IBM IGI Training
IBM IGI (IBM Information Governance and Integration) training refers to programs and courses designed to teach professionals how to manage, integrate, and govern their data effectively using IBM’s suite of information governance and integration solutions.
Why should you choose Nisa For IBM IGI Training?
Nisa Trainings is the best online training platform for conducting one-on-one interactive live sessions with a 1:1 student-teacher ratio. You can gain hands-on experience by working on near-real-time projects under the guidance of our experienced faculty. We support you even after the completion of the course and happy to clarify your doubts anytime. Our teaching style at Nisa Trainings is entirely hands-on. You’ll have access to our desktop screen and will be actively conducting hands-on labs on your desktop.
Job Assistance
If you face any problem while working on IBM IGI Course, then Nisa Trainings is simply a Call/Text/Email away to assist you. We offer Online Job Support for professionals to assist them and to solve their problems in real-time.
The Process we follow for our Online Job Support Service:
- We receive your inquiry for Online Job
- We will arrange a telephone call with our consultant to grasp your complete requirement and the tools you’re
- If our consultant is 100% confident in taking up your requirement and when you are also comfortable with our consultant, we will only agree to provide service. And then you have to make the payment to get the service from
- We will fix the timing for Online Job Support as mutually agreed by you and our consultant.
Course Information
IBM IGI Training
Duration: 25 Hours
Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
Training Method: Instructor Led Online One-on-One Live Interactive
Sessions.
COURSE CONTENT :
1. Introduction to Information Governance and Integration
- Overview of Data Governance: Importance of data governance, strategies, policies, and compliance.
- IBM IGI Tools: Introduction to IBM’s suite of tools for data integration, governance, and management.
- Data Quality and Security: Best practices for ensuring data quality, security, and compliance.
2. IBM InfoSphere Information Server
- Introduction to InfoSphere Information Server: Overview and architecture of the tool.
- Data Integration: How to integrate data from multiple sources.
- ETL Processes: Using DataStage for Extract, Transform, Load (ETL) tasks.
- Data Quality: Ensuring data consistency and cleanliness.
- Data Profiling and Validation: Techniques for profiling data to assess its quality and structure.
3. Metadata Management and Governance
- Metadata Management Overview: What metadata is, how to manage it, and why it’s important for data governance.
- IBM InfoSphere Information Governance Catalog: Using this tool for metadata management.
- Business Glossaries: Creating and managing business glossaries for consistent data terminology.
- Data Lineage: Understanding the flow and transformations of data across systems.
- Data Stewardship: Assigning roles and responsibilities for data quality and governance.
4. Master Data Management (MDM)
- Concepts of MDM: What is master data and why it is important for governance.
- IBM InfoSphere MDM: How to create a master data hub for managing critical business data.
- Data Consistency: Ensuring data consistency across different systems and departments.
- Data Matching and Merging: Techniques for identifying and merging duplicate data entries.
5. Data Security and Privacy
- Data Security and Compliance: Understanding the legal and regulatory frameworks governing data (e.g., GDPR, CCPA).
- Data Encryption and Masking: Implementing techniques for data protection.
- Access Control and Auditing: Managing user access to sensitive data and auditing data access.
- Data Privacy Management: Policies for ensuring the privacy of sensitive data.
6. IBM Watson Knowledge Catalog
- Cataloging Data Assets: Introduction to Watson Knowledge Catalog and its capabilities.
- Creating and Managing Data Catalogs: Organizing and tagging data assets for easy discovery and governance.
- Collaboration and Data Sharing: Best practices for collaboration across teams and sharing data securely.
- Data Stewardship and Governance: Managing metadata and business rules within the catalog.
7. Data Integration and DataOps
- Data Integration Framework: How to structure and manage data integration workflows.
- DataOps: Introduction to DataOps and how it helps in automating data integration, testing, and deployment.
- Cloud Data Integration: Leveraging cloud technologies for data integration and governance.
- Data Transformation and Pipelines: Best practices for building scalable and efficient data pipelines.
8. Advanced Topics
- Artificial Intelligence (AI) and Machine Learning (ML) in Governance: Using AI/ML tools to improve data governance and automate data classification.
- Cloud and Hybrid Data Governance: Managing data governance across hybrid and multi-cloud environments.
- Data Governance at Scale: Implementing governance strategies for large and complex datasets.
- Performance and Optimization: Optimizing data integration and governance processes for better performance.
9. IBM Cloud Pak for Data
- Introduction to Cloud Pak for Data: Overview of the platform and its integration with other IBM tools.
- Data Governance on the Cloud: Leveraging IBM Cloud Pak for Data for data governance, security, and integration in cloud environments.
- Managing Data and Workflows: Managing data pipelines, integration, and governance in the cloud.
- AI-Driven Data Governance: Using AI models to govern data quality and metadata in Cloud Pak for Data.