This 1-day workshop provides attendees with an overview of the data pipeline and transformation process examining how to harness the power of data intelligence by recognizing the importance of improving data quality for AI productivity use cases.
Course Objectives:
Upon completing this course, the learner will be able to meet these overall objectives:
- Gain a comprehensive understanding of the value of data in increasing organizational productivity.
- Develop the ability to identify various data sources and understand where to look for data within an organization.
- Assess the five dimensions of data quality: accuracy, completeness, timeliness, consistency, and relevance.
- Obtain skills in building a data pipeline for AI, including understanding the difference between ETL and ELT, to ensure data quality.
- Identify how data quality affects various AI applications such as image recognition, natural language processing (NLP), and predictive analytics.
Module 1: Fundamentals of Data Value in Organizations
By the end of this section, learners will be able to articulate the multifaceted value of data across various organizational functions.
1.1 Driving Organizational Performance
Enabling Objective: Use data to drive decision making and optimize processes.
- Data-driven decision making
- Process optimization and bottleneck elimination
- Employee performance enhancement
1.2 Financial Impact of Data
Enabling Objective: Identify and implement data strategies to reduce costs and increase revenue.
- Cost reduction through resource optimization
- Revenue growth via targeted marketing and pricing strategies
- Risk mitigation (fraud detection, compliance, cybersecurity)
1.3 Customer-Centric Data Utilization
Enabling Objective: Analyze customer data to enhance experiences and services.
- Behavior analysis and predictive service
- Personalization of products and experiences
1.4 Innovation and Competitive Advantage
Enabling Objective: Leverage data for innovation and maintaining a competitive edge.
- Market gap identification and product development
- Competitor benchmarking and trend analysis
Module 2: Identifying and Integrating Data Sources
By the end of this section, learners will be able to identify diverse data sources and apply appropriate integration techniques.
2.1 Internal and External Data Sources
Enabling Objective: Distinguish between internal and external data sources and their uses.
- Customer databases, CRM, ERP systems
- Social media, public datasets, third-party providers
2.2 Data Integration Strategies
Enabling Objective: Implement various data integration techniques.
- Data warehousing vs. data lakes
- APIs and data federation techniques
2.3 Practical Data Source Identification (Lab)
Enabling Objective: Locate and identify data sources within an organization through practical exercises.
Module 3: Avoiding Common Pitfalls in AI Data Usage
Participants will be able to recognize and mitigate common data-related issues in AI applications.
3.1 Ensuring Data Quality and Diversity
Enabling Objective: Assess and improve data quality and diversity.
- Addressing bias, volume, and variety issues
- Proper data labeling and preprocessing
3.2 Contextual Considerations
Enabling Objective: Understand and apply data context in analysis.
- Understanding data limitations and applicability
- Balancing model performance with data context
3.3 Data Privacy and Security Measures
Enabling Objective: Implement data privacy and security measures.
- Anonymization techniques and regulatory compliance
Module 4: Building Effective Data Pipelines for AI
Participants will be able to design and implement robust data pipelines for AI applications.
4.1 ETL vs. ELT Approaches
Enabling Objective: Compare and choose between ETL and ELT approaches.
- Comparative analysis and use cases
4.2 Data Quality Assurance
Enabling Objective: Profile and validate data quality.
- Profiling, validation, and cleansing techniques
4.3 Advanced Data Transformation
Enabling Objective: Apply advanced data transformation techniques.
- Normalization, feature engineering, and augmentation
4.4 Data Integration and Aggregation Strategies
Enabling Objective: Integrate and aggregate data from multiple sources.
- Combining data from multiple sources
- Aggregating data for analysis
4.5 Hands-on Data Cleaning Lab
Enabling Objective: Clean and prepare data for AI applications through practical exercises.
Module 5: Data Quality Impact on AI Applications
Participants will be able to evaluate and optimize data quality for specific AI domains.
5.1 Domain-Specific Data Quality Considerations
Enabling Objective: Assess and improve data quality in various AI domains.
- Image recognition, NLP, and predictive analytics
5.2 Comparative Analysis Lab: Raw vs. Transformed Data
Enabling Objective: Compare the performance of AI models using raw data versus transformed data through practical exercises.
Module 6: Leveraging Splunk for AI Data Operations
Participants will be able to utilize Splunk for efficient data management in AI workflows.
6.1 Data Management Ecosystem Overview
Enabling Objective: Compare various data management platforms.
- Comparison with Databricks, Snowflake, and other platforms
6.2 Splunk Fundamentals for AI
Enabling Objective: Understand and utilize Splunk’s capabilities for AI data operations.
- Data ingestion, processing, and visualization capabilities
6.3 Automation Lab: Streamlining Data Workflows with Splunk
Enabling Objective: Automate data ingestion and processing using Splunk through practical exercises.
The primary audience for this course is as follows:
- CTO/CIO
- Director of IT
- Line Of Business Leaders
- Technical Decision Makers
- Anyone who touches Data