Innovation and collaborative, synchronized program management for new programs
Aerospace & Defense
Innovation and collaborative, synchronized program management for new programsExplore Industry
Automotive & Transportation
Integration of mechanical, software and electronic systems technologies for vehicle systemsExplore Industry
Consumer Products & Retail
Product innovation through effective management of integrated formulations, packaging and manufacturing processesExplore Industry
Electronics & Semiconductors
New product development leverages data to improve quality and profitability and reduce time-to-market and costsExplore Industry
Energy & Utilities
Supply chain collaboration in design, construction, maintenance and retirement of mission-critical assetsExplore Industry
Industrial Machinery & Heavy Equipment
Integration of manufacturing process planning with design and engineering for today’s machine complexityExplore Industry
Insurance & Financial
Visibility, compliance and accountability for insurance and financial industriesExplore Industry
Shipbuilding innovation to sustainably reduce the cost of developing future fleetsExplore Industry
Media & Telecommunications
Siemens PLM Software, a leader in media and telecommunications software, delivers digital solutions for cutting-edge technology supporting complex products in a rapidly changing market.Explore Industry
Medical Devices & Pharmaceuticals
“Personalized product innovation” through digitalization to meet market demands and reduce costsExplore Industry
Faster time to market, fewer errors for Software DevelopmentExplore Industry
Small & Medium Business
Remove barriers and grow while maintaining your bottom line. We’re democratizing the most robust digital twins for your small and medium businesses.Explore Industry
Siemens Digital Industries Software Predictive Analysis
Predictive analysis is the process of using historical data to make predictions about the future.
It is used in almost every industry, including financial, manufacturing and commerce. For example, data analytics has been used frequently by marketing managers who have engaged with big data from multiple apps and websites, online clicks and other ways to predict customer behavior. The vast, unstructured data that is collected through data mining is then delivered and packaged in an interactive format using dashboards to signify profits, conversion rates, averages and percentages.
The goal of data, then, is to search for patterns and find relationships between variables using inputs from human beings. If one assumption and hypothesis is valid, additional testing on the data continues. Data analytics, based on past events, is descriptive and does not predict the impact of a change in any variable.
A natural segue of data analytics is to predict using the collected data of what might happen. One differentiating factor is the elaborate methodologies, technologies, and infrastructure involved in predictive analysis. The predictions are based on historical data and where human interaction controls data query, pattern validation, and testing assumptions.
Of course, assumptions drawn from past experiences presupposes the future will follow the same patterns. “What if” assumptions based on the understanding of the past are used in this analysis, which is limited by the volume, time and cost constraints of human data analysts.
The insights gained by these predictions are extremely useful to marketers to predict campaign effectiveness, decision-making on collateral, geographic markets and targeted demographics. One of the most famous application scope is big data analytics, which aims to unveil patterns, subtle correlations, and trends to empower decision-making processes.
Machine learning is an extension of the concepts around predictive analytics, with one key difference – AI is able to make assumptions, test and learn autonomously.
Every modern business sector can gain maximum potential from predictive analytics. The key ingredient being the possession of sufficiently large, varied, detailed, and reliable historical and “intelligent” data in order to optimize their processes. This can be applied to fraud detection, processes optimization, costs reductions, market trends anticipation, and the discovery and innovation of new business opportunities.