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🔧 Investment / Complexity
1. How is your data typically stored and accessed?
A
In spreadsheets
B
In a business intelligence tool
C
Centralized in a warehouse with some data cleaning and structuring
D
Fully integrated with real-time pipelines and governed layers
2. Who currently works with your data?
A
No one or ad hoc
B
A part-time analyst
C
A small dedicated team
D
A cross-functional data org with embedded analysts
3. How are dashboards and reports built?
A
Manually in Excel
B
Built by request from analysts
C
Self-service dashboards available to most teams
D
Embedded insights and proactive alerting
4. What best describes your analytics tooling stack?
A
No formal tools
B
One or two point tools (e.g., Google Analytics, Excel)
C
A modern BI tool and data warehouse
D
A fully managed modern data stack (e.g., dbt, Snowflake, Fivetran) with CI/CD workflows, pull requests, and code reviews
5. Which analytics use cases are you currently pursuing?
A
None at the moment
B
Descriptive and diagnostic (e.g., dashboards tracking KPIs and targets)
C
Predictive and forecasting (e.g., sales forecasting or churn prediction)
D
Prescriptive and generative AI (e.g., optimization models, AI-generated insights or content)
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📈 Business Results / Impact
6. How often does analytics directly influence decision-making?
A
Rarely
B
Occasionally, when leadership asks
C
Frequently across multiple departments
D
Routinely – data drives most decisions org-wide
7. What’s the return on investment (ROI) from your analytics efforts?
A
Unknown
B
Less than 1x
C
Between 1x and 5x
D
Greater than 5x
8. What’s the turnaround time from question to insight?
A
Days or weeks
B
A few days
C
Within a day
D
Minutes or instantly, via dashboards or automation
9. How would you describe the trustworthiness of your data?
A
Garbage in, garbage out – we lack sufficient internal systems (e.g., we use a spreadsheet instead of a CRM) or have poor governance of those systems
B
Decent governance of business applications, but the way we extract insights is inconsistent, leading to multiple sources of truth or manual errors
C
Improving consistency – we’re aligning systems and standardizing how data is processed – we're building trust.
D
High fidelity – our data is well-governed, traceable, and trusted across the organization
10. How aligned is your company around KPIs and performance targets?
A
We lack consensus on KPIs
B
We’ve identified KPIs but don’t set clear targets
C
We have KPIs and targets, but don’t manage to them
D
We have KPIs and targets, and manage to them via mechanisms like MBRs and gap analysis
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