The document discusses IoT and data governance as essential frameworks for evaluating stakeholder needs and achieving enterprise objectives. It highlights critical aspects like data quality, ownership, access requirements, and common pitfalls in governance practices. Key questions for effective data management are posed, emphasizing the importance of balancing control with the need for ad hoc analytics.

1. IoT and Data Governance
Ray Henry
Department of Information Systems
Cleveland State University

4. Governance
• Ensures that stakeholder needs, conditions and options
are evaluated to determine balanced, agreed-on
enterprise objectives to be achieved
– setting direction
– prioritization
– decision making
– monitoring performance and compliance

5. Data Governance
Decision rights and accountability for
information/data assets

6. Corporate Governance
IT Governance
Data Governance

7. Governance Enablers
• Processes
• Organizational Structure
• Culture, Ethics, and Behavior
• Resources

8. Domains of Data
Governance
Data Principles
Role of data as an asset
Data Quality
Requirements for data
quality
Data Lifecycle
Retention and
retirement
Meta Data
Semantics and data definition
Data Access
Requirements for data access and
security

9. Asking the Right
Questions
• Who owns the data?
• How do we ensure data quality, discovery, usability and security for the
many different teams and business units that create, use and manage the
data?
• What are the key business questions and goals that are driving what data
we collect and use?
• How do we manage ad hoc data analytics? Do we restrict it or encourage
it?

10. IoT Data Governance
Pitfalls
• Not considering all stakeholders needs
• Underestimating security and privacy implications
• Too much or too little control

11. Questions?
Thank you!