Metadata Management Solutions
Connect all the pieces of your data management and data governance lifecycles
to realize the value of your enterprise data
erwin Named a Leader in the Gartner 2019 Metadata Management Magic Quadrant
Metadata management is the hero in unleashing the real potential of enterprise data. But finding, ingesting, integrating, linking, sharing and analyzing metadata usually depends on manual processes and takes a lot of time, money and specialized technical resources. However, the right metadata management solution can help any organization reach deeper conclusions about how to drive revenue, achieve regulatory compliance or achieve any other strategic objective. erwin’s metadata management solution, the erwin Data Intelligence Suite (erwin DI), can help you wring out all the value from your data assets.
What Is Metadata?
Quite simply, metadata is data about data. It’s generated every time data is captured at a source, accessed by users, moved through an organization, integrated or augmented with other data from other sources, profiled, cleansed and analyzed. Metadata is valuable because it provides information about the attributes of data elements that can be used to guide strategic and operational decision-making. It answers these important questions:
- What data do we have?
- Where did it come from?
- Where is it now?
- How has it changed since it was originally created or captured?
- Who is authorized to use it and how?
- Is it sensitive or are there any risks associated with it?
What Is Metadata Management?
Metadata management is the administration of data that describes other data, with an emphasis on associations and lineage. It involves establishing policies and processes to ensure information can be integrated, accessed, shared, linked, analyzed and maintained across an organization.
Why Is Metadata Management Challenging?
Creating and sustaining an enterprise-wide view of and easy access to underlying metadata is complicated for several reasons:
- The numerous data types and data sources that exist today weren’t designed to work together.
- Data infrastructures have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration.
- Business context is limited or missing, and common terms haven’t been standardized and adopted across the enterprise.
- Policies, processes and tools that define and control data access by roles and across workflows are lacking.
- The traditional manual approach is expensive, time-consuming, error-prone and can’t keep pace with a dynamic enterprise data management infrastructure.
Thus the applications and initiatives that depend on a solid data infrastructure may be compromised, rendering faulty analyses and insights. Without being able to take full advantage of analytics tools, an organization can’t become data-driven — or worse, it could end up going in the wrong direction.
Benefits of Using erwin DI for Metadata Management
Organizations don’t know what they don’t know, and this problem is only getting worse. As data continues to proliferate, so does the need for data and analytics initiatives to make sense of it all. With erwin DI, users can realize these metadata management advantages:
Better Data Quality
Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair.
Quicker Project Delivery
Accelerate Big Data deployments, Data Vaults, data warehouse modernization, cloud migration, etc., by up to 70 percent.
Faster Speed to Insights
Reverse the current 80/20 rule that keeps high-paid knowledge workers too busy finding, understanding and resolving errors or inconsistencies to actually analyze source data.
Greater Productivity & Reduced Costs
Being able to rely on automated and repeatable metadata management processes results in greater productivity. Some erwin DI customers report productivity gains of 85+% for coding, 70+% for metadata discovery, up to 50% for data design, up to 70% for data conversion, and up to 80% for data mapping.
Regulatory Compliance
Regulations such as GDPR, HIPAA, PII, BCBS and CCPA have data privacy and security mandates, so sensitive data needs to be tagged, its lineage documented, and its flows depicted for traceability.
Digital Transformation
Knowing what data exists and its value potential promotes digital transformation by improving digital experiences, enhancing digital operations, driving digital innovation and building digital ecosystems.
Enterprise Collaboration
With the business driving alignment between data governance and strategic enterprise goals and IT handling the technical mechanics of data management, the door opens to finding, trusting and using data to effectively meet organizational objectives.
Why erwin DI Works for Metadata Management
erwin DI combines data management and data governance processes in an automated flow. It’s unique in its ability to automatically harvest, transform and feed metadata from a wide array of data sources, operational processes, business applications and data models into a central data catalog and then make it accessible and understandable within the context of role-based views.
erwin DI builds on our diverse industry expertise and proven data modeling software, and it integrates with third-party data access rights management and security tools. For more than 30 years, we’ve worked with high-profile brands in financial services, healthcare, technology and critical infrastructure, as well as government agencies, to co-develop products they can rely on, adjusting our roadmap based on their input.
erwin DI sits on a common metamodel that is open, extensible and comes with a full set of APIs. A comprehensive list of erwin-owned standard data connectors are included for automated harvesting, refreshing and version-controlled metadata management. Optional erwin Smart Data Connectors reverse-engineer ETL code of all types and connect bi-directionally with reporting and other ecosystem tools. These connectors offer the fastest and most accurate path to data lineage, impact analysis and other detailed graphical relationships.
Additionally, erwin DI is part of the larger erwin EDGE platform that integrates data modeling, enterprise architecture, business process modeling, data cataloging and data literacy. We know our customers need an active metadata-driven approach to:
- Understand their business, technology and data architectures and the relationships between them
- Create an automate a curated enterprise data catalog, complete with physical assets, data models, data movement, data quality and on-demand lineage
- Activate their metadata to drive agile and well-governed data preparation with integrated business glossaries and data dictionaries that provide business context for stakeholder data literacy
Customer Testimonials
“We selected erwin as our data governance partner because of their solution’s comprehensive metadata management, data harvesting and automated data lineage capabilities. What that means is that E.ON will be able to serve the right data to the right people according to their roles and responsibilities, with confidence in its quality. Then they can use it to make better, faster decisions that ultimately improve our performance as an energy company.”
Romina Medici
Data Manager
E.ON
“We selected the erwin data intelligence software because of its comprehensive metadata management capabilities and its ability to seamlessly integrate definitions from multiple solutions including business glossaries, data models and multiple sources, providing us data lineage and impact analysis across the business.”
Duane Evans-Parker
Data and Analytics Domain Manager
New Zealand Post
erwin DI’s Key Metadata Management Features
The erwin DI Suite combines data cataloging with data literacy capabilities so enterprise stakeholders have greater awareness of and access to available data assets, guidance on their use, and guardrails to ensure data policies and best practices are followed. An automated, real-time, high-quality data pipeline results, including “data at rest” in databases, data warehouses and data lakes and “data in motion” as it is integrated with and used by key applications, with the ability to control if for collaboration and risk management.
erwin Data Catalog (DC)
erwin DC automates enterprise metadata management, data mapping, code generation and data lineage for data movement and/or integration and modernization of the data architecture. The solution harvests metadata from a broad variety of data sources and maps data elements from source to target, including “data in motion,” while harmonizing data integration across platforms.
erwin Data Literacy (DL)
erwin DL enables data stewards to curate and govern data assets so data consumers can discover data relevant to their roles and understand it within a business context. By creating data communities and promoting data fluency, stakeholders have an integrated and contextual view of the semantic, business and technical aspects of the entire data landscape. erwin DL includes:
erwin DI customers may opt to add erwin Smart Data Connectors, automation and developer productivity tools that harvest data-in-motion directly from a broad variety of code types and industry standard tools and languages (BI, ELT, ETL). These tools provide visibility, improve processes and integrate data consumption platforms with reusable code, saving considerable time and money.
Choosing a Metadata Management Solution
Some vendors may boast about a strong business glossary but lack functionality to maintain business rules and processes. Others may not provide data quality features specific to meeting compliance requirements. Some may claim they use automation but have to outsource professional services to generate lineage. erwin DI is the only metadata management tool available with all of these essential capabilities:
- Reference data management for capturing and harmonizing shared reference data domains
- Data profiling for data discovery, assessment and validation
- Data quality metrics for data valuation and quality assurance
- Data mapping management to capture the data flows, reconstruct data pipelines, and visualize data lineage
- Data integration lifecycle management to provide visibility, oversite and accelerated testing and deployment
- Data lineage and usage statistics to support impact analysis
- Data pipeline automation to help develop and implement new data pipelines
- Data cataloging to capture and curate metadata for identified data assets
- Data discovery via a shared environment so data consumers understand the use of data from a wide array of sources
