Informatica Data Quality Analyst - Beginner's Guide
What you'll learn
- Perform data profiling and root cause analysis to identify data quality issues opportunities and provide recommendations to work with business and IT for remediation.
- Work with business stakeholders to gather requirements, conduct data profiling and track metrics.
- Proactively identify, prioritize and re-mediate data quality issues.
- Work with cross-functional areas: IT, Data Architecture, Data Integration, Business Intelligence, Analytics, and business stakeholders.
- Participate as a member of key cross-functional working groups related to data quality, and the company’s Data Governance Working Group representing the data domain.
- Partners with development and business resources in all phases of Data Quality Implementation.
- Perform analysis and provide recommendations for modification or creation of business/technical rules based on data results.
- Excellent analytical ability to understand business issues associated with data management, architecture and governance, business process, in terms of data and related standards.
- Create and monitor data quality scorecards and data integrity efforts to works with data owners/stewards to resolve data quality issues.
- Mandatory: Data Quality Fundamentals (Refer to the existing course on Udemy by Inf Sid)
- Good to know: Data Warehouse Concepts - Beginner to Advanced (Refer to the existing course on Udemy by Inf Sid)
- Good to know: Informatica Power Center - Beginner to Advanced (Refer to the existing course on Udemy by Inf Sid)
- Good to know: Cloud Data Warehouse Concepts (Refer to the existing course on Udemy by Inf Sid)
- Good to know: Informatica Cloud - Data Integration (Refer to the existing course on Udemy by Inf Sid)
Informatica Analyst or IDQ Analyst is a web-based application client that analysts can use to analyze, cleanse, standardize, profile, and score data in an enterprise.
Business analysts and developers use Informatica Analyst for data-driven collaboration. You can perform column and rule profiling, scorecarding, and bad record and duplicate record management. You can also manage reference data and provide the data to developers in a data quality solution.
Organizations use Informatica Analyst to accomplish the following tasks:
Profile data. Create and run a profile to analyze the structure and content of enterprise data and identify strengths and weaknesses. After you run a profile, you can selectively drill down to see the underlying rows from the profile results. You can also add columns to scorecards and add column values to reference tables.
Create rules in profiles. Create and apply rules within profiles. A rule is reusable business logic that defines conditions applied to data when you run a profile. Use rules to further validate the data in a profile and to measure data quality progress.
Score data. Create scorecards to score the valid values for any column or the output of rules. Scorecards display the value frequency for columns in a profile as scores. Use scorecards to measure and visually represent data quality progress. You can also view trend charts to view the history of scores over time.
Manage reference data. Create and update reference tables for use by analysts and developers to use in data quality standardization and validation rules. Create, edit, and import data quality dictionary files as reference tables. Create reference tables to establish relationships between source data and valid and standard values. Developers use reference tables in standardization and lookup transformations in Informatica Developer.
Manage bad records and duplicate records. Fix bad records and consolidate duplicate records.
Who this course is for:
- Business Analysts
- Data Analysts
- Data Scientists
- Data Analytics Professionals
- Data Warehouse Professionals
- Big Data Professionals
- ETL Architects
- ETL Developers
- ETL Testing/QA Professionals
Business Intelligence Consultant and Trainer with 14+ years of extensive work experience on various client engagements. Delivered many large data warehousing projects and trained numerous professionals on business intelligence technologies. Extensively worked on all facets of data warehousing including requirement gathering, gap analysis, database design, data integration, data modeling, enterprise reporting, data analytics, data quality, data visualization, OLAP.
Has worked on broad range of business verticals and hold exceptional expertise on various ETL tools like Informatica Powercenter, SSIS, ODI and IDQ, Data Virtualization, DVO, MDM.