Understand the Maturity Level of Metadata Management

In this digital era, Metadata management [MM] has suddenly become a business necessity. Organizations and people are more and more interested in digitalization and so exponential data growth is flourishing. Metadata is associated with data or information that offers an insight into the characteristics of described data. Metadata includes –

  • Name
  • Purposes & meaning
  • Definitions
  • Descriptions
  • Classifications
  • Relationships with other data assets
  • Ownership or access approval data
  • Technical attributes
  • Lineage
  • Quality statistics and scores
  • Version data
  • Location
  • Other attributes

Metadata management discipline means systematically handling the metadata across its life cycle.

Benefits of metadata management

  • Roles and responsibilities associated with data assets are well defined.
  • Better compliance and security.
  • Increases accuracy and trust in the business data.
  • Increases dexterity in data-related niches like data analytics, engineering, science, etc.
  • Cost of hiring, training, and transfer of knowledge cost is reduced.

Improper metadata management stance will hinder the organization’s ability to extract and maximize the value necessary for business development. So, approach EWSolutions for your company’s data management consulting. The professional consultant will offer knowledge about the maturity levels of metadata management.

Maturity Level 1 – Unawareness

Business owners are unaware of metadata management discipline. It can be called documentation and is performed irregularly. The maintenance process is manual, so is error-prone and time-consuming. Non-technical users find the process mysterious.

Maturity Level 2 – Some awareness

The organization gains some awareness regarding MM and how essential it is to their business. Basic concepts are made known but they don’t have methodologies and tools to handle the metadata at this level. They use simple tools like spreadsheets, databases, wiki pages, etc. to identify, gather, organize, and manage metadata.

Maturity Level 3 – Split

At this maturity level, organizations can recognize how MM helps to enhance effectiveness and efficiency. Unfortunately, IT silos or business units decide their personal MM systems and liabilities without any consideration about the wide organizational needs. This splits the metadata management process.

There is a need for enterprise-level data governance or management program because without a clear definition of who is liable for what, there can be a misrepresentation. At this level, organizations are mature enough to detect that MM needs a coordinated and holistic effort across different departments in an enterprise. Data is shared among different departments without any standard approach regarding metadata management. Therefore, even if you introduce automated MM there will be splits. There are advanced companies that introduce quality metadata metrics at this level.

Maturity Level 4 – Enterprise-level understanding

Metadata management importance is recognized at the highest level. There is a chief data office and enterprise-level DM team. At this level, MM processes and tools are used across the enterprise. More and more employees identify the strategic significance of metadata. It becomes a common workplace language. Without human intervention, metadata is discovered and scrapped. Plenty of lineage data is captured in both ways – manually and automatically. The quality of metadata is systematically measured and reported at C-level.

Maturity Level 5 – Planned and Calculated Use

An organization starts treating metadata as strategic information and stores it in a single repository. There is a universal metadata format employed for better integration in every process and system. An external data is first adapted into internal metadata standards before storing. Metadata standards are strict and their quality is managed meticulously.

Metadata management maturity helps to define how well your business knows about its data and how good is it in processing and extracting value. This helps to build a reliable and consistent data asset.

 

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