Predictive analytics Definitions

  • A

    A/B testing (split testing)

    A/B testing is a statistical method used to assess proposed changes to a product or service.

  • advanced analytics

    Advanced analytics is a broad category of inquiry that can be used to help drive changes and improvements in business practices.

  • association rules (in data mining)

    Association rules are if/then statements that help uncover relationships between seemingly unrelated data in a transactional database, relational database or other information repository.  

  • B

    big data analytics

    Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.

  • business analytics (BA)

    Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis.

  • C

    customer analytics (customer data analytics)

    Customer analytics, also called customer data analytics, is the systematic examination of a company's customer information and customer behavior to identify, attract and retain the most profitable customers.

  • D

    data journalism

    Data journalism in an approach to writing for the public in which the journalist analyzes large data sets to identify potential news stories.

  • data preparation

    Data preparation is the process of gathering, combining, structuring and organizing data so it can be analyzed as part of data visualization, analytics and machine learning applications.

  • data sampling

    Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points in order to identify patterns and trends in the data set being examined as a whole.

  • data science as a service (DSaaS)

    Data science as a service (DSaaS) is the delivery of analytical reports and data products with embedded analytics produced by a service provider's team of data scientists to a client enterprise.

  • deep analytics

    Deep analytics is the application of sophisticated data processing techniques to yield information from large and typically multi-source data sets comprised of both unstructured and semi-structured data.

  • E

    edge analytics

    Edge analytics applies algorithms to data at the point of collection in order to trigger actions and determine what should be sent back to a central data repository and what should be discarded.

  • ensemble modeling

    Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.

  • F

    funnel analysis

    Funnel analysis is a way to measure and improve the performance of customer interactions in a step-wise progression from the initial customer contact to a predetermined conversion metric.

  • L

    logistic regression

    Logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. A logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.

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