples include productivity below the planned installation rate, committed cost rising faster than earned value, unresolved requests for information accumulating in a trade package, or forecast final cost moving beyond an approved tolerance. A dashboard is valuable only when a named manager owns the response and the system records the corrective action.
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Complex programs generate dependencies that are difficult to see in isolated spreadsheets. Linking schedule activities to design packages, procurement status, labor curves, inspections, safety observations and cost codes allows teams to identify bottlenecks before they affect the critical path.
Scenario analysis is particularly useful. A project team can test the effect of resequencing work, changing a material, adding a shift, reallocating equipment or accepting a design alternative. The model should show assumptions and confidence ranges; a single precise-looking forecast can hide weak or incomplete data.
Make better location and materials decisions
Infrastructure work depends on conditions outside the project boundary. Firms may need to evaluate access, logistics, climate exposure, utility capacity, permitting constraints, workforce availability and local supply chains. Combining external location data with internal productivity and cost history can improve early feasibility studies.
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Material decisions can be analyzed against installed cost, lead time, availability, maintenance implications, embodied impacts where required by the owner, and the risk of substitution. Analytics does not replace engineering judgment or specifications. It makes the trade-offs visible and records why a choice was made.
Support smart-building and smart-city delivery
Connected assets produce information during construction and operation. IoT devices, building controls, occupancy systems, utility meters and infrastructure sensors can support commissioning, performance monitoring and maintenance planning. An EC firm that understands the data architecture can contribute beyond physical installation by defining useful data structures, handover requirements and operating interfaces.
The opportunity described in the 2018 outlook was tied to urban growth and infrastructure demand, including IDC’s then-forecast $158 billion in global smart-city spending by 2022. That forecast is historical. The enduring lesson is that connected-project capability can be part of the value proposition when owners are procuring outcomes rather than isolated construction packages.
How connected construction generates usable data
BIM as a shared project information model
BIM can connect geometry and specifications with quantities, sequencing, costs, asset identifiers and handover information. Its value depends on agreed naming, ownership, revision control and exchange rules. A model that cannot be matched to field records or operational systems becomes another silo.
Robotic process automation for repetitive administration
Robotic process automation can handle rule-based tasks such as moving approved data between systems, checking fields, assembling recurring reports or notifying a user when a condition is met. It is most effective when the underlying process is stable. Automating a broken approval chain simply moves errors faster, so teams should document exceptions and audit logs before deployment.
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Drones and site mapping
Drones can capture frequent aerial imagery, topographic information and progress evidence. A construction site mapping drone is one optional data-capture category, not a universal requirement. Commercial surveying, airspace, privacy, insurance, pilot qualification, site safety and data-processing requirements vary by jurisdiction and project. Equipment suitability must be evaluated with current regulations and the owner’s specification; the 2018 outlook did not compare models or establish that any particular drone is appropriate.
Wearables and augmented reality
Wearables can provide location, environmental or physiological data when workers consent and the program has clear safety and privacy boundaries. Augmented-reality tools can overlay design information or work instructions in the field. Both technologies require reliable connectivity, training and a process for acting on alerts. Collecting sensitive data without governance can damage trust and adoption.
Interfaces for decisions and scenarios
Field supervisors, project managers and executives need different views of the same information. A useful interface lets each role see the current status, the source of a metric, the threshold that triggered attention and the next action. Scenario tools should allow users to change a small number of meaningful variables and understand the consequences rather than presenting an opaque score.
A practical implementation sequence
- Define the decisions. Select two or three decisions with measurable business impact, such as bid selection, cost-to-complete forecasting, equipment utilization or design-change response. Write down who decides, how often and what evidence is currently missing.
- Map the data beyond ERP. Inventory BIM models, estimating files, schedules, project-management records, field apps, equipment logs, drone imagery, quality records, safety observations and external datasets. Record the owner, update frequency, format, access rights and known quality problems.
- Choose a controlled pilot. Use a project or portfolio with a committed sponsor, representative complexity and a team willing to change its routine. Set a baseline before introducing the dashboard or model so improvement can be measured.
- Standardize definitions. Agree on cost codes, activity IDs, location breakdowns, asset identifiers, revision rules and status meanings. Establish which system is authoritative for each field and how corrections are approved.
- Build the smallest useful workflow. Start with a reliable feed and an action: for example, a forecast variance that creates an assigned review, or a design-package status that alerts procurement. Avoid a large “single pane of glass” that no one uses.
- Validate with practitioners. Have estimators, superintendents, engineers, commercial managers and safety leaders test whether the outputs reflect field reality. Investigate disagreements instead of hiding them; they often reveal inconsistent definitions or missing context.
- Measure adoption and outcomes. Track response time, forecast accuracy, rework, unresolved issues, reporting effort, safety follow-up and margin variance. Also measure whether users trust the data and complete the associated action.
- Scale with governance. Create access controls, retention rules, cybersecurity responsibilities, model-version management and a process for retiring unused metrics. Replicate only workflows that produced a demonstrated benefit.
People and capability are part of the system
Technology cannot compensate for a shortage of people who understand both construction and data. The outlook connected digital transformation with employer brand, apprenticeships, technical schools and training. That combination remains practical: firms need field leaders who can interpret analytics, analysts who understand production realities, and managers who can change workflows without undermining safety or quality.
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- Build blended roles. Pair a data specialist with a project practitioner so models reflect actual sequencing, contracts and site conditions.
- Train for interpretation. Teach teams how a metric is calculated, what its limitations are and which action follows a threshold.
- Use apprenticeships and technical-school partnerships. Structured pathways can develop surveying, BIM, controls, data-management and field-technology skills while expanding the recruiting pipeline.
- Protect professional judgment. Analytics should inform engineering, commercial and safety decisions, not remove accountability from licensed or experienced professionals.
Common failure modes and fixes
Collecting data without a decision owner
Symptom: teams produce attractive dashboards but meetings do not change. Fix: assign an owner to each metric, define the trigger and document the required response.
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Trying to integrate everything at once
Symptom: a long implementation stalls while interfaces and definitions multiply. Fix: begin with one decision, a limited data set and a measurable baseline.
Confusing more data with better data
Symptom: contradictory progress, cost or quantity figures appear in different systems. Fix: establish authoritative sources, validation checks and revision responsibility before expanding collection.
Ignoring contracts and privacy
Symptom: teams capture worker, location or owner data without clear rights, retention or permitted use. Fix: involve legal, commercial, information-security and workforce representatives at design time, not after deployment.
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Symptom: software is installed but field teams continue using informal workarounds. Fix: document the process, incentives, training and support required; then assess whether a technology category fits.
How to judge whether an analytics approach fits
When evaluating a platform, internal build or service, use criteria tied to the firm’s work rather than a generic feature checklist:
- Integration: Can it exchange data with existing ERP, BIM, estimating, scheduling and field systems without creating duplicate records?
- Project-level reporting: Can managers move from portfolio trends to the contract, location, activity or cost code that requires action?
- Implementation burden: What standardization, migration, training and ongoing administration are required?
- Scale and complexity: Does it support the firm’s project sizes, joint ventures, subcontractor relationships, geographies and security obligations?
- Workforce fit: Can supervisors and craft professionals use it in the field, with the connectivity and devices available on the project?
- Auditability: Can users see where a number came from, when it changed and who approved an exception?
These axes are decision criteria, not a ranking of products. The 2018 outlook did not evaluate vendors or establish a preferred technology stack.
A defensible competitive position
An EC firm can present analytics as a delivery capability when it links the technology to owner outcomes: earlier risk visibility, transparent cost and schedule control, better coordination, safer work, reliable asset information and more informed lifecycle decisions. The strongest proposal describes the data sources, governance, people and actions—not merely a list of devices or software categories.
The historical outlook’s central warning is therefore still useful as a management test: if important project information remains scattered outside the systems leaders use to make decisions, digital investment has not yet produced operational capability. Firms that define the decisions first, connect the relevant data and train people to act on it are better positioned to pursue complex infrastructure work without treating analytics as a technology side project.
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