--- title: 451 Report Discusses the Latest in Data Management description: The 451 Group’s Matt Aslett and Matthew Utter recently shared their insight on the latest advances in data management in a report image: https://blog.promethium.ai/hubfs/Imported_Blog_Media/file-Apr-26-2022-04-41-29-44-PM.jpg --- Dec 4, 2020 12:00:00 AM # 451 Report Discusses the Latest in Data Management [ Brett Arnott, VP Marketing ](https://blog.promethium.ai/author/brett-arnott-vp-marketing) Share: [Share this page on LinkedIn ](http://www.linkedin.com/shareArticle?mini=true&url=https://blog.promethium.ai/post/451-report-discusses-the-latest-in-data-management) [Share this page on Twitter ](https://twitter.com/intent/tweet?url=https://blog.promethium.ai/post/451-report-discusses-the-latest-in-data-management) [Send us an email ](mailto:?body=https://blog.promethium.ai/post/451-report-discusses-the-latest-in-data-management) The 451 Group’s Matt Aslett and Matthew Utter recently shared their insight on the latest advances in data management in a report that included survey data on the challenges data teams face. According to the report, becoming data-driven at the enterprise level is no easy task as companies continue to struggle with data silos, bureaucracy and difficulty in establishing common goals. To solve these issues, organizations are looking to the DataOps methodology, which involves creating a collaborative environment between data consumers and data operators. However, the current DataOps model has a few gaps before it can become a practical benefit. Consequently, nearly half (47%) of respondents to 451 Research’s Voice of the Enterprise: Data & Analytics 2H 2019 survey indicated that data analysts spend 50% or more of their working hours finding and preparing data for analysis. The report, which includes key data from this survey, discusses several technologies and approaches that will help to overcome this delay in data analysis. You can visit here for a full look at the [451 report on data management](https://bit.ly/35EWhzD). Here are some of the highlights: - Use of artificial intelligence (AI) and machine learning (ML) technologies, including natural language query and inference of metadata and lineage, with the ability to learn and adapt based on previous questions and queries - Virtualization to gather metadata across multiple data sources or from data catalogs - Data validation, including identification of duplicate tables and tracing the data’s lineage - Automated assembly of data from disparate tables, as well as autogeneration of the SQL statements used to retrieve those tables. - Ability to visualize relationships that exist within the data tables. - Ability to create a virtual view of data in multiple underlying data sources, which appears to BI tools (such as Tableau, Looker, Superset and ThoughtSpot) like a single database.   Did you know that Promethium offers a [Free Data Catalog](https://www.pm61data.com/free-data-catalog)? 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