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ASG Announces New Model for Data Intelligence

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ASG Technologies, at its annual EVOLVE customer conference held in Florida last week, announced a dynamic new trust scoring capability for ASG Data Intelligence, its metadata management solution, designed to help Chief Data Officers provide self-service access to trusted data.

With a (patent pending) trust model, the new solution will help data consumers to identify and understand available data and evaluate its potential fit and value for both human and artificial intelligence/machine learning-driven analytics.

As part of its process to develop the new product, the firm conducted market research to explore how organizations think about trust, which factors are the most objective and relevant for measuring trust, and which barriers make understanding data difficult.

Trust historically relies on data quality measures, stakeholder collaboration and crowdsourced reviews. While relevant, this can lead to a partial and even biased understanding of data, where the business impact of trying to use data that is of poor quality or fit isn’t realized until well into the analytics.

“Our analysis concluded, among other things, that trust is multi-faceted, it’s organic and evolves over time, and a “one size fits all” approach for enterprises and their data is impractical,” says ASG.

Instead, the firm attempted to define a next-generation trust model for data understanding – where a data item’s trust is dynamically computed based on the value of one or more facets whose values are determined by logic and metrics within (or external to) the Data Intelligence solution.  Trust scores are measured over time and rich policies drive automated actions such as instantiating workflow when a score falls below (or rises above) a threshold.

“Data management leaders are investing in people, processes, and technologies serving both offensive and defensive data strategies,” says Marcus MacNeill, Senior Vice President of Product Management at ASG. “Data consumers, from data analysts to data scientists with diverse analytics goals and data literacy skills, need to confidently understand and assess available data.”

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