In the past, Metadata Management involved just knowing how to use a data catalog to find simple data or a book or a periodical in a library. However, metadata Management is today one of the most critical data practices for a successful digital strategy in any organization dealing with data. With the rise of distributed architectures, including cloud and big data, metadata management is now critical in the management of data in organizations. So what is metadata management?
Metadata management is the proactive use of metadata in an organization to govern data in order to enable well-informed business decisions and efficiency in data handling. Metadata management involves ingesting metadata to learn about the data an organization owns, its value, and the optimization of data storage and its retention.
Why an organization needs a metadata management system
By having a metadata management system in place, an organization is able to have its employees add metadata into their repositories quickly and accurately without affecting the access of data within their systems. Metadata management improves creative workflows, thus enabling enhanced business processes.
Managing metadata can be overwhelming when the right tools are not used. Digital asset management (DAM) platform such as DQLabs.ai comes in handy. Digital asset management systems facilitate metadata management and offer security features that control content access and distribution as well as tools to support creative workflows.
What are the benefits of metadata management?
Enhanced data quality
through automation, data quality is gradually assured with the governing and operationalization of the data pipeline to the benefit of all data stakeholders. All data issues and inconsistencies within an organization’s integrated data sources are captured in real-time, thus improving overall data quality. This also increases the time taken to draw insights from the data.
Faster project delivery timelines
By automating metadata management, greater accuracy levels of 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 and maps all data elements from their sources to target and enhances data integration across various platforms.
Enhanced speed to make insights
Currently, data scientists spend up to 80% of their time gathering and understanding data and resolving 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.
Data regulations, including the General Data Protection Regulation (GDPR), and the Health Insurance and Portability Accountability Act (HIPAA), and The California Consumer Privacy Act (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.
Metadata management enables knowledge of what data exists and its potential value, thus promoting digital transformation through;
How can you successfully implement metadata management?
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.
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.