
Data Quality: Why It Matters and How to Ensure It
Learn the six key dimensions of reliable data, the risks of poor input, and best practices for ensuring accurate insights.
Deliverables: Visualizations, Statistical Outputs, Recommendations Report
In a lot of circumstances building your own Artificial Intelligence model isn’t cost effective. If your Data Science use-case involves object classification, natural language processing, image annotation or other common AI tasks, then using Microsoft Azure Cognitive Services is likely the fastest, cheapest, and highest accuracy solution for you. Let our experts rapidly build a deployment that harvests insight using Microsoft’s vast AI resources.
Learn the six key dimensions of reliable data, the risks of poor input, and best practices for ensuring accurate insights.
Whether you use a data mart or query data directly from APIs, a hybrid strategy often delivers the best balance.
When evaluating the best ways to manage and store their data, the decision between on-premises and Azure data assets is crucial, as it impacts performance, security, cost, and scalability.
Improve data quality, enhance scalability, and accelerate insights for analytics, machine learning, and business growth. In today’s fast-paced digital world, data is one of the most valuable assets for organizations. However, managing and deriving meaningful insights from vast amounts of raw data can be complex and overwhelming. That’s where Medallion Architecture comes in — an