Data is the critical ingredient for all things AI. Without adequate and good data AI is not only ineffective it can be downright dangerous! The better the quality of data the more productive and accurate the AI powered solution.
In my view an AI initiative shouldn’t even be on the implementation agenda without assuring availability of the necessary data. The following requirements regarding data availability, preparation and integration must be met.

High-Quality Data: Data is the essential fuel that drives the AI engine. So, the higher the quality of the data the more effective your AI solutions. Ensure your data is accurate, relevant, and comprehensive.
Data Cleaning: Cleanse and preprocess your data to remove inconsistencies, duplicates, and errors.
Feature Engineering: Extract relevant features from your data to improve model performance.
Data Integration and Accessibility: Integrate data from various sources, internal and external. Ensure data accessibility and availability for AI applications and analytics across the organization.

A sample of data platforms, internal and external data sources relevant for marketing and customer lifecycle optimization functions are depicted in the tables below. These are not exhaustive lists. The particular ones utilized have to be determined based on the fit with the organization.

Data is the cornerstone of AI solutions, underpinning their development, performance, and impact. Its critical importance permeates every facet of

AI — from model training and optimization to validation and deployment. By recognizing the pivotal role of data and prioritizing efforts to ensure its quality, diversity, and ethical use, the full potential of AI can be realized.