In the past, Metadata Management is used to know how to use data catalog to find simple data or a book or a periodical in a library. However, today it is one of the most critical data practices for a successful organization dealing with data. With the rise of distributed architectures, including cloud & big data, metadata management is now critical for organization to manage.
So what is metadata management?
Metadata management is the proactive use of metadata to govern data in an organisation, allowing for well-informed business decisions and data handling efficiency. It involves ingesting metadata in order to learn about an organization’s data, its value, and how to optimise data storage and retention.
By having a metadata management system, organizations employees can add metadata into their repositories quickly and accurately without affecting the access of data within their systems. It improves creative workflows, thus enabling enhanced business processes. Managing metadata can be overwhelming when the right tools aren’t used. DQLabs.ai Metamanagement platform, comes in handy. This platform make it easier to manage metadata and provide security features that control content access and distribution, as well as tools to aid creative workflows.
What are the benefits of metadata management?
- Enchance data quality: through automation, all data issues and inconsistencies within an organization’s integrated data sources are captured in real-time, thus improving overall data quality. Data quality is gradually assured with the governing and operationalization of the data pipeline to the benefit of all data stakeholders.
- Faster project delivery timelines: By automating, accuracy level up to 70% ensure the acceleration of project delivery for data movement and deployment of projects. Automated metadata management gathers metadata from different data sources & maps all data elements from their sources to target and enhances data integration across various platforms.
- Enhanced speed to make insights: Now a days, data scientists spend up to 80% of their time to gather and understand data to resolve errors instead of analyzing it to draw real value. This time can be reduced greatly by the use of stronger data operations and analytics, leading to drawing insights faster, with access to underlying metadata.
- Improved productivity & reduced costs: Reliance on automated and repeatable metadata management systems and processes leads to improved productivity and reduced costs.
- Regulatory compliance: Data regulations, including the GDPR, HIPAA, and CCPA are to be complied with, depending on the area an organization is located and the type of operations they are engaged in. When critical data is not collected, cataloged, classified, and standardized in integration processes, compliance audits may be inaccurate. Metadata management ensures that sensitive data is automatically flagged and tagged, it is then automatically documented, and its flows captured so that it is easily noticed and its use across various workflows easily detected.
How can you successfully implement it?
A good metadata management implementation must include; a metadata strategy, metadata integration and publication, metadata capture and storage, and metadata governance and management. A Metadata strategy ensures the consistency of an organization’s entire data ecosystem. The metadata strategy defines why the organization is tracking metadata and lists all the metadata sources and processes used.
During the collection of metadata, be sure to identify all internal and external sources of the metadata that the organization seeks to collect. This can be achieved by the use of solutions such as metadata repositories, data modeling, and data governance tools.
Lastly, an organization needs a metadata governance structure, which entails a review of the responsibility, life cycles, and statistics of metadata and how different business processes integrate metadata.
Conclusion
Metadata management brings about business value, thereby improving innovation, collaboration and helps to mitigate imminent risks. Metadata management solutions like DQLabs.ai helps organizations to access high-quality and trusted data, in order to ensure that they get accurate insights from their data for optimal business goals.
