David Menninger's Analyst Perspectives

Alation Helps Organizations Get More Value From Data

Posted by David Menninger on May 20, 2021 3:00:00 AM

Alation recently announced the release of its 2021.1 version, introducing new data governance capabilities, enhancements in search and discovery through data domains, and extended connector and query coverage for data sources. Alation’s new federated authentication enables users to query cloud services such as Amazon Web Services, Snowflake, Tableau and more, using a single sign-on. The release also includes a Search application programming interface that allows for the integration of Alation Search with third-party tools. And, with the addition of the Open Connector Framework software development kit in the 2021.1 update, Alation enables organizations to create connectors for data sources not already supported by Alation.

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Topics: Analytics, Business Intelligence, Collaboration, Data Preparation, Data, Information Management (IM), AI and Machine Learning

Why Your Data Lake Needs Bad Data

Posted by David Menninger on May 13, 2021 3:00:00 AM

Everyone talks about data quality, as they should. Our research shows that improving the quality of information is the top benefit of data preparation activities. Data quality efforts are focused on clean data. Yes, clean data is important. but so is bad data. To be more accurate, the original data as recorded by an organization’s various devices and systems is important.

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Topics: Data Governance, Data Preparation, Information Management, Data, data lakes

DataRobot Automates and Simplifies AI/ML

Posted by David Menninger on May 5, 2021 3:00:00 AM

Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML. Traditional ML model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. And as the number of ML models grow, their management becomes difficult. By bringing automation to ML, organizations can reduce the time it takes to create production-ready ML models. AutoML can also enable organizations to make data science initiatives more accessible across the organization.

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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning