A blog about Product Management, Data Management and Self-Driven Learning
The post discusses the importance of quality data in AI product management, outlining the DASUD model: Design, Acquire, Store, Use, and Delete. It emphasises defining success, collecting accurate data, securely storing it, ethical use, and timely deletion. Trust in data is crucial for effective AI implementation, ensuring good governance.
Free apps often seem costless, but they exploit personal data as their main revenue source. Users unknowingly grant access to sensitive information, which can be sold or used for targeted advertising. To safeguard privacy, it's crucial to review app permissions, read privacy policies, and choose privacy-focused alternatives.
Data quality is crucial for effective decision-making, operational efficiency, and regulatory compliance. It encompasses attributes like accuracy, completeness, consistency, and timeliness. Establishing a data quality framework involves defining goals, conducting inventory, implementing tools, and continuous monitoring. Regular auditing ensures sustained quality, helping organisations turn data quality into a strategic advantage.
As businesses expand, effective data governance must evolve to address new challenges. Key strategies include establishing a solid foundation, anticipating growth needs, automating processes, expanding tools, introducing metrics, maintaining clear data ownership, and regularly reviewing the governance framework. These actions ensure data integrity, security, and compliance as organisations grow.
Streamlining data governance involves defining roles, automating tasks, implementing a centralised framework, prioritizing data quality, utilising recognised frameworks, and conducting regular audits. This approach enhances efficiency, compliance, and data quality while fostering collaboration among stakeholders. Continuous improvement is key to adapting governance processes to emerging needs and challenges.
Nigel D'Souza 