Generative AI Data Lake

Disparate data sources create information silos, hindering the ability to gain a holistic view of the business. Legacy databases often operate in isolation, preventing valuable data from different departments from being easily combined and analyzed. This fragmented data landscape makes it difficult to:

• Identify trends: Analyze data across departments to uncover patterns and opportunities for improvement.
• Optimize operations: Gain insights to streamline processes and workflows.
• Understand the customer base: Develop a complete customer profile by integrating data from various touchpoints.

Even with the potential of AI to unlock valuable insights from data, legacy databases can create a significant bottleneck. Integrating legacy databases with modern AI tools can be a complex and time-consuming process due to compatibility issues and differing data formats. Legacy databases often store data in formats that are incompatible with modern AI tools.  

This incompatibility creates the need for data transformation before it can actually be used to train AI models, while transforming the data to meet the required format can be a time-consuming and resource-intensive process, significantly delaying the deployment of the model and its impact.

The high cost associated with traditional AI development can be a significant barrier for many businesses:

• High Talent Cost:  The expertise required to build and maintain AI models can be expensive, particularly for smaller businesses with limited resources.  This can limit their ability to leverage AI for competitive advantage.
• Technology and Infrastructure Costs:  The cost of acquiring the necessary hardware, software, and cloud infrastructure for AI development can be prohibitive for some businesses.  Smaller businesses might struggle to invest in the necessary resources to get started with AI initiatives.
• Lengthy Development Cycles:   Traditional AI development processes can be time-consuming, requiring specialized skills and significant upfront investment. This can delay the time it takes to see a return on investment (ROI) from AI initiatives.  A growing startup looking to leverage AI for product recommendation might find the process of hiring data scientists and building the necessary infrastructure to be too slow and expensive, challenging their ability to innovate and compete.