Commonly used Abbreviations and Definitions in Data Migration
Abbreviations
API – Application Programming Interface
BIDM – Business Integrated Data Migration
CRM – Customer Relationship Management
CSV – Comma Separated Values
DBA – Database Administrator
DM – Data Migration
DP – Delivery Phases
DQ – Data Quality
DV – Data Verification
ECM – Electronic Content Management
EDW – Enterprise Data Warehouse
ERP – Enterprise Resource Planning
ETL – Extract, Transform, Load
ICT – Information, Communication, Technology
JSON – JavaScript Object Notation
LA – Landscape Analysis
MDM – Master Data Management
REST – Representational State Transfer
SOAP – Simple Object Access Protocol
SFTP – Secure File Transfer Protocol
SH – Stakeholder
SIT – System Integration Testing
SME – Subject Matter Expert
SS – Spreadsheets
SSIS – SQL Server Integration Services
UAT – User Acceptance Testing
XML – eXtensible Markup Language
Definitions
Business Integrated Data Migration (BIDM) – Our proprietary methodology, used to keep projects on track
Data Migration – The process of transferring data from one system to another, often required during system upgrades or consolidations.
Legacy System – An old or outdated system that is being replaced or upgraded by a new system.
Data Objects – are a logical subset of a business domain representation, encapsulating business information. E.g. Customer, Supplier, Employee etc
Target System – The new system where data is migrated to from the legacy system.
Data Mapping – The process of identifying the relationships and transformations between the data elements in the source and target systems.
Data Transformation – The process of converting data from its original format in the legacy system to the required format in the target system.
Data Cleansing – The process of correcting, standardizing, and improving the quality of data in preparation for migration.
Data Extraction – The process of retrieving data from the legacy system for migration to the target system.
Data Loading – The process of inserting the transformed data into the target system.
Data Validation – The process of verifying that the migrated data is accurate, complete, and consistent with the source data.
Data Owner – The people or person within an organisation that is responsible for signing off on the various stages of a data migration
Data Quality – The measure of data’s accuracy, consistency, completeness, and relevancy within a specific context.
DM Package – A collection of data migration collateral including the mapping specification documentation, stakeholders and data verification techniques.
Data Integration – The process of combining data from different sources and making it available in a unified format.
Data Conversion – The process of changing data from one format or structure to another, often required during data migration.
ETL (Extract, Transform, Load) – A process used in data migration and data warehousing that involves extracting data from source systems, transforming it, and loading it into the target system.
Data Profiling – The process of examining, analyzing, and understanding the content, structure, and relationships of data to ensure its quality and readiness for migration.
Data Governance – The set of policies, processes, and practices that organizations use to manage, maintain, and protect their data assets.
Data Reconciliation – The process of comparing source and target data to ensure consistency and accuracy after migration.
Legacy Data – A data store or stores that hold data to be transferred to a new target system
Data De-duplication – The process of identifying and eliminating duplicate data records to improve data quality and consistency.
Data Staging – A temporary storage area used to prepare and transform data before loading it into the target system.
Landscape Analysis – The analysis of legacy data to find and catalogue its source and how the various sources relate to each other
Data Archiving – The process of securely storing historical or infrequently accessed data for long-term retention, compliance, and backup purposes.
Data Migration Strategy – A comprehensive plan outlining the goals, methods, tools, and resources required for a successful data migration project.
Incremental Data Migration – The process of transferring data in smaller, manageable batches over time, rather than in a single, large-scale migration.
Data Cutover – The point in a data migration project when the source system is decommissioned, and the target system becomes operational.
Data Migration Testing – A series of tests performed to validate the accuracy, completeness, and performance of the migrated data and to ensure its readiness for production use.
Data Model – A conceptual representation of the relationships, structure, and rules governing data within a database or system.
Metadata – Descriptive information about data, such as data types, formats, relationships, and usage, which helps manage, understand, and maintain data assets.
Data Migration Tools – Software applications or utilities designed to facilitate and automate the processes involved in data migration projects.
Data Masking – The process of obscuring sensitive data elements by replacing them with fictional or scrambled values to protect data privacy and security.
OBJECTS – See Data Objects
