Fashion Journo Requests

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Journalism Professionals for Fashion Industry Gender Reporting Study

*Fashion Reporting Research Outreach* Hello all! As I get closer to graduating with my master's in May 2026, I am looking for outreach with my thesis research. I am a journalism graduate student at the University of Arkansas, conducting a research study analyzing the different writing, editing and pitching techniques publications use when reporting on gender hierarchies within the fashion industry’s top creative designer positions. The purpose of the study is to better understand how different editorial publications may approach the discussion of fashion industry accountability based on established relationships within the industry. I am looking to interview journalism industry professionals (reporters, editors, copywriters, CEOs etc.) on their experience and insight on pitching, reporting, or editing articles on the lack of women in top creative designer roles in the fashion industry and overall industry accountability. If you or anyone you know may have valuable insight in my research findings, I would love to connect to discuss setting up an interview! Interviews will be one hour long, preferably in person or over Zoom. Participants will not be compensated. If you are unable to help in this study but know other industry professionals who fit the criteria, I’d greatly appreciate you sharing their name and contact information with me or resharing this post. If you have any questions, you can reach me here, by my email [email redacted]. Thank you!

Data Engineering Experts on Decade-Long Industry Constants

5 things that still remain true for data engineers and teams even after a decade. 1. Dashboards And Queries Are Still Slow - This one really surprised me. I am still coming across companies with minute long queries and dashboards that they are servicing to the business. As one person told me, if it takes more than 45 seconds, people will assume it’s broken. 2. Data Modeling Remains Essential - Some companies have used new methods to manage and model their data  as Zach Wilson brought out with his recent article. But generally speaking, getting all your data in a central location for analytics in an organized fashion that a wider variety of end-users can understand and work with is needed. 3. Batch Processing Is Still The Backbone Of Most Data Platforms - The tools we have to ingest data in real-time are far easier to set-up today but batch still tends to be the one most data teams rely on. Most of the time, because it’s all they need. 4. Business Alignment Trumps Tools - Tools solve a lot of technical problems and they have made most of our lives so much easier. But if you're just adding tools to your data stack for the sake of it, you're going to have a bad time. 5. Data Quality and Validation - And of course, there is still a need to ensure your data makes sense. I don't think data teams can just throw their hands in the air and always blame the data. We need to have systems in place that help us better validate whether data is good or bad before it's pushed downstream. I am working on an article about what I have seen actually change. But I'd love to hear from you on both sides. What remains the same and what has changed?

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