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Quiz test

Quiz

Personal information

First name(Required)

Last name(Required)

Company name(Required)

Your work email where we can send the assessment report(Required)

Questions

1. Stage of digital transformation journey in your company(Required)

Early - foundational systems still manual

Developing - key processes digitized

Mature - data and digital embedded company-wide

Advanced - data-driven and AI at scale

2. Main business goals currently tied to data initiatives (Multiple selections allowed)(Required)

Improving decision-making and reporting

Increasing operational efficiency

Reducing costs

Enhancing customer experience

Enabling AI and ML use cases

Compliance and risk management

New product or revenue opportunities

Other

(Required)

3. How satisfied are stakeholders with the current data-driven decision-making process?(Required)

Very satisfied

Somewhat satisfied

Neutral

Not satisfied

Very dissatisfied

4. How often do data incidents (e.g. pipeline failures, data errors) impact business decisions?(Required)

Daily

Weekly

Monthly

Occasionally

Never

5. How is your data team structured?(Required)

Centralized data team

Federated / domain-based teams

Hybrid (central + domain experts)

Outsourced / external support only

Other

(Required)

6. Number of data team members(Required)

<5

5-10

10-50

>50

7. Would you like to add a comment to the previous answer? (if needed for clarity)

8. Who primarily "owns" data quality and reliability in your organization?(Required)

Central data team

Individual domain teams

Shared responsibility

No clear ownership

9. How standardized are your data definitions and metrics?(Required)

Well-defined and consistent across all teams

Defined but not enforced

Vary widely by department

No formal definitions

10. How easily can stakeholders access data for decision-making?(Required)

Fully self-serve

Partially self-serve

Request-based via data team

Difficult or incosistent access

11. Have you implemented appropriate technical measures like data anonymization techniques to protect sensitive personal data in accordance with various compliance regulations, such as GDPR?(Required)

Yes - well implemented and satisfactory

Partially implemented

Piloting / in-progress

Not implemented yet

12. Do you have formal data governance or cataloging practices in place?(Required)

Yes - well established

Partially implemented

Piloting / in-progress

No governance yet

13. How do you currently manage data lineage and documentation?(Required)

Automated tools

Manual or partial documentation

Minimal or none

14. Do you currently track and optimize your data platform costs?(Required)

Yes, regularly

Occasionally, ad hoc reviews

Not really, we lack visibility

No, costs are not tracked

15. What is the current type of data architecture(s) in place? (Multiple selections allowed)(Required)

Data warehouse

Data lake

Data lakehouse

Data Fabric

Data Mesh

Other

(Required)

16. Which cloud or infrastructure providers do you use? (Multiple selections allowed)(Required)

AWS

Azure

GCP

Private cloud

Mostly on-prem virtual machines

Other

(Required)

17. Key technologies in your stack? (Multiple selections allowed)(Required)

Snowflake

BigQuery

Databricks

Redshift

Cloudera

Starburst

dbt

Airflow

PowerBI

Tableau

IBM Cognos

IBM Datastage

Informatica

Kafka

Spark

Flink

Collibra

Alation

S3

Oracle

Postgres

Other

(Required)

18. Are your workloads predominantly batch (daily) or (near)realtime?(Required)

Batch

(Near)realtime

Both equally

Other

(Required)

19. How important are (near)realtime for your data analytics workloads?(Required)

20. How would you rate your data quality overall?(Required)

Excellent

Good

Fair

Poor

21. Would you like to add a comment to the previous answer?

22. How do you measure data quality?(Required)

Manually

Partially automated with dashboards in place

Fully automated with automated feedback loops

Other

(Required)

23. Do you have testing procedures in place?(Required)

No

Yes, testing is mandatory but not automated

Yes, testing is fully automated

Other

(Required)

24. How automated are your data and analytics deployments (DataOps/DevOps)?(Required)

Manual

Partially automated (some CI/CD)

Mostly automated with pipelines and approvals

Fully automated with standardized CI/CD and rollback

25. How do you monitor your data pipelines and platform health?(Required)

Dedicated data observability tools

Built-in pipeline monitoring

Custom scripts and dashboards

No systematic monitoring

26. How often do you deploy changes to data pipelines, models or analytics products?(Required)

Less than monthly

Monthly

Weekly

Daily or multiple times per day

27. Does that satisfy your needs or you'd like to deploy more often?

28. How would you describe the current level of AI and machine learning adoption in your organization?(Required)

No adoption, still exploring potential

Early pilots, a few isolated projects

Growing adoption, several models in production

Advanced, AI integrated into core business processes

29. What are the biggest challenges on your organization's AI adoption journey?(Required)

No challenges

Skills

Cost

Data quality

Other

(Required)

30. What are your top 3 challenges with your current data setup? (select up to 3) (Multiple selections allowed)(Required)

Data quality and reliability

Integration of multiple data sources

Performance or scalability issues

Cost management (FinOps)

Lack of clear ownership or processes

Skills gap or team capacity

Difficulty delivering business value from data

Other

(Required)

31. If you could fix one thing about your data platform or organization this quarter, what would it be?(Required)

Consent(Required)

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