Across
- 1. – Visual tool showing key data metrics.
- 4. – Type of analytics that finds why something happened.
- 6. – Actionable finding derived from data analysis.
- 9. – Analyzing past data to aid decisions.
- 10. – The “V” of big data that refers to quantity.
- 12. – The “V” of big data that refers to data types.
- 13. – Understanding gained from information.
- 16. – The “V” of big data that refers to trustworthiness.
- 17. – Type of analytics that suggests what to do next.
- 18. – Uniformity of data across systems.
- 19. – Processed and organized data.
- 21. – Type of analytics that forecasts what will happen.
- 22. – Extremely large and complex datasets.
- 25. – Professional who improves business processes.
- 26. – The science of analyzing data to gain insights.
- 27. – Moral principles guiding data use.
- 29. – Professional who analyzes datasets.
Down
- 2. – Simulating human intelligence using machines.
- 3. – Field combining statistics and computing to predict trends.
- 5. – The “V” of big data that refers to usefulness.
- 7. – Protecting personal or sensitive information.
- 8. – Type of analytics that explains what happened.
- 10. – The “V” of big data that refers to speed.
- 11. – Technology used for predictive modeling.
- 14. – European law protecting personal data.
- 15. – Safeguarding data from unauthorized access.
- 20. – California law ensuring consumer data rights.
- 23. – Availability of data when needed.
- 24. – Key quality characteristic of reliable data.
- 28. – Raw facts and figures before processing.