This article examines the high rate of unused and broken dashboards in organizations, highlighting how they often fail to provide lasting value. It discusses the disconnect between dashboard creation and actual usage, driven by shifting priorities and limited attention spans within teams. The piece also touches on the implications of this phenomenon for organizational behavior and project management.

• About half of an org's dashboards audited turned out to be broken or ignored, raising the question of why they were built at all.
• Dashboards act as "grave markers" for past priorities—built for a moment of attention that fades as new projects compete for focus.
• Letting teams self-manage their own dashboards helped somewhat, but didn't solve the core problem since attention still shifts away as new initiatives emerge.
• Organizational attention is a genuinely limited resource (with academic grounding), so strategic planning that overcommits people guarantees old dashboards get abandoned.

SoccerData is a toolset for scraping soccer data from various websites like ESPN and FBref, providing users with structured Pandas DataFrames. It allows for easy access to game schedules and player statistics, while emphasizing the importance of responsible usage and compliance with website terms. Users are encouraged to contribute to the project and report any issues due to potential changes in the source websites.

• SoccerData scrapes and normalizes soccer data from eight sources (Club Elo, ESPN, FBref, Football-Data.co.uk, Sofascore, SoFIFA, Understat, WhoScored) into consistent Pandas DataFrames with matching column names and IDs across sources.
• It caches downloaded data locally to avoid redundant scraping.
• Because it depends on scraping live websites, changes to those sites can break functionality, so the project relies on users reporting issues and contributing fixes via pull requests.
• Users are responsible for using it ethically and complying with each source website's terms of service, as it's provided without warranty.

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• The article content is corrupted or encoded and cannot be read.
• No actual claims, data, or findings could be extracted from the source material.