PREDICTIVE ANALYTICS
Предсказательная аналитика (прогнозная аналитика, предиктивная аналитика от англ. predictive analytics) — класс методов анализа данных, концентрирующийся на прогнозировании будущего поведения объектов и субъектов с целью принятия оптимальных решений.
Предсказательная аналитика использует статистические методы, методы интеллектуального анализа данных, теории игр, анализирует текущие и исторические факты для составления предсказаний о будущих событиях. В бизнесе прогнозные модели используют паттерны, найденные в исторических и выполняемых данных, чтобы идентифицировать риски и возможности. Модели фиксируют связи среди многих факторов, чтобы сделать возможной оценку рисков или потенциала, связанного с конкретным набором условий, руководя принятием решений о возможных сделках.
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.
The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.
Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, mobility, healthcare, child protection, pharmaceuticals, capacity planning, social networking and other fields.
One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.
Предсказательная аналитика использует статистические методы, методы интеллектуального анализа данных, теории игр, анализирует текущие и исторические факты для составления предсказаний о будущих событиях. В бизнесе прогнозные модели используют паттерны, найденные в исторических и выполняемых данных, чтобы идентифицировать риски и возможности. Модели фиксируют связи среди многих факторов, чтобы сделать возможной оценку рисков или потенциала, связанного с конкретным набором условий, руководя принятием решений о возможных сделках.
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.
The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.
Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, mobility, healthcare, child protection, pharmaceuticals, capacity planning, social networking and other fields.
One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.
«Management is prediction» Edward Deming
If you can combine data control, real-time access, cloud-based computer algorithms, mobile computing, and the right decision-making culture, you will have not only a very powerful predictive analytics capability, but also an organization with extreme velocity capabilities.
Predictive analytics requires a deep understanding of the technical and commercial attributes of the supply chain ecosystem, as well as advanced statistical and modeling capabilities.
If you can combine data control, real-time access, cloud-based computer algorithms, mobile computing, and the right decision-making culture, you will have not only a very powerful predictive analytics capability, but also an organization with extreme velocity capabilities.
Predictive analytics requires a deep understanding of the technical and commercial attributes of the supply chain ecosystem, as well as advanced statistical and modeling capabilities.
