The Top 10 Metadata Management Platforms for Efficient Data Administration

Metadata management, lately, has emerged as one of the most important practices in organizations. The underlying reason being the increased usage of big data and cloud platforms, and the tendency of information to get scattered in the absence of efficient metadata management. This has necessitated the need for organizations to adopt the best in metadata management practices in order to manage the information assets within the. In simple terms, metadata management is the efficient administration of data that describes other data.

The most efficient metadata management platforms in the market are characterized by their ability to provide a user with easy access to information on key attributes in a user interface. The easier it is for a user to locate a data asset, the better the platform may be considered. Moreover, they also simplify sharing and accessibility of data for everyone in the organization.

On that note, let’s turn our attention to some of the best metadata management platforms available today.

Alation

Alation provides everyone in an organization with a single point of reference for all kinds of enterprise data, including data glossaries, Wiki articles, and business dictionaries. Its data catalog indexes data by source, while monitoring its usage, thereby giving users an accurate insight into its creation, usage, and sharing. Alation received multiple rewards for data cataloging in 2018, including inclusion in CRN’s 2018 list of emerging vendors. Recently, it was named as the top data cataloging solution by Dresner Advisory Services’ 2019 Wisdom of Crowds Data Catalog Market Study, for the third year running.

Alex Solutions

Alex Solutions is a household name when it comes to providing metadata management and data governance solutions to important business problems. It has been designed to enable everyone to securely discover, protect, and comprehend data. It differentiates itself from others by being technology agnostic and featuring a business glossary using which users can define key business terms corresponding to physical data assets, outputs, and processes. Besides that, it also comes with intelligent tagging that lets users add a business context to information assets. Gartner Risk Management calls it cool as a summer breeze , for its ability to visualize and understand information assets.

Io-Tahoe

Io-Tahoe believes in the automated discovery of data relationships throughout the organization. It helps its users in doing away with manual processes and grows into an insight-driven business.It helps users automatically discover data relationships in the organization, and offers greater control on the data landscape by letting them search the unknown dark’ data as well.It’s proprietary, advanced machine learning algorithms help organizations find even the most obscure data relationships in structured, semi-structured, and unstructured formats. It goes beyond the analysis of metadata, and provides users with a thorough analysis of all the data, thus bringing the absolute truth to light.

ASG Technologies

ASG Technologies helps organizations find an edge in the information economy by bringing in more than 220 big data and traditional sources. The tool is instrumental in automated data tagging due to its ability to match patterns, while also offering reference data integration and richer metrics. Currently, its customer list comprises over 3500 organizations, including Baylor Health Care System and Penn Medicines. ASG Technologies was included alongside big players like Amazon and Accenture in the seventh DBTA 100 list, in which the companies that matter most in data are featured. BIG Awards also named it the 2018 Enterprise Company of the Year.

Collibra

Collibra was the leader in Gartner’s Magic Quadrant. Considered as the metadata management solution of choice by one-third of the participants in the survey, Collibra has created a strong brand in the data governance sphere by allowing users to find, understand, and trust their data. Its data dictionary comprises all the technical metadata in the organization, as well as its relationship with other data, along with the format, origin, and use. According to a report by the International Data Corporation, organizations using Collibra take 69% less time to locate data and reports, have 23% higher gross productivity and record 28% lower frequency of data related errors.

Datum

Datum finds a niche in the metadata management space by defining itself as a blockchain data storage and monetization platform. It has been built to let users discover, comprehend, connect, and evaluate enterprise data. It uses a proprietary algorithm to combine different aspects of metadata management, like performance and policy management, data dictionaries, and business glossaries, into one central approach. This approach has been designed to directly impact businesses in terms of their goals and outcomes while also helping them drive business value. Sovren, one of Datum’s customers, finds value in Datum’s ability to put users at the center of their digital interactions.

IBM

IBM’s Infosphere Metadata Workbench has been adopted as an industry standard for a wide variety of data use cases. IBM has been known to be innovative in data analytics, governance, and stewardship under its Unified Governance and Integration Platform offering, and continues to do so. For example, developing new ways to manage data in Hadoop distributions, and using ML for automatic asset classification and tagging. Its efficacy lies in its ability to give a summary of the effects made in dynamic information management environments, through impact analysis.

Informatica

Informatica positions itself as the data innovation disruptor, making possible what didn’t exist before. It has a track record of being the best in customer loyalty for 12 years in a row, besides being number one in cloud data management with 5 million transactions per month. Its mind share in the market is ubiquitous, with a high likelihood of its emergence in competitive situations. Its speciality lies in being platform and application agnostic while offering a wide array of recognized information architectures. Informatica’s customer success stories include increasing American Equity Investment Life Insurance’s business in the broker-dealer space by 400%.

Oracle

Oracle Enterprise Metadata Management (OEM) is an all-inclusive metadata management platform that has the ability to extract and catalog metadata from a number of metadata providers, including Hadoop and ETL. However, it is not just a metadata inventory, but rather, it also allows the user to search and browse metadata while providing them with data lineage, impact analysis, semantic definition, and semantic usage analysis for every metadata asset in the catalog. Oracle’s recognition in the market for its expansive portfolio (addressing the problem of data integration in organizations) continues to catalyze its popularity in deployment situations.

Smartlogic

Smartlogic’s Semantic AI platform, Semaphore, is influential in helping organizations drive organizational initiatives, like customer service, lifecycle management, data analytics, information security, and regulatory compliance. It allows users to enhance data, harvest facts, and synchronize information resources; and features a model-based, rule-driven approach that improves the potential of existing technologies as well. It has taken great strides in the market by leveraging its partner network for driving sales, with a customer portfolio that extends across industries like healthcare, media, life sciences, financial services, and manufacturing. Smart logic was named as a leader in the 2018 Magic Quadrant for Metadata Management Solutions.

Due to the rise of big data in the past few years, there are numerous sub-categories that have emerged from the traditional data management industry. This includes numerous metadata and master data management tools, as well as data integration, data quality and governance, machine learning and data science, etc. Though not linearly related to each other, when seen through the bigger lens of big data analytics, there is certainly an inter-relatedness that exists among them.

The process of finding, analyzing, purchasing, and finally deploying a metadata management platform can be a complicated one. Therefore it is important for organizations to refrain from adopting a one-size-fits-all approach and instead turn their attention to the specific problems that need to be addressed. While the platforms listed here are leaders in the metadata management space, thorough research, though, is advisable.