Quality and Risk Awareness for Data Products and Services

Ensuring Excellence & Mitigating Hazards in the Data-Driven Era
Why This Training?
In the era of data-driven decision-making, maintaining quality and understanding potential risks in data products and services is non-negotiable. Poor data quality and unforeseen risks can lead to flawed decision-making, compliance issues, and even financial losses. Equip yourself with the tools, techniques, and insights to ensure your data products are of impeccable quality and understand how to identify, assess, and mitigate risks associated with them.
Duration: 6 Hours (online / virtual live session)

Who Should Attend?

 Data Professionals: Data Scientists, Data Analysts, Data Engineers
 Project Managers & Team Leads: Those overseeing data-centric projects
See more  
 Data Stewards and Data Governance Officers: Professionals responsible for data quality and management
 Business Analysts: Those leveraging data for business insights
 IT & Tech Teams: Professionals developing or managing data platforms and services
 Decision Makers: CTOs, CIOs, and other leaders relying on data for strategic decisions

Course Highlights

 Dive deep into the principles of Data Quality Management.
 Learn to identify, assess, and categorize data-related risks.
 Master data quality tools and techniques for modern data platforms.
See more  
 Gain insights into risk mitigation, including anonymization, backup, and disaster recovery strategies.
 Understand challenges and strategies in ensuring quality and mitigating risks in Big Data and AI environments.
 Participate in real-world case studies and group discussions to contextualize learning.
 Build an organizational culture that prioritizes data quality and risk awareness.


 Basic understanding of data management principles.
 Familiarity with common data platforms and tools (e.g., databases, data warehouses).
 No prior knowledge of risk management is required, but a general awareness of data security principles will be advantageous.

Training Materials Needed by Participants

A laptop or tablet with internet access for interactive sessions and hands-on exercises.
Any data management or analytics tool you commonly use (for practical sessions).
Notepad and pen for taking notes.
Pre-training reading material (will be shared prior to the training session).
Write your awesome label here.

Training Content

Quality and Risk Awareness for Data Products and Services

Session 1: Introduction to Quality and Risk in Data Products and Services

Understanding Quality in Data
  • Definitions and importance of data quality for businesses and operations
  • Data accuracy, completeness, relevance, timeliness, and consistency
Principles of Data Quality Management
  • Aligning data quality with business objectives
  • Continuous monitoring and improvement of data sources
  • Importance of data lineage and traceability
  • Metadata and documentation
Introduction to Risk in Data Services
  • Understanding data-related risks: breaches, misinformation, inaccuracy
  • The balance between data accessibility and data security
Risk Identification and Assessment for Data Products
  • Tools for data risk identification: Data audits, anomaly detection, and profiling
  • Risk categorization: Privacy risks, security risks, compliance risks, and business decision risks
  • Evaluating data risks using the risk matrix: Understanding likelihood and impact

Session 2: Delving Deeper into Quality and Risk Management for Data Products

Quality Tools and Techniques for Data
  • Data cleansing and validation techniques
  • Data quality frameworks: DAMA, TDWI
  • The role of ETL (Extract, Transform, Load) processes in ensuring quality
Risk Mitigation and Management in Data Services
  • Data anonymization and pseudonymization
  • Data backup, archiving, and disaster recovery strategies
  • Data breach response plans
Quality and Risk in the Era of Big Data and AI
  • Challenges posed by unstructured data and real-time data streams
  • Risks of bias and ethics in AI-driven data services
  • Ensuring quality in Machine Learning models and outputs
Creating a Culture of Quality and Risk Awareness for Data Teams
  • Role of Data Stewards and Data Governance teams
  • Training and continuous education tailored for data professionals
  • Promoting a transparent data culture with open communication
Case Studies & Group Discussion
  • Real-world examples of successful and failed data projects
  • Analyzing data challenges and how organizations navigated them
Conclusion & Actionable Takeaways
  • Best practices for ensuring quality and managing risks in data projects
  • Further resources for mastering specialized data topics.
Created with