Yes: despite Generative AI, you sill need a Data Strategy
Some individuals in leadership positions may advocate for discarding their data strategy due to the emergence of Generative AI: Just think twice before dropping it altogether.
While Generative AI may operate effectively without extensive training data in certain scenarios, for large-scale business operations, particularly within specialized sectors such as healthcare and government, fine-tuning Large Language Models (LLMs) remains essential to achieve specific objectives.
This fine-tuning process needs access to relevant data. Yet, acquiring this data requires thoughtful planning to determine its nature, acquisition methods, and long-term storage for various projects. Moreover, data acquisition can present challenges, particularly when bureaucratic procedures are involved. Integration of data from diverse sources or development of new user interfaces may be necessary to facilitate direct data collection.
However, beyond fine-tuning LLMs, data plays a crucial role in leveraging machine learning models for informed business decision-making. Whether it's predicting market trends, streamlining operations, or enhancing customer experiences, data-driven insights are fundamental.
Bottom line: maintaining a robust data strategy is still necessary! Failure to do so may result in skilled professionals leaving due to insufficient data resources, and AI projects getting delayed due to inadequate data collection planning.
