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Need of Data Catalog for the Digitally Transforming Organizations.

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In recent years, organizations have been increasingly focusing on digital transformation initiatives to stay competitive and improve efficiency in the market. They have started adopting digital transformation that involves technologies and services to fundamentally change the traditional way an organization operates and delivers value to its customers.

Data lies at the heart of all digital transformation initiatives. It helps organizations better understand their customers, improve products and services, drive operational efficiencies, and reduce risk. But to achieve those goals, data needs to be managed as an asset. Just like physical assets (property, plant, and equipment), data needs to be properly maintained. Organizations need processes in place to assure data quality and protect data from misuse. They need to maintain a deeper and richer understanding of how data flows through an organization. In addition to this, there is also a recurring disconnect between IT and business. It doesn’t understand data from a business standpoint, while business users may struggle to leverage IT data tools for valuable business analysis. To achieve successful management of enterprise data, it is crucial to establish formal alignment between divergent lines of business, technology, and processes through data governance to create a comprehensive data catalog.

Data analysts working with data without a data catalog means they must go through the data in the organization, which can be incomplete, inaccurate, outdated, and incomprehensible because of the technical language used as per business terms, which means these data is tribal knowledge in the organization. The process of analysis begins with the tribal knowledge of finding the data, which then is retrieved to the desktop for validation for accuracy and evaluation. This process is repeated until the document is fit for the process. Then the analyst understands and prepares the dataset for analysis, realizes the data is inadequate, and repeats the whole process by trial-and-error method on the data. It has been researched that these techniques take about 80% of the analyst’s time in finding data and just 20% in the analysis.

Data catalog, on the other hand, needs to be metadata-managed with tagged information with search terms. Analysts don’t need to evaluate data on their desktops; instead, they can rely on metadata, curator annotations, ratings, reviews, and documentation to assess data suitability for analysis. If data is inadequate during analysis, analysts can quickly search and add more data. The data catalog will help with faster processing and efficiency in finding the data, and the analysts will be of high confidence in working on the right analysis. Data catalog lets data analysts work on analysis 80% of the time and 20% on the data finding.

Introduction to Data Catalog

Intelligent Data Catalog (IDC) is a comprehensive metadata repository that creates and maintains an inventory of data assets in an organization. It includes AI and ML models, unstructured and structured data, lineage, classification, data queries, reports, visualizations, and dashboards. The intelligent data catalog is a scalable solution and provides automatic scanning of a wide variety of data sources to discover, classify, and catalog enterprise data assets. IDC helps data professionals to connect, collect, discover, organize, access, understand, consume, and enrich data in a better-structured manner.

Data catalog in an enterprise organizes and catalogs data for easy access and use. This helps in solving problems on data silos and fragmented data by providing a central location where all data can be easily accessed and searched by all users. It offers the means to manage metadata and curate the necessary information to make data assets easier to discover, manage, and consume. In doing so, data catalogs have become an essential component of enterprise data architectures.

The Process of Cataloging and Curating Information for Enhanced Data Discoverability.

Metadata from various dimensions can be collected by using algorithms and machine learning (ML) techniques. It uses algorithms to automatically extract metadata from the data that is to be cataloged and uses ML to find patterns and relationships in the data. For example, using algorithms will automatically extract metadata such as the date, location, and size of a file, while an ML model is trained to identify the content of the file and generate metadata based on that content. These data need to be tagged based on semantic inference by the algorithm, for example, customer, product, partner, financial, and many more. This tagging can be automatically tagged with search terms as a part of the ML process, and the algorithm can be trained to better tag data. Additionally, it will analyze which data are sensitive data, compliance data, and which support governance.

Data search is typically conducted using natural language queries with terms, keywords, and facets, allowing for more accurate retrieval of relevant data. The data catalog will also generate the protocol attached to the document with access required if needed and sensitive data compliance with the policy. Data curation is more about managing the data, organizing and maintaining data over time. It involves storing data and shared databases. It also involves sharing knowledge through catalog metadata.

Data curation can involve cleaning and organizing the data, as well as ensuring that it is accurate, up-to-date, and relevant to the users of the data catalog. Data curation also involves maintaining the data catalog itself, ensuring that it is organized and easy to navigate, and collecting tribal knowledge about data through collaboration. This can involve developing and implementing policies and procedures for adding and updating data in the catalog, as well as monitoring the catalog for any potential issues or problems. It also involves sharing datasets that are not part of a data warehouse or data lakes, such as files or workbooks created by self-service data consumers.

The Role of Augmented Data s Intelligent Data Management

Augmented data catalogs have emerged as invaluable tools that empower intelligent data management, providing advanced features and functionalities to enable organizations to search, discover, and utilize their data more intelligently. The metadata repository of a data catalog is the foundation that enables the catalog to provide intelligent data search capabilities. It is impossible to catalog data assets, and the metadata repository acts as a system with an organized and structured manner of all metadata, making it easier for the catalog and allowing users to find and access data assets. This would be possible only by activating metadata with augmentation and have modernized the features of the data catalogs.

Key Components Empowering the Augmented Data Catalog

  • Automated metadata extraction: Use of native connectors and integrated platforms to extract metadata from various data sources such as enterprise data warehouses, data lakes, operational databases, enterprise applications, cloud data stores, and non-relational data stores. It can extract metadata such as data schema, table names, column names, data types, and relationships between tables, among others. It is then stored in a centralized repository, which serves as the foundation for the data catalog. Automated extraction of metadata reduces the manual efforts involved in extracting metadata from various data sources. It ensures that the metadata collected is accurate and up to date, as there is a standardized way of extracting these data. These collected data are governed and updated with any changes in the system. This ensures that the metadata is consistent and can be easily integrated into the data catalog.
  • Automated Data discovery: Data catalog automatically crawls, profiles, organizes, links, and enriches all your metadata with natural language processing (NLP), machine learning (ML), and AI algorithms to automatically scan and analyze data assets across an organization’s systems. This includes structured data from databases, unstructured data from documents, and even data from cloud-based applications. This process helps to create a comprehensive and up-to-date inventory of an organization’s data assets, which can be used to facilitate data discovery and analysis. By using AI and ML, the data catalog can also provide insights into how data is being used, identify potential data quality issues, and suggest data assets that may be relevant to specific business use cases.
  • Data classification: The data catalog utilizes metadata and business definitions to automatically classify data as it is ingested. This process involves defining classification categories and criteria, which are then applied to each data asset in the catalog. Data stewards can manually perform this task, or ML algorithms can automatically analyze the content of the data and assign the appropriate classifications. This will ensure that data in the system is accurately classified, which makes it easier for users to discover, access, and utilize the data. Data classification also includes identifying and categorizing personally identifiable information (PII) data, such as names, addresses, and social security numbers, to ensure that this data is properly protected and managed. This involves applying additional security controls and access restrictions to PII data and ensuring that appropriate data handling and storage procedures are in place.
  • Data profiling: Automated data profiling in a data catalog is scanning through the enterprise data using ML algorithms to analyze the metadata and content of data assets in the catalog. The algorithms identify patterns, relationships, anomalies, and data quality issues, such as missing values, outliers, and inconsistencies, in a detailed format. It also provides data stewards insights to take action to improve data quality and consistency. By skimming and analyzing the repository data, data stewards can understand the structure, quality, and relationships of the data, helping them make informed decisions for improvement. With the usage of ML algorithms, it can automatically scan the metadata and content of data assets in the catalog. The algorithms can identify patterns, relationships, anomalies, and data quality issues, such as missing values, outliers, and inconsistencies.
  • Data curation: Data curation is the process of collecting and managing data entities into specific groups. It involves the selection, organization, and maintenance of data to ensure its accuracy, completeness, and reliability. This includes activities such as data cleaning, transformation, and integration to improve data quality and usability. Automated data curation is facilitated by an augmented data catalog and metadata. It enables the automatic identification of duplicate data, assigning classifications and tags to data assets, and the identification of relationships between them. This helps to reduce the manual efforts required to manage data and can help ensure that data is properly organized and easily discoverable by users. It can also help to improve data quality by identifying and addressing issues such as data inconsistencies or missing values.
  • Semantic search: Semantic search in a data catalog uses NLP and ML algorithms to understand the meaning and context of search queries and data assets. By analyzing past usage and behavior, the search engine can predict and suggest relevant results to users based on their search history. This allows users to find relevant data assets even if they don’t know the exact terminology or phrasing used to describe them. Semantic search can help improve data discovery and accessibility, as well as facilitate collaboration and knowledge sharing within an organization.
  • Automated end-to-end lineage: End-to-end lineage enables organizations to track and understand the complete lifecycle of their data. It utilizes the collected metadata repository to extract about the data assets and their relationships across the entire data supply chain, from the point of origin to the point of consumption. This metadata includes information about the source system, the data transformations and processes applied to it, and the destination of the data. By tracing the lineage of data, organizations can ensure data quality and compliance with regulations and policies, as well as improve their decision-making by understanding the impact of changes made to the data at each step of the process. In addition, lineage plays an important role in complying with data privacy regulations such as GDPR or CCPA, as it allows organizations to track the use of personal data and demonstrate accountability to regulatory bodies.
  • Embedded governance in the catalog: A data catalog enables users to find the data in the enterprise. By embedding governance policies and rules into the catalog, data users can be assured that the data they access and use is compliant with organizational policies and regulations. This helps to reduce the risk of data breaches or other compliance issues. Additionally, by providing a centralized location for data discovery and access, the catalog can help to eliminate silos and promote collaboration across departments and teams.

Intelligent Data Catalog Revolutionizing Data Management in the Market

An augmented data catalog is a solution that will solve various data management issues in an enterprise. It provides features with an easy, automatic way to consolidate data inventory and contextualize data to access and use. An augmented data catalog is a solution that can solve various data management issues in an enterprise. Today, data management vendors have evolved to focus not only on providing data catalog as a standalone solution but also on enabling businesses to discover, understand, govern, collaborate, and consume data. Investing in an augmented data catalog with enterprise-grade capabilities is an important step in this direction. An augmented data catalog can help democratize enterprise data, making it easier for all stakeholders to access and utilize data for their needs. Overall, an augmented data catalog is an important tool for businesses that want to transform their data management strategies and make data-driven decisions.

Author : Sreejith PS Analyst at Quadrant Knowledge Solutions

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