by Dr. Owns | Feb 11, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
How Reliable Are Your Predictions? About To be considered reliable, a model must be calibrated so that its confidence in each decision closely reflects its true outcome. In this blog post we’ll take a look at the most commonly used definition for calibration and then...
by Dr. Owns | Feb 11, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
There seems to be a consensus that leveraging data, analytics, and AI to create a data-driven organization requires a clear strategic approach. However, there is less clarity and agreement on exactly what this strategic approach should look like in practice. This...
by Dr. Owns | Feb 11, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Overview Introduction — Purpose and Reasons Datasets, Tasks, and Settings Results Conclusions Wrapping Up Introduction — Purpose and Reasons Speed is important when dealing with large amounts of data. If you are handling data in a cloud data warehouse or similar, then...
by Dr. Owns | Feb 10, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Stable Diffusion 1.5/2.0/2.1/XL 1.0, DALL-E, Imagen… In the past years, Diffusion Models have showcased stunning quality in image generation. However, while producing great quality on generic concepts, these struggle to generate high quality for more specialised...
by Dr. Owns | Feb 7, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Which Outcome Matters? Here is a common scenario : An A/B test was conducted, where a random sample of units (e.g. customers) were selected for a campaign and they received Treatment A. Another sample was selected to receive Treatment B. “A” could be a communication...
by Dr. Owns | Feb 7, 2025 | Analytics, Artificial Intelligence, Data and Information, Decision Support
Accurate impact estimations can make or break your business case. Yet, despite its importance, most teams use oversimplified calculations that can lead to inflated projections. These shot-in-the-dark numbers not only destroy credibility with stakeholders but can also...
