Quiz Test

Step 1 of 8

12%

How is your data currently stored and managed?(Required)

Mostly on paper/manual processes (files, binders, carbon copies)

In spreadsheets or separate apps with no integration

In a central system (ERP, job management software, or data warehouse)

In multiple systems that are partially integrated

In fully automated, real-time AI-enabled systems

How are business decisions typically made in your organization?(Required)

Gut feel or individual experience

Basic reports (Excel, ad hoc exports)

Dashboards and standardized KPIs

Predictive models (forecasting risks, costs, or delays)

Automated insights and AI-driven recommendations

How much time does it take to prepare your monthly reports?(Required)

Weeks of manual effort

Several days of combining data

Hours, with partial automation

Minutes, with real-time dashboards available

Instant, fully automated and always up to date

How consistent and trusted is your data across teams?(Required)

Completely inconsistent; everyone tracks things their own way

Somewhat consistent, but still siloed by department

Mostly consistent, with shared definitions across some teams

Consistent and reliable across most of the business

Fully standardized and governed; trusted as the single source of truth

What challenges do you face with your current approach to data?(Required)

Lost paperwork, duplicate entry, or chasing missing info

Siloed systems and disconnected tools

Inconsistent or unreliable reporting

Lack of advanced analytics or predictive capability

Difficulty scaling or managing AI and governance

What would you most like to achieve with better access to and control over your data?(Required)

Faster, more accurate reporting

Clearer visibility into project profitability and performance

Predictive insights to anticipate risks and delays

Automation to reduce admin overhead and errors

AI-driven innovation and competitive advantage

What is the primary obstacle preventing your business from implementing the next step toward data-driven excellence?(Required)

Budget or resource limitations

Lack of internal data expertise

Resistance to change or adoption by staff

Complexity of current systems or legacy processes

Governance, compliance, or scaling AI responsibly

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Company(Required)

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Data Maturity Self-Assessment