Infometry Metadata Management for Enterprises
Metadata is one of the vehicles for achieving data standardization and integration. Metadata is contextual information about IT assets, such as data, processes, programs, etc. Metadata components for data assets include business definitions, domains (valid values), data formats (type and length), business rules for creating the data, transformation and aggregation laws, security requirements, ownership, sources (operational files and databases), timeliness, and applicability, to name a few. Not many companies capture all of these metadata components, and those that do, don’t use it effectively. Metadata is no longer the dirty “D” word: documentation. It is now the nice “N” word: navigation. Documentation is often considered an IT overhead; after all, programmers can read the code, which is usually more reliable than any documentation. But navigation cannot be dismissed so quickly because it is an essential tool for business people to navigate through their BI/DW environment.
Infometry has been helping customers deliver Meta Data Management solution using on-prem and cloud-based technology solutions.
Our Metadata Management Services
When Infometry’s professionals tap into your enterprise’s active metadata, it enables them to refine the metadata so that you can use the data and add immense value to your business decisions. Our team at Infometry divides the metadata into four categories and analyzes and manages them, which are as given below –
Business-related Metadata
For business-related metadata, we collect and analyze the various operational terms, the process of governance, and their application in the business context.
Technical-Metadata
This involves various aspects, from quality checks and transformations to codes, mappings, and database schemas.
Operational Metadata
We tap into the log information, information about location and system, time stamps, stats on run-time, volume metrics, and much more.
Usage-related Metadata
We also assess usage-related metadata for your business, such as comments, user ratings, and access patterns
Benefits of Infometry Metadata Management Solutions
Infometry is a professional metadata management service provider with years of experience and a large team of professionals to take care of all the client requirements, from delving deep into the understanding of the business to providing flawless enterprise metadata management services.
- Automatically profiles and catalogues all files.
- Detects when the contents of files have changed and notify users.
- Inspect each field in a file to infer its meaning and tag the area accordingly.
- Track intermediate and "golden" files after the wrangling process.
- Discover sensitive data and enable users to remain productive and leverage the data sets freely without concern for breaching sensitive data.
- Hold business metadata, which powers multi-faceted search, enabling users to focus on the correct files quickly without spending weeks going back and forth to find the appropriate data.
- Enables data stewards to manage tags and ontologies.
- View and drill into the lineage information as they work with the data.
Contact us to discuss your Metadata and Data Lineage Requirement
Frequently Asked Questions
Enterprise metadata refers to information about data used throughout an organization. This can include information such as the meaning of data elements, the relationships between data elements, and the rules for using the data. Enterprise metadata is used to help ensure data consistency, improve data quality, and facilitate data governance. It can be stored in a central repository, such as a metadata management system, and can be used to support activities such as data modelling, data integration, and data warehousing.
Master data management (MDM) and metadata management are related but distinct concepts.
Master data management identifies, defines, and maintainsan organization’s critical data shared across multiple systems and applications. This includes data such as customer information, product information, and financial data. MDM aims to create a single, accurate, and consistent view of this data across the organization.
Metadata management, on the other hand, is managing information about data. This includes information such as the meaning of data elements, the relationships between data elements, and the rules for using the data. Metadata management aims to improve the understanding and use of data across the organization.
In short, MDM is focused on maintaining the integrity of the data itself, whereas metadata management is focused on supporting the information about the data.
Poor metadata management can manifest in several ways, including:
Lack of consistency: Different departments or systems may use different terms or definitions for the same data elements, leading to confusion and errors.
Limited accessibility: Metadata may be stored in silos, making it difficult for users to find and access the information they need.
Inadequate documentation: Metadata may be incomplete or outdated, making it difficult to understand the meaning or context of the data.
Limited data governance: There may be little oversight or control over the creation, modification, or deletion of metadata, leading to inconsistencies and errors.
Lack of integration: Metadata may not be integrated with the data it describes, making it difficult to understand the relationships between different data elements.
No standardization: Different metadata standards may be used in different departments or systems, making it difficult to share or combine data across the organization.
Limited automation: Processes for managing metadata may be manual and time-consuming, leading to errors and inefficiencies.
Overall, poor metadata management can lead to confusion, errors, and inefficiencies in data management and decision-making and may negatively impact the organization’s performance.
Ensuring that metadata is compliant and auditable involves several key steps:
Establishing clear policies and procedures: Organizations should develop clear policies and procedures for creating, modifying, and deleting metadata. These policies should comply with relevant laws, regulations, or industry standards.
Implementing data governance: Organizations should have a data governance structure to oversee metadata management. This includes roles and responsibilities for creating, modifying, and deleting metadata and oversight and review processes to ensure compliance.
Using automated tools: Automated metadata management tools can help ensure compliance by enforcing policies and procedures, providing oversight and review capabilities, and enabling auditing and reporting.
Regularly reviewing and updating policies: Organizations should check and update their metadata policies and procedures to ensure they remain compliant with any changes in laws, regulations, or industry standards.
Keeping detailed records: Organizations should keep records of all metadata activities, including who created, modified, or deleted metadata, when it was done, and what changes were made. This can help with auditing and compliance reporting.
Auditing regularly: Regular metadata audits should be conducted to ensure compliance with procedures or policies and find any issues that need to be addressed.
By implementing these steps, organizations can ensure that their metadata is compliant and auditable, which can help to mitigate risk and ensure data integrity.
Achieving metadata alignment across applications involves several key steps:
Identify the critical data elements: Identify the organization’s essential factors and share them across multiple applications. These are the elements that need to be aligned.
Establish a standard data model: A common data model defines the critical data elements’ structure, relationships, and definitions. This will serve as the foundation for metadata alignment across applications.
Define common metadata standards: Define common metadata standards for how the critical data elements should be represented, including naming conventions, definitions, and relationships.
Implement a metadata management system: Implement a metadata management system that stores and manages the common data model, metadata standards, and metadata for all applications.
Integrate with the applications: Integrate the metadata management system with the applications that use the critical data elements. This will ensure that the metadata for these elements is consistent across all applications.
Regularly review and update the metadata: Regularly review and update the metadata to ensure it remains aligned with the common data model, standards, and business needs.
Train employees and make the metadata accessible: Train employees on the common data model and metadata standards and make the metadata accessible to them so they can use it effectively.
Organizations can achieve metadata alignment across applications by implementing these steps, improving data consistency, quality, and governance.