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Data profiling is the process of analyzing actual data and understanding its true structure and meaning. It is one of the most common and important activities in information management. Data profiling is the first critical step in many major IT initiatives, including implementing a data warehouse, building an MDM hub, populating metadata repository, as well as operational data migration and integration. It is also the key ingredient to successful data quality management.
While proliferation of commercial tools made data profiling accessible for most information management professionals, successful profiling projects remain elusive. This is largely because the tools allow gathering large volumes of information about data, but offer limited means and guidelines for analysis of that information.
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In this online training course you will learn all practical skills necessary to succeed in a data profiling initiative.
You will learn:
- The what, why, when, and how of data profiling
- Various data profiling techniques, from simple column profiling to advanced profiling methods for time-dependent and state-dependent data
- How to efficiently gather data profiles
- How to analyze the data profiling information and ask the right questions about your data
- How to organize data profiling results
- How to perform dynamic data profiling and identify changes in data structure and meaning
This course is geared towards:
- data quality practitioners
- MDM practitioners
- metadata management practitioners
- IT and business analysts involved in data management
- those responsible for implementation and maintenance of various data management systems
DQ-06 Data Profiling
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About the Course (7 min)
Introduction to Data Profiling (48 min)
- What is Data Profiling?
- Myth and Reality of Data Profiling
- Profiling Techniques
- Profiling Challenges
- Role of Profiling
- Data Profiling for Big Data
- People and Technology
Column Profiling(89 min)
- Introduction
- Basic Counts
- Value Frequency Charts
- Value Distribution Characteristics
- Value Distribution
Profiling Time-Dependent Data (58 min)
- Introduction
- Timeline Profiling
- Timestamp Pattern Profiling
- Multi-Dimensional Profiling
- Event Dependency Profiling
Profiling State-Transition Models (49 min)
- Introduction
- Data Structures for State-Dependent Data
- Profiling Techniques
Other Profiling Techniques (65 min)
- Subject Profiling
- Relational Integrity Profiling
- Attribute Dependency Profiling
- Dynamic Data Profiling
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Click –here- to download a more detailed outline of this course.
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This exam tests knowledge and understanding of data profiling techniques.
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You will be tested in these areas:
- Data profiling objectives and role in various data-driven initiatives, such as data quality assessment, MDM, data migration, and data integration
- Attribute profiling components, including basic counts, frequency charts, and value distributions
- Techniques for relational integrity profiling
- Techniques for profiling time-dependent data, including timeline profiling, timestamp pattern profiling, and event history profiling
- Advanced data profiling techniques, including profiling state-transition models, subject profiling, and attribute dependency profiling
- Techniques for dynamic data profiling and identifying changes in data structure and meaning over time
- Approaches to gathering data profiles
- How to analyze the data profiling information and ask the right questions about your data
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Additional Information
Number of Questions: 25
Time Limit: 50 Minutes
Passing Score: 70%
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Once you pass the exam, you will receive a Certificate of Education
documenting that you have demonstrated mastery of the topic. Course
exams count towards eLC certification programs. Visit our Certification page for more information about our various programs.
We recommend that you take detailed notes and review the course material multiple times before taking this exam.
Click here to learn more about CIMP exams.
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