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1. European regulation governing data protection and privacy.
5. Field focused on analysing historical data to support business decisions.
7. The process of obtaining data from multiple sources.
9. Professional who examines datasets to draw quantitative conclusions.
10. Type of data source found inside a company.
11. The amount of data generated (one of the 5 Vs of Big Data).
12. Measure of how accurate, complete and consistent data is.
17. Managing data quality, availability, and security.
19. Analytics that explains why something happened.
21. Measures to safeguard data from unauthorized access or breaches.
22. California law giving consumers rights over their personal data.
24. The usefulness or benefit gained from analysing data.
25. Analytics that forecasts what will happen.
26. Discipline focussed on identifying business needs and recommending solutions.
28. Data that follows a defined format or schema.
29. The simulation of human intelligence by machines.

2. Analytics that suggests what should be done next.
3. A subset of AI that enables systems to learn from data automatically.
4. The practice of using data and analysis to improve business performance.
6. Data without a predefined format.
7. Practice of ensuring data consistency, quality, availability and security.
8. Analytics that summarizes what happened.
13. The speed at which data is generated and processed.
14. Type of data source from outside the organisation.
15. The accuracy and trustworthiness of data.
16. Field involving algorithms and machine learning to predict trends and behaviours.
18. Large and complex data sets requiring advanced tools.
20. Protection of personal or sensitive data from misuse.
23. Professional who identifies business problems and proposes improvements.
27. The different types and formats of data (structured, unstructured etc.).