Types, Functions, and Benefits of A Data Catalog

Data catalogs are relatively a core component of data management tools. Thru this, automatic metadata management were enabled and molds with user-friendly interface that makes data easy to understand even for non-IT members of a business organization.

Metadata is the core of data catalog. It is the data that provides information about other data. To simplify, it is “data about data”. Many distinct types of metadata exist, including descriptive metadata, structural metadata, administrative metadata, reference metadata, statistical metadata and legal metadata.

  • Descriptive metadata is descriptive information about a resource. It is used for discovery and identification. It includes elements such as title, abstract, author, and keywords.
  • Structural metadata is metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters. It describes the types, versions, relationships, and other characteristics of digital materials.
  • Administrative metadata is information to help manage a resource, like a resource type, permissions, and when and how it was created.
  • Reference metadata is information about the contents and quality of statistical data.
  • Statistical metadata, also called process data, may describe processes that collect, process or produce statistical data.
  • Legal metadata provides information about the creator, copyright holder, and public licensing if provided.
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Every organization seeking competitive advantage uses data catalog to turn big data into actionable insights. Data transforms meaningful customer insights to improve outcomes. In today’s rapid pace, managing massive data amounts is doable with the use of such data governance tool.

A tool where everyone in a business can find the data needed for collaboration is essential. A modern data catalog includes many features and functions that all depend on the core capability of cataloging data—collecting the metadata that identifies and describes the inventory of shareable data.

It is considered impractical to attempt cataloging manually. Automated discovery of datasets, both for initial catalog build and ongoing discovery of new datasets is critical. The use of AI and machine learning for metadata collection, semantic inference, and tagging, is important to get maximum value from automation and minimize manual effort.

With robust metadata as the core of the data catalog, many other features and functions are supported. The most essential functions includes:

  • Dataset Searching—Robust search capabilities include search by facets, keywords, and business terms. Natural language search capabilities are especially valuable for non-technical users. Ranking of search results by relevance and by frequency of use are particularly useful and beneficial features.
  • Dataset Evaluation—Choosing the right datasets depends on the ability to evaluate their suitability for an analysis use case without needing to download or acquire data first. Important evaluation features include capabilities to preview a dataset, see all associated metadata, see user ratings, read user reviews and curator annotations, and view data quality information.
  • Data Access—The path from search to evaluation and then to data access should be a seamless user experience with the catalog knowing access protocols and providing access directly or interoperating with access technologies. Data access functions include access protections for security, privacy, and compliance sensitive data.

A robust data catalog provides many other capabilities including the need for data curation and collaborative data management, data usage tracking, intelligent dataset recommendations, and a variety of data governance features.

Benefits of a Data Catalog

  • Improved data efficiency

This helps the business more cost-efficient in deriving effective data-driven decisions to boost performance thru real-time data analytics.

  • Improved data context

Gaining better data understanding to put things into perspective is a great way to visualize circumstances that surrounds each metric. With this, the context turn facts into actionable information leading to a well-informed decision for a positive business impact.

  • Reduced risk of errors

The manual processing of errors is time consuming and can cause massive data confusion. Whereas with investing in data governance, an organized and improved inventory is presented with high quality and confidence.

  • Improved data analysis

Correct data and analysis is provided for deeper, more informed insights. The rich context captured of enterprise data, including relationships between data sets, analyst usage & trusted comprehension is highly accurate.

Overall, data catalog dramatically improves the productivity of analysts, increases the reliability of analytics, and drives confident data-driven decision-making while empowering everyone in your organization to find, understand, and govern data.

The Use of AI for Materials Inventory and Data Management

Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning and other artificial intelligence technologies to automate the identification and resolution of common information technology issues. 

The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor assets and gain visibility into dependencies without and outside of IT systems.

Information Technology plays a major role for a business to save more time and money, helping employees more productive and draws more business. In this case, we can clearly states that IT is not a business plan but rather, it strongly \”supports\”a business plan.

For modern business, having a high quality data is essential for making decisions, help boost lead conversions, and drives success in inventory management.

A data driven-environment\’s common data quality issues are poor organization, incorrect and poorly defined data, data inconsistency, and poor data security. Validating and cleaning bad data records equates to costly data cleansing tactics that threatens business efficiency.

When it comes to materials management optimization, obtaining an AI-enabled platform can quickly give a solution in providing inventory visibility and rebalance. It is a promising alternative compared to traditional cleansing methods.

This platform is widely used by businesses that maintain  and operate big data to achieve operational excellence. With the use of  AI platform, it is mostly possible to  ensure efficient operations. Businesses mostly in Maintenance Repair and Operations (MRO) use this platform solution  to keep an on-site inventory of the most commonly used items that can reduce downtime, extra expenditures, and the general stress of restocking. Industries mostly in Automotive, Industrial Equipment, Utilities, CGS, Aerospace, and Electronics, mainly uses this AI platform to optimize inventory management.

Wikipedia\’s concise definition of inventory optimization is that \”It\’s a method of balancing capital investment constraints or objectives and service-level goals over a large assortment of stock-keeping units (SKUs) while taking demand and supply volatility into account.\”

Artificial Intelligence is transforming the way we work, live, and collect data and it can help leverage technology in many ways to improve the MRO industry. It\’s an effective way to utilize the use of  technology or devices to act smarter than humans.

Here\’s the Top 5 substantial Reasons to use MRO Optimization:

1. To help avoid the risk of overpaying and under performing.
2. Effectively determines stock levels.
3. Balance demand and supply.
4. Increase service and safety levels.
5. Provides decision support system.

To be able to compete in dynamics market, relying on data stewardship is necessary to speed and scale a business. At this time, trusting manual process doesn\’t makes sense if an organization aims for operational excellence and continually improve the consumer/partner experience.

With the right tools and technology, supported with the right MRO process and best practices, organizations can digitally transform. Leveraging data for more strategic, creative opportunities driving to advanced supply chains forward.

It is important to develop a full understanding of what specific MRO the company needs, to be able to efficiently foresees and monitor the inventory, suppliers, cost and services. To thrive, it is crucial to have positive mindset towards innovation and technology.

Again, with the right tools and with the right kind of help, inventory and performance efficiency can be achieved rather than relying on manual approaches that are prone to error.