According to Gartner, 80% of D&A governance projects will fail by 2027 because a genuine crisis has not been created. Linking AI models to suitable, real-time data is challenging for nearly 70% of organizations. Furthermore, approximately 65% of developers spend too much time developing distinctive connectors for every data source. These problems prevent innovation and reduce the value of AI systems.
According to Gartner, the global AI market in wealth management is projected to grow at a compounded annual Growth Rate (CAGR) of 20.4% from 2022 to 2027, reflecting the rapid adoption of AI technologies in this sector
Gartner predicts that 40% of AI-related data breaches will arise from the improper use of generative AI by 2027. The rapid evolution of generative AI technologies exceeds the development of essential data governance and security measures, leading to significant organizational risks.
Inadequate analysis of large amounts of data can cause serious problems for users and organizations, often leading to frustration and inefficiency. For example, a data scientist searching for the “best machine learning library in Python” might encounter irrelevant results focusing on basic Python classes rather than advanced libraries like TensorFlow or Scikit-learn.
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