Why is Metadata Manager needed?

“Metadata Management” is the process of managing an organization’s data assets in the context of how they are used by systems and business processes.  This enables effective changes to the data assets to be made.  Metadata Manager helps organizations understand the full impact of those changes.

When organizations don’t know what data they have, where it resides, or what its lineage is, you’re almost guaranteed not to have a smooth and successful data migration. Data lineage shows you where data originates and how it moves through the organization. With visibility into data lineage, you can be much more strategic about data migration and its impact.

Adaptive Metadata Manager enables you to interactively trace data origin through business-friendly summarized lineage views that highlight the end points and all the complex details in between. A drill-down lineage view expands any lineage path to show columns and lineage diagram metrics.

Impact analysis can tell you how data is used and what is dependent on it. Adaptive Metadata Manager enable organizations to perform detailed impact analysis on upstream and downstream data assets. That way you can easily understand the impact of migration across data assets, resources, and users. You can also use this information to demonstrate the cost benefits of moving certain data assets and workloads to the cloud.

Adaptive Metadata Manager automatically inspects source data warehouse schema structures to help your IT organization identify which structures need to be modified. After migration, you can use lineage diagrams to validate that structure changes have been performed correctly.

Organizations realize the need for Adaptive’s Metadata Manager when they have reached the limit of process modeling and data modeling tools and/or home-grown Microsoft Office Access databases and spreadsheets.

Key Capabilities include:

Data Traceability – Metadata Manager allows organizations to understand the business terms and relationships to the conceptual, logical and physical models of the data leveraging abstracting to ensure data quality and data integrity understanding including leveraging rules.

Data Lineage – When examining data from a report or dashboard, trace the data to its origin to ensure full data lineage is understood including transformations to the data.   This is very important for proper data governance and analytic capabilities including migrating data to cloud based analytic platforms.

Impact Analysis – When considering a change to a data source, understand what downstream consumers are impacted and ensure that the right stakeholders are involved in the process to include audit trails and change management processes.

Reusability – In the course of developing new capabilities, understand the best source of data (and avoid creating redundant sources of data).   This reduces the time for data analyst and data scientists to leverage the correct data sources.

Analysis – Enables an analyst or developer to investigate anomalies by revealing data flows, and identify participating systems and responsible parties including how data resides and moves from one system to another.

With the Adaptive Metadata Manger, organizations can answer the following questions with confidence and speed:

• Who is using the data?

• Which data elements are used where?

• What is the quality of the data?

• What is the definition of an entity or attribute?

• What processes use a specific object?

• What applications will be impacted if my customer data “source” (feed) changes?

• Which business functions/processes are supported by customer data?

• Which data elements are in compliance with industry standards?