Organizations are responsible for governing more data than ever before, making a strong automation framework a necessity. But what exactly is an automation framework and why does it matter?
In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape.
Often these enterprises are heavily regulated, so they need a well-defined data integration model that helps avoid data discrepancies and removes barriers to enterprise business intelligence and other meaningful use.
IT teams need the ability to smoothly generate hundreds of mappings and ETL jobs. They need their data mappings to fall under governance and audit controls, with instant access to dynamic impact analysis and lineage.
With an automation framework, data professionals can meet these needs at a fraction of the cost of the traditional manual way.
In data governance terms, an automation framework refers to a metadata-driven universal code generator that works hand in hand with enterprise data mapping for:
Such automation enables organizations to bypass bottlenecks, including human error and the time required to complete these tasks manually.
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.
So without further ado, here are the five key benefits of an automation framework for data governance.
Code automation templates (CATs) can be created – for virtually any process and any tech platform – using the SDK scripting language or the solution’s published libraries to completely automate common, manual data integration tasks.
CATs are designed and developed by senior automation experts to ensure they are compliant with industry or corporate standards as well as with an organization’s best practice and design standards.
The 100-percent metadata-driven approach is critical to creating reliable and consistent CATs.
It is possible to scan, pull in and configure metadata sources and targets using standard or custom adapters and connectors for databases, ERP, cloud environments, files, data modeling, BI reports and Big Data to document data catalogs, data mappings, ETL (XML code) and even SQL procedures of any type.
Stage DDL from source metadata for the target DBMS; profile and test SQL for test automation of data integration projects; generate source-to-target mappings and ETL jobs for leading ETL tools, among other capabilities.
It also can populate and maintain Big Data sets by generating PIG, Scoop, MapReduce, Spark, Python scripts and more.
An organization can achieve a more comprehensive and sustainable data governance initiative than it ever could with a homegrown solution.
An automation framework’s ability to automatically create, version, manage and document source-to-target mappings greatly matters both to data governance maturity and a shorter-time-to-value.
This eliminates duplication that occurs when project teams are siloed, as well as prevents the loss of knowledge capital due to employee attrition.
Another value capability is coordination between data governance and SDLC, including automated metadata harvesting and cataloging from a wide array of sources for real-time metadata synchronization with core data governance capabilities and artifacts.
Automated reverse-engineering of ETL code into natural language enables a more intuitive lineage view for data governance.
With end-to-end lineage, it is possible to view data movement from source to stage, stage to EDW, and on to a federation of marts and reporting structures, providing a comprehensive and detailed view of data in motion.
The process includes leveraging existing mapping documentation and auto-documented mappings to quickly render graphical source-to-target lineage views including transformation logic that can be shared across the enterprise.
Similarly, impact analysis – which involves data mapping and lineage across tables, columns, systems, business rules, projects, mappings and ETL processes – provides insight into potential data risks and enables fast and thorough remediation when needed.
Impact analysis across the organization while meeting regulatory compliance with industry regulators requires detailed data mapping and lineage.
Intelligent automation delivers enhanced capability, increased efficiency and effective collaboration to every stakeholder in the data value chain: data stewards, architects, scientists, analysts; business intelligence developers, IT professionals and business consumers.
It makes it easier for them to handle jobs such as data warehousing by leveraging source-to-target mapping and ETL code generation and job standardization.
It’s easier to map, move and test data for regular maintenance of existing structures, movement from legacy systems to new systems during a merger or acquisition, or a modernization effort.
Mature and sustainable data governance requires collaboration from both IT and the business, backed by a technology platform that accelerates the time to data intelligence.
Part of the erwin EDGE portfolio for an “enterprise data governance experience,” the erwin Automation Framework transforms enterprise data into accurate and actionable insights by connecting all the pieces of the data management and data governance lifecycle.
As with all erwin solutions, it embraces any data from anywhere (Any2) with automation for relational, unstructured, on-premise and cloud-based data assets and data movement specifications harvested and coupled with CATs.
If your organization would like to realize all the benefits explained above – and gain an “edge” in how it approaches data governance, you can start by joining one of our weekly demos for erwin Mapping Manager.