Predictive analytics facilitates business choices. It unveils unseen patterns and insights within data to support decision making. Predictive analytics, possibly fed by AI, is becoming the core of the decision process thanks to new functions and capabilities such as sensitivity analysis and live predict. Furthermore, predictive analytics accelerate action with natural language processing features and crafting unique and unexpected competitive assets.
Thanks to data analytics, information grows into meaningful insights. A thorough understanding of data is required to transform raw data into smart and usable knowledge. Processing, preparing, and analyzing data yields trustworthy analytics to serve your business.
When predictive analytics serves business purposes, it becomes business intelligence. For instance, it can estimate the likelihood of a customer terminating service sometime soon. It can support implementing efficient cross-sales or help selling additional services and products to current customers. Inspiring and powerful graphic visualization of the business intelligence results allow experts to anticipate and generate value for the company.
Augmented analytics utilizes artificial intelligence (AI) through machine learning to improve analytics over all stages of the data lifecycle — from the way we prepare data to how we interpret it and produce insights. A blend of data science and artificial intelligence, augmented analytics makes data analytics available for more people. It allows us to benefit from data, empowers us to investigate, and automatically produces perspectives.
Predictive analytics is looking into the future. It creates forecasts of likely outcomes based on identified trends and correlations through statistics and machine learning. Business leaders can outperform the competition with an effective predictive analytics strategy, which helps them anticipate future outcomes.
Statistics is a mathematical domain consisting of collecting, classifying, and representing numerical data through computations such as standard deviation, regression, and other techniques. It provides results that represent the actual data, usually through the use of graphics.
Data mining turns raw data into usable information. It is about identifying significant patterns in data generated by various systems and devices within a company. It operates at the intersection of machine learning, statistics, and database systems.
Through data modeling, one can create a global system representation of its data. This representation illustrates the data and the relationships between its different attributes at a certain time. Used appropriately, it allows us to analyze a business and help it achieve its business goals.
Machine Learning aims to extend accuracy in the identification of patterns hidden within datasets. It learns from knowledge to activate the performance of a machine on its task. It involves Automated Machine Learning to provide results as exploitable data that can be processed by AI.
As we stated in another article, AutoML points to a collection of techniques and methods that automate portions of the machine learning workflow. It allows to generate models automatically without the implication of a data scientist.
Versatile and reliable, predictive analytics can serve the purposes of many types of industries. By quickly creating predictive models, usable on small datasets, known as Small Data, and by being easily deployed, predictive analytics can profitably impact everyday decision-making processes and reporting in fields of expertise such as:
Predictive analytics matches the issues encountered in healthcare. Conditions and diseases are most likely to be cured when risk is detected beforehand. Predictive analytics brings the power of data to medical assessment, increasing security and safety all the way.
When it comes to marketing, staying one step ahead form trends and customers expectations is a valuable asset. Predictive analytics helps professionals optimise their campaigns, boost their sales and evaluate their ROI.
It can help improve credit card fraud detection. Whether the fraud is scamming or stealing, identifying fraud is critical, especially for CNP (Card Not Present) fraud, representing 85% of all credit card fraud.
Whether it is to prevent failure or malfunction, risk evaluation is critical to avoid industry downtime. Predictive analytics allow experts to anticipate risks and therefore increase productivity.
Churn is a significant threat to growth. Its prediction is a crucial asset for a business. It allows professionals to better understand customer attrition, identify customers most at risk of leaving, and have clear insights to improve retention.
Measuring a price objectively for a given asset is more than complicated. Predictive analytics helps business experts to get fair evaluations through previous transactions or assessments.
Predicting quality can be a game-changer whether you are in service, goods, or manufacturing. Quality can benefit from predictive analytics to, for instance, prevent component resistance, determine wine quality or predict the accuracy of customers’ answers.