What you'll learn

Define the connection between two systems implementing data Integration or Migration project
Identify roles needed for Data Mapping
Describe Transformation of data implementing Integration or Migration project
Define data Inconsistencies between two systems
Define the roles and responsibilities in IT project

Course content

6 sections • 25 lectures • 43m total length

Introduction2:30

Peculiarities of the methodology1:30
Relevant audience1:51
The data mapping process involves business and process analysts, data analysts, data architects, developers, QA analysts, and project managers. They ensure requirements are understood and data is accurately represented.
Needed skills1:44
Importance of Data Mapping1:37
Unify data from multiple systems through data mapping to enable integrated views for reporting, analytics, and decision making, while improving data quality and automating data compatibility.
Concepts used for data mapping3:42
Practical task0:23

Practical task0:14
Purpose of working template1:50
Header1:22
In a collaborative team, the header defines the data mapping requirements and links this document to key project documents, clarifying roles, fields to fill, and contact points.
Filling sequence1:56
Follow a strict filling sequence from header fields to the target schema, capturing data scope, technical specification, and business requirement for complete attribute alignment.
Sections and steps1:57
Mandatory fields1:25
Responsibilities1:46
Distribute responsibilities across project scopes to adapt to varying company needs, align template requirements, and assign a competent data coordinator to gather information and maintain the big-picture data structure.
Data mapping rules3:10
Explore six data transformations for synchronizing data, from extraction and cleaning to enrichment and reconciliation, using SQL for precise, auditable mappings.
Filling process4:00
Practical task0:16

Data structure analysis1:47
Analyze data structure in the data mapping approach by identifying source and target systems, owners, and entry points, then build a logical data model with key fields and relationships.
Data quality assurance2:11
Apply an iterative data mapping cycle to ensure quality, including information gathering, structure analysis, rule application, data extraction, validation, and addressing discrepancies for reconciled rules.
Data quality assessment2:31
Learn to validate data by checking format, range, completeness, consistency, referential integrity, and accuracy to ensure high quality and fit for purpose.
Practical task0:14

Requirements

Ability to analyze data
Ability to use tool for data transformation (SQL statement),
Ability to design and build data models

Description

This course is based on long-term practice and project diversity. The main advantage of the methodology is that it does not require long-term learning. It only requires understanding the logical concept and attempting to apply it in your ongoing projects.

The provided methodology can be applied to processes such as data migration, integration, warehousing, report creation, or analytics. The main goal of the methodology is to facilitate collaboration among different roles involved in the data mapping process and to outline the steps that should be followed to achieve high-quality results. The main goal of the methodology is to facilitate collaboration among different roles involved in the data mapping process and to outline the steps that should be followed to achieve high-quality results. Data mapping also helps identify any inconsistencies or incompatibilities in the data, which may require additional resolutions before using the data. This can significantly impact the progress of the project and its technical and logical decisions.

Many specialists encounter various problems when executing data mapping processes. This happens because there is not always enough information gathered for data harmonization, and it is not always possible to systematically process the collected information. Timely trained specialists have the opportunity to work systematically and understand the actions required of them and how subsequent steps depend on them.

I wish you success in your studies!

Who this course is for:

Business analysts
Data analysts
Data architects
Developers
QA analysts
Project managers

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 3min

Basis of data mapping6 lectures • 11min

Data mapping process3 lectures • 5min

Working template10 lectures • 18min

Data quality4 lectures • 7min

Conclusions and recommendations1 lecture • 1min

Requirements

Description

Who this course is for: