Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value.
It assists in successfully meeting increasingly strict compliance requirements, such as those in the General Data Protection Regulation (GDPR). And it provides a clear gauge on business performance.
A mature and sustainable data governance initiative must include data integration.
This often requires reconciling two groups of individuals within the organization: 1) those who care about governance and the meaningful use of data and 2) those who care about getting and transforming the data from source to target for actionable insights.
Both ends of the data value chain are covered when governance is coupled programmatically with IT’s integration practices.
The tools and processes for this should automatically generate “pre-ETL” source-to-target mapping to minimize human errors that can occur while manually compiling and interpreting a multitude of Excel-based data mappings that exist across the organization.
In addition to reducing errors and improving data quality, the efficiencies gained through automation, including minimizing rework, can help cut system development lifecycle costs in half.
In fact, being able to rely on automated and repeatable processes can result in up to 50 percent in design savings, up to 70 percent conversion savings, and up to 70 percent acceleration in total project delivery.
Data Governance and the System Development Lifecycle
Boosting data governance maturity starts with a central metadata repository (data dictionary) for version-controlling metadata imported from a broad array of file and database types to inform data mappings. It can be used to automatically generate governed design mappings and code in the design phase of the system development lifecycle.
The right toolset – one that supports a unifying and underlying metadata model – will be a design and code-generation platform that introduces efficiency, visibility and governance principles while reducing the opportunity for human error.
Automatically generating ETL/ELT jobs for leading ETL tools based on best design practices accommodates those principles; it functions according to approved corporate and industry standards.
Automatically importing mappings from developers’ Excel sheets, flat files, access and ETL tools into a comprehensive mappings inventory, complete with automatically generated and meaningful documentation of the mappings, is a powerful way to support governance while providing real insight into data movement – for lineage and impact analysis – without interrupting system developers’ normal work methods.
GDPR compliance, for example, requires a business to discover source-to-target mappings with all accompanying transactions, such as what business rules in the repository are applied to it, to comply with audits.
THE REGULATORY RATIONALE FOR INTEGRATING DATA MANAGEMENT & DATA GOVERNANCE
When data movement has been tracked and version-controlled, it’s possible to conduct data archeology – that is, reverse-engineering code from existing XML within the ETL layer – to uncover what has happened in the past and incorporating it into a mapping manager for fast and accurate recovery.
This is one example of how to meet data governance demands with more agility and accuracy at high speed.
Faster Time-to-Value with the erwin Automation Framework
The erwin Automation Framework is a metadata-driven universal code generator that works hand in hand with erwin Mapping Manager (MM) for:
- Pre-ETL enterprise data mapping
- Governing metadata
- Governing and versioning source-to-target mappings throughout the lifecycle
- Data lineage, impact analysis and business rules repositories
- Automated code generation
If you’d like to save time and money in preparing, deploying and governing you organization’s data, please join us for a demo of erwin MM.
