This case study highlights how a major US media company overcame challenges with their data strategy and improved database performance by 40%. Initially, the company faced issues in managing data from over 300 different sources and struggled with outdated models and inefficient feature usage. To address these issues, a new team was brought in to assess the existing structure and recommend a new data architecture, including a new feature store architecture. The team refactored the existing store into this new structure, which encompassed over 20,000 features. This strategic overhaul led to a significant 40% improvement in feature generation performance from each data source, resulting in increased business satisfaction and user trust in the data.

Our client, a prominent media company, was grappling with the challenge of consolidating information from over 300 disparate data sources. Their models were often deprived of timely data inputs, affecting their efficacy. Moreover, the diverse range of models employed across their business operations faced inconsistent feature usage and recurring feature creation, leading to operational inefficiencies. The process was further complicated by the unstructured use of data and features, exacerbating inefficiencies.

Our client saw a remarkable 40% enhancement in feature generation across all data sources. This optimization, brought about by the revamped feature factory, not only boosted business satisfaction but also bolstered user trust in the data.

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