This short survey explores how companies approach data management, data integration, data governance and other aspects of data engineering.  It should take 5-6 minutes and your responses are completely anonymous.

Complete the survey and we'll share the report of the survey findings with you. You'll also be entered in a drawing for free copies of the “Data Teams” book and other prizes.

We really appreciate your input! Contact us at survey@gradientflow.com for any questions.

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1. Describe your PRIMARY role in using data for BI, analytics, and data science. Select the choice that best describes your role or how you spend most of your time. 

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2. How would you characterize your organization’s level of maturity with DataOps (agile and automated approaches to data management)? (select one)

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3. How would you describe how and where you process and store data today for BI, analytics and data science? (select one)

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4. For BI, analytics and data science, what is your likely future state (within the next 12-24 months) with respect to using cloud-based software? (select one)

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5. What database platforms or cloud data platforms are currently in production in your organization? (check all that apply)

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6. How important are the following when evaluating a database or cloud data platform?  (1=Not important, 5=Very important)

  1 2 3 4 5
Available on multiple cloud platforms (multi-cloud)
Support for open file formats (e.g. Parquet)
Speed and Scale
Total Operational Costs
Best-of-breed for specific workload
Integration with current infrastructure

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7. What database platforms or cloud data platforms are you likely to adopt within the next 12-24 months? (check all that apply)

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8. To the best of your ability, rate the level of challenge involved in each of the following steps in using cloud platforms for analytics. (1=Not challenging, 5=Extremely challenging)

  1 2 3 4 5
Extract and load data in the cloud
Classify and catalog cloud data assets for discovery
Transform and model cloud data for analytical use
Data Quality and Validation tests
Control user access to cloud data (implementing roles and grants based on policy)
Mask or anonymize cloud data to conform with business rules or privacy laws
Monitor and audit cloud data use for legal or compliance purposes
Sharing and exchanging data with third parties
Data Engineering team’s ability to deliver value

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9. What specific challenges do you face when working on any of the areas that you rated 4 or 5 in Question 8?

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10. What tools does your organization use to integrate data (i.e. extraction, loading/ingestion, transformation, modeling, master data management, etc) for BI, analytics and data science? (check all that apply)

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11. What tools does your organization use for workflow management and orchestration? (check all that apply)

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12. What tools does your organization use to improve data quality?  (check all that apply)

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13. What Data Catalog and Data Discovery tools are in use in your organization? (check all that apply)

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14. What BI & Analytics tools are in use in your organization? (check all that apply)

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15. What best describes how your organization handles sensitive data - defined as any data that cannot or should not be seen by all analysts or data scientists - within BI/analytics and data science today? (select one)

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16. What rules must your organization comply with when sensitive or personal data is used for analytics or data science? (check all that apply)

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18. What is the size (by employee count) of your current employer? (select one)

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