Category: Data Gap

A significant portion of corporate data resides in unstructured formats, posing a challenge for traditional BI and ML pipelines. This unstructured data offers rich insights into customer sentiment, market trends, and operational inefficiencies. However, its inherent lack of standardization necessitates extensive pre-processing before integration into BI frameworks or training ML models.

The promise of artificial intelligence (AI) is tantalizing: increased efficiency, automated tasks, and data-driven insights that lead to better decisions. Yet, a startling statistic reveals a harsh reality – 73% of companies are not prepared for AI rollout due to a critical issue: the Data Gap.