The need for high-quality data for AI applications is emphasized in the recent McKinsey report, led by Joe Caserta and Kayvaun Rowshankish. The report identifies a growing pressure to utilize generative AI, but emphasizes that inadequate data readiness will hinder business success with generative AI. The report advocates for developing a clear understanding of the data implications of generative AI, suggesting that businesses will need to invest in data labeling and tagging strategies. This shift in data concerns requires organizations to reassess their data architecture to support generative AI initiatives.
Industry leaders are increasingly expressing concerns about enterprises’ ability to handle large data influx necessary for managing generative AI challenges. The proliferation of devices and cutting-edge technologies across all departments is contributing to a remarkable expansion, requiring businesses to develop strategies and adopt advanced technologies to ensure that data remains an invaluable asset.
To prepare for the AI-driven era, the report recommends considering elements such as establishing a data governance strategy, setting up a data storage strategy, ensuring data quality, measuring progress, addressing unstructured data capabilities, building data architecture to support broad use cases, and employing AI to aid in managing data. The use of generative AI can accelerate tasks and improve the entire data value chain, from data engineering to data governance and data analysis.
In conclusion, while AI holds great promise, it is essential to have well-managed data to achieve the desired outcomes.