This course also describes the main types of analytics and where they are best used. Students will also learn about data governance and why it is important to organizations. The course also provides successful use cases that demonstrate the concepts presented in the course, including data governance and data preparation.
Who Should Take DT102?
- Automation professionals engaged in designing, implementing, and operating Industry 4.0 solutions
- Digital transformation managers working with OT data
- Automation software providers wishing to expand their enterprise-based offerings
- IT professionals tasked with implementing enterprise-based OT solutions
- Professionals responsible for procuring OT big data solutions who need a stronger background in big data fundamentals to make more informed technological decisions
View Offerings by Format
Classroom (DT102)Length: 1 day |
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Visit our course formats page for a detailed description of each format.
Learning Objectives
- Data Ingestion and Preparation
- List the major steps in data ingestion and preparation
- Discuss the role of automation professionals in the cleansing process
- Evaluate which of the data preparation steps are important to your organization
- Storing Big Data
- List the significant differences between SQL and noSQL
- Describe the major types of noSQL databases and the primary advantages of each database
- Explain the key differences between data warehouses and data lakes
- Describe the role of time series databases and distinguish plant historians from enterprise historians
- Appraise how the various big data components would be useful in your work environment
- Analytics—Making Sense of Data
- List the major types of analytics and the advantages of each type
- Discuss what type of analytics would be useful for various use cases
- Evaluate where edge, cloud, and hybrid analytics should be used
- Data Governance
- List what data governance is and why it is important
- Describe the various roles in data governance
- Design a data governance program using industry best practices
- Evaluate how the various data governance challenges would impact your organization
- Use Cases
- Describe the business problems being addressed for each use case
- Explain the key value propositions for each use case
- Appraise if your organization could benefit from these use cases