Jose Plehn Dujowich

The Shift from Intuition to Insight Once upon a time, business decisions were made mostly by gut feeling. A store owner guessed what might sell next season, or a manager relied on years of experience to set prices. While instinct still plays a role, today’s world is far too complex for guesswork alone. Enter data analytics —the practice of using data to guide better, faster, and more confident decisions. Think of it as moving from “I think this will work” to “I know this will work because the numbers prove it.” From small startups to Fortune 500 giants, data has become the new business compass, helping leaders steer through uncertainty with clarity and confidence. Making Sense of the Chaos Businesses are flooded with information—from sales figures and social media stats to customer feedback and web traffic. But data by itself is just noise unless you know how to interpret it. That’s where analytics steps in. Data analytics helps filter through the chaos to find what truly matters. A re...

Preparing your data for artificial intelligence (AI) is a crucial step to ensure your AI projects deliver accurate and meaningful results. Whether you are working on machine learning models, natural language processing, or any AI-driven application, the quality and readiness of your data can significantly impact the outcome. This blog post will guide you through essential steps to get your data AI ready, making the entire process smoother and more effective. Understanding what it means for data to be AI ready is the first step in this journey. AI models require clean, structured, and well-labeled data to learn patterns and make predictions. Raw or messy data can lead to poor performance, biased outcomes, or even failure of the AI system. By following these easy steps, you can ensure that your data meets the requirements for AI projects and helps you unlock the true potential of artificial intelligence. 1.Data Cleaning and Validation Data cleaning is the process of detecting and corr...