Data Management, Analytics, and Information Security
Data management ensures data quality, availability, and security across its lifecycle. Key concepts include data governance, master data management (MDM), data warehousing, and data lakes. Analytics ranges from descriptive to predictive and prescriptive, with increasing business value. Information security protects confidentiality, integrity, and availability (CIA triad). Candidates must understand risk management frameworks (e.g., NIST), access control models (RBAC, ABAC), and encryption. A common trap is conflating data governance with data management: governance defines policies and accountability, while management executes. Another trap is assuming that analytics is only about tools; it requires clean, governed data and skilled analysts. Also, be clear on security vs. compliance: security protects assets, compliance adheres to regulations. A worked example: A company wants to improve customer insights. It should first establish data governance to ensure data quality, then implement a data warehouse and analytics tools. Rule of thumb: Treat data as a strategic asset; apply the CIA triad to security; and ensure analytics initiatives are driven by business questions, not technology.