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3. The usefulness or benefit gained from analysing data.
4. Analytics that forecasts what will happen.
7. Type of data source from outside the organisation.
10. Professional who identifies business problems and proposes improvements.
12. The accuracy and trustworthiness of data.
13. Field involving algorithms and machine learning to predict trends and behaviours.
15. The amount of data generated (one of the 5 Vs of Big Data).
17. Measure of how accurate, complete and consistent data is.
19. A subset of AI that enables systems to learn from data automatically.
20. European regulation governing data protection and privacy.
22. Analytics that explains why something happened.
23. Analytics that summarizes what happened.
25. Protection of personal or sensitive data from misuse.
26. Measures to safeguard data from unauthorized access or breaches.
27. Professional who examines datasets to draw quantitative conclusions.
28. The simulation of human intelligence by machines.

1. The process of obtaining data from multiple sources.
2. Field focused on analysing historical data to support business decisions.
3. The speed at which data is generated and processed.
5. Analytics that suggests what should be done next.
6. Managing data quality, availability, and security.
8. Discipline focussed on identifying business needs and recommending solutions.
9. California law giving consumers rights over their personal data.
11. Type of data source found inside a company.
14. Data that follows a defined format or schema.
16. The practice of using data and analysis to improve business performance.
17. Practice of ensuring data consistency, quality, availability and security.
18. The different types and formats of data (structured, unstructured etc.).
21. Large and complex data sets requiring advanced tools.
24. Data without a predefined format.