Data Strategy

Quality data is crucial for automation, pattern recognition, and training AI for advanced capabilities. A data-first mindset and a data-focused platform are essential for long-term success

The starting point of any RAG system is its source data, often consisting of a vast corpus of text documents, websites, or databases

The cornerstone of a successful RAG implementation is the quality of your data

Also, eliminating irrelevant or sensitive information such as personally identifiable information (PII) is crucial to align with privacy standards

In today’s data world, there are so many options for an EL tool to avoid you developing your own extracting script and to help you gain a LOT of time.

Fivetran, Mage, and Airbyte to mention a few.

You don’t have to maintain custom scripts, these tools come with +300 connectors, basic scheduling, and error handling.

The amount of data we now access has grown far faster than the general level of data literacy in companies. Most companies have too much data, it’s stored in too many places, it’s not ‘clean’ and it’s often more confusing than helpful when it comes to what matters – making decisions with it. We need to ensure companies create centralized yet custom

Meta-data management efforts provide for the management and traceability of all components of the BI environment. Meta-data needs range from ensuring that definitions and business rules associated with BI applications are made available to business users to providing source data mapping information to manage the environment or answer questions rega