Quiz

Step 1/3

Please confirm your name & email

Enter your name*

Enter your email*

Cancel Next

🔧 Investment / Complexity

1. How is your data typically stored and accessed?

In spreadsheets

In a business intelligence tool

Centralized in a warehouse with some data cleaning and structuring

Fully integrated with real-time pipelines and governed layers

2. Who currently works with your data?

No one or ad hoc

A part-time analyst

A small dedicated team

A cross-functional data org with embedded analysts

3. How are dashboards and reports built?

Manually in Excel

Built by request from analysts

Self-service dashboards available to most teams

Embedded insights and proactive alerting

4. What best describes your analytics tooling stack?

No formal tools

One or two point tools (e.g., Google Analytics, Excel)

A modern BI tool and data warehouse

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?

None at the moment

Descriptive and diagnostic (e.g., dashboards tracking KPIs and targets)

Predictive and forecasting (e.g., sales forecasting or churn prediction)

Prescriptive and generative AI (e.g., optimization models, AI-generated insights or content)

Back Next

📈 Business Results / Impact

6. How often does analytics directly influence decision-making?

Rarely

Occasionally, when leadership asks

Frequently across multiple departments

Routinely – data drives most decisions org-wide

7. What’s the return on investment (ROI) from your analytics efforts?

Unknown

Less than 1x

Between 1x and 5x

Greater than 5x

8. What’s the turnaround time from question to insight?

Days or weeks

A few days

Within a day

Minutes or instantly, via dashboards or automation

9. How would you describe the trustworthiness of your data?

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

Decent governance of business applications, but the way we extract insights is inconsistent, leading to multiple sources of truth or manual errors

Improving consistency – we’re aligning systems and standardizing how data is processed – we're building trust.

High fidelity – our data is well-governed, traceable, and trusted across the organization

10. How aligned is your company around KPIs and performance targets?

We lack consensus on KPIs

We’ve identified KPIs but don’t set clear targets

We have KPIs and targets, but don’t manage to them

We have KPIs and targets, and manage to them via mechanisms like MBRs and gap analysis

Back