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.
Photo by Austin Distel on Unsplash

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.

Meaningful Insights from Master Data Management Solutions

Data Management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. Effective data management is a crucial piece of deploying the IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning by corporate executives, business managers and other end users.
 
Strategic planning is vital to growth of every small business because this is an action plan on how to effectively assess the business direction. It is simply a clear vision of where the business at, and where it is going.
 
Financial projections and market analysis were crucial examples on what to include in a business plan. Writing down a business plan includes a layout of ideas, goals and objectives and on how to accomplish it and a path to do that. In this way,  business opportunities and challenges are carefully analyzed. False assumptions can lead to poor decision making that is why it is very important to utilized facts from accurate data to make more informed decisions that will lead to commercial growth.

Finding the right reporting tools and the right data integration platform can ease the burden of getting overwhelmed with a huge amount of information for your data driven decisions.This is where Master Data Management Solutions comes in. Through a wide choice of enterprise MDM solutions. Master Data Management Solutions (“MDM”) is a technology-enabled discipline in which business and Information Technology  work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.

Master data management can be viewed as a “discipline for specialized quality improvement” defined by the policies and procedures put in place by a data governance organization. It has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing master data throughout an organization to ensure a common understanding, consistency, accuracy and control, in the ongoing maintenance and application use of that data.
Processes commonly seen in master data management include source identification, data collection, data transformation, normalization, rule administration, error detection and correction, data consolidation, data storage, data distribution, data classification, taxonomy services, item master creation, schema mapping, product codification, data enrichment, hierarchy management, business semantics management and data governance.

Simply put, MDM offers a fast, affordable, and scalable way to build trusted foundation of data across the enterprise. Like that of gartner master data management that offers an easy and affordable solution in a shorter implementation times. This is the best way to get business insights on strengths and weaknesses that can later on optimize sales and profits.

With today\’s technology advances, obtaining data to solve business issues through MDM is a proven and effective way to draw meaningful insights.

Data sources like social media, point of sale, enterprise applications, and more becomes manageable when collected, analyzed, and leveraged in a single view. Thus, it improves decision making and execution in a fast and reliable approach. A big advantage that MDM offers above all is data trustworthiness. It supports business initiatives that can eliminate costly inefficiencies caused by information silos. In this way, duplication of effort, lack of synergy and missed opportunities can be avoided.
Overall,  developing a digital transformation strategy is the best gear to do business in this digital age.