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The Big Data world is oozing with opportunities and ongoing tech advancements. Year-on-year, the world has experienced a growth in data generation like never before. Today, the numbers for business data generation stand at an all-time staggering high. This has merged well with the desperate hiring procedures for qualified data science professionals as well. This yields a clear-cut way ahead for targeted data-driven decisions and an insightful business amplification worldwide.

The new in town is Meta Data! Yes, you read that right! Years gone by have experienced a growth in data-driven decision-making with the global big data analytics market expected to grow at a CAGR of 13% through 2032, reaching USD 924.39 eventually ( Further, the Harvard Business Review Survey goes on to reflect that 80% of respondents rely on data in their roles, and 73% rely on it to make decisions. Isn’t that a staggering number to put your stakes on?

Let us get to the bottom of Meta Data; revealing data science in operations management, and what it has in store to offer for the data science industry at large and in world business order.

What is Meta Data?

Simply and most widely put, Meta Data is ‘data about other data.’ Metadata describes the other data. It supports connected data pieces, including video, photographs, web pages, content, and spreadsheets. Metadata summarizes basic information about data such as the type of asset, author, date created, usage, file size, and more. Most of the business interactions are in the format of unstructured data, which makes sorting and defining the data a time-consuming expensive affair. Metadata can be a boon here!

Meta Data in Big Data Management- What is the hype about?

Metadata management is critical to understand the depths of your data. The right metadata management tools can help improve data quality and relevance. It shall allow enough room to unravel the data’s complete business value. Using metadata can help in:

  • Discovering data
  • Understanding data inter-relationships
  • Tracking data usage path
  • Assessing the value and associated risks with data usage

Metadata is a critical pivot in effectively managing the big data industry while attending to the following types of metadata:

  • Technical metadata
  • Business metadata
  • Operational and infrastructure metadata
  • Usage metadata

Metadata can flag missing or incorrect data, and automatically corrects and enriches the metadata. It assists in improving the quality of analytics, avoids costly mistakes, and enhances data-driven decision-making. Metadata has the power to strengthen major components of data science while delivering results. The more robust your metadata, the quicker your team will be able to extract actionable information and make quick business decisions. Metadata supports data consistency across an enterprise and enables associations between datasets for high-quality results.

Meta Data Management- Mechanism:

Collect>> Curate>> Infer

The metadata management process is guided by the three core steps including collection, creation, and deriving inferences from the collected data.

  • Collect: Scan metadata from enterprise data systems across the cloud and on-premises such as data lakes and warehouses.
  • Curate: document the business view of data with the terminology and involve augmentation of collected metadata with this business context.
  • Infer: This involves applying intelligence to derive relationships not obvious in the collected metadata; including data provenance and data lineage.

Well-defined metadata structures Characteristics:

  • Metadata syntax is defined by the markup or programming languages used.
  • Schemata help in defining relationships between elements.
  • When creating a metadata taxonomy, it is important to consider the level of granularity or detail.
  • Hypermapping in metadata is the geospatial element that serves special views and accounts for applying real-world complexities.

Meta Data Management- 7 Best Practices:

  1. Building a unified metadata ground
  2. Harnessing the full potential of all metadata categories
  3. Applying AI/ML to activate metadata
  4. Leveraging AI-powered data catalog
  5. Ensure the scale of metadata management
  6. Enabling AI/ML model governance
  7. Developing a metadata stewardship program

Final word:

Looking at the expanse at which the data is growing, it is essential to cool off the burden by utilizing metadata management. Data science in operations management has a massive role to play and acts as a pivot in evolving and churning out effective data-driven decisions for organizations. Make way for qualitative addition in your enterprise management with quality metadata deployment today!


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