AI-driven root cause refers to the use of artificial intelligence systems to automatically identify the underlying reason behind a problem or failure in a process, system or product. It analyses large volumes of data, detects patterns and correlations, and suggests the most likely causes without the need for manual investigation. This approach helps organisations to…

Predictive risk scoring is a method used to estimate the likelihood of a specific event or outcome by analysing existing data and statistical models. It assigns a numerical score to indicate the level of risk associated with a person, action, or situation. Organisations use these scores to make informed decisions, such as preventing fraud, assessing…

AI-based what-if analysis uses artificial intelligence to predict how changes in one or more factors might affect future outcomes. It helps people and organisations understand the possible results of different decisions or scenarios by analysing data and simulating potential changes. This approach is useful for planning, forecasting, and making informed choices without having to test…

Smart Data Visualisation refers to the use of advanced techniques and tools to present data in a way that is easy to understand and interact with. It often includes features such as automatic chart recommendations, interactive dashboards, and the ability to highlight important patterns or trends. The goal is to help people make sense of…

Clinical Decision Support refers to computer systems or tools that help healthcare professionals make better decisions by providing relevant information, reminders, or recommendations at the point of care. These tools analyse patient data and medical knowledge to suggest possible diagnoses, alert about potential medication interactions, or remind clinicians of evidence-based guidelines. The aim is to…

AI for Data Visualisation uses artificial intelligence to help people understand complex information by automatically creating graphs, charts or other visual tools. It can quickly spot patterns, trends or outliers in large amounts of data, making it easier for users to see important insights. This technology saves time and reduces errors by handling repetitive tasks…

Data science model interpretability refers to how easily humans can understand the decisions or predictions made by a data-driven model. It is about making the inner workings of complex algorithms clear and transparent, so users can see why a model made a certain choice. Good interpretability helps build trust, ensures accountability, and allows people to…

Data Science Model Explainability refers to the ability to understand and describe how and why a data science model makes its predictions or decisions. It involves making the workings of complex models transparent and interpretable, especially when the model is used for important decisions. This helps users trust the model and ensures that the decision-making…

Model explainability dashboards are interactive tools designed to help users understand how machine learning models make their predictions. They present visual summaries, charts and metrics that break down which features or factors influence the outcome of a model. These dashboards can help users, developers and stakeholders trust and interpret the decisions made by complex models,…