Success Story ASM Aerosol-Service AG ASM streamlines reporting: 30 hours saved per month with Qlik Analytics Cloud & iVIEW.
Success Story Häfele Group Häfele’s BI Evolution: A Cyberattack as a Catalyst for Future-Driven Data Analytics
Success Story ASM Aerosol-Service AG ASM streamlines reporting: 30 hours saved per month with Qlik Analytics Cloud & iVIEW.
Success Story Häfele Group Häfele’s BI Evolution: A Cyberattack as a Catalyst for Future-Driven Data Analytics
Machine Learning Machine Learning goes beyond traditional data analysis: systems learn autonomously from data, identify complex patterns (including through Deep Learning), and generate predictions. This enables companies to act more proactively, optimize processes, and make well-founded decisions in less time. Artificial Intelligence Machine Learning in practice Increasing amounts of data Our world is fully connected today. In almost every area of life – whether private or professional – data is being generated: we accept cookies while browsing the internet, pay by credit card, use apps and social media, or rely on sensors in machines, vehicles, and everyday devices. Weather stations and IoT systems also continuously provide new streams of information. Machine learning: systems that learn from data Companies have been using business intelligence software for years to analyze data and generate reports. However, these data sets can unlock even greater potential when combined with Machine Learning. It is a subset of artificial intelligence and describes the ability of systems to generate knowledge from experience – in other words, from data. Software learns rules from examples, which it can then generalize and apply to new situations.For example, a Machine Learning model can learn to distinguish apples from pears. By analyzing training data – in this case, photos of apples and pears – the system identifies patterns and regularities. Based on this learning, the model or algorithm can then classify whether a new, previously unseen image shows an apple or a pear. The result is validated, and the model is improved over time.The quality of a Machine Learning model is measured by its error rate. The principle is simple: the more relevant data a model has access to – essentially, the more “practice” it gets – the better it learns and the more accurate its predictions become. Artificial Intelligence The new era governed by data Artificial Intelligence (AI) enables systems to learn from data, recognize patterns, and independently generate well-informed decisions. AI-powered solutions have already profoundly transformed the business world – and AI-driven BI tools are now essential for maintaining long-term competitiveness.More about Artificial Intelligence Supervised and Unsupervised Learning In this example, machine learning takes place using verifiable data as supervised learning: the test data is given a correct label (apple or pear) by humans in advance. The algorithm can then compare its prognoses with the actual results and improve itself step by step.This is different from machine learning using the unsupervised learning principle: no learning target is provided. The machine learning model is fed with data without any prior definition of what this data means. The machine learning algorithm recognises patterns and divides data up into clusters or categories without knowing what categories these are, i.e. under which labels they fall (such as ‘apple’ and ‘pear’).The majority of machine learning processes used in practice entail supervised learning. Deep Learning - what is that? Deep Learning is a subset of Machine Learning and is based on artificial neural networks. Through training methods, the system develops artificial intelligence capable of learning from data. Information is continuously processed, and new inputs are integrated with what has already been learned, allowing the model to improve over time. Based on these learning processes, the system can generate predictions and make decisions, which are continuously reviewed and adjusted. Human intervention is generally required only initially. Deep Learning enables systems to learn autonomously and is particularly suitable for applications with large datasets, from which patterns and models can be derived. The foundation consists of artificial neural networks, which are repeatedly reconnected and optimized during the learning process. Machine learning in practice A whole range of technology that we use daily in our private lives is already based on machine learning algorithms. Examples are facial recognition in photos and videos, speech recognition on mobile phones, translation programmes online and computer spam filters. Machine Learning for Industry In the industrial sector, ML can be used to gather insights from machine data, such as determining after how many operating hours a component needs to be replaced. This so-called predictive maintenance allows companies to plan service and maintenance proactively, avoiding costly downtime due to part failures. Machine learning methods can also be used to forecast, for example, the pricing of finished products in custom manufacturing, depending on raw material availability and associated costs. Machine Learning in the energy sector Energy suppliers use machine learning algorithms to coordinate supply and demand as well as to recognise anomalies in the power supply. Machine Learning for Financial Services Banks can use machine learning algorithms to make credit decisions for retail and business clients, such as in relation to credit checks, establishing credit conditions and recognising credit fraud. Machine Learning for Healthcare In healthcare, machine learning-based similarity analyses of patient files, patterns and connections can be used to optimise the treatment of new patients. Machine Learning for Retail Retail can use machine learning for more efficient market research or personalised customer communication to reflect customers’ purchase preferences. Machine Learning for Telecommunications Telecommunications companies can search network traffic for anomalies indicating attacks.