Part 2/9:
The session opened with reflections on women's journeys and aspirations for progress, setting the tone for a forward-looking, innovative mindset. While themes of empowerment and transformation were highlighted, the core focus soon shifted to the technical and practical challenges faced by data teams—namely, data quality.
The speaker pointed out that amidst advancements like deep learning and robotics, a persistent and fundamental problem remains: much of the data collected is flawed, incomplete, or inconsistent. These defects hamper the effectiveness of sophisticated tools and models, essentially constraining the potential of Business Intelligence (BI) and AI initiatives.