Predictive analytics Definitions

  • A

    A/B testing (split testing)

    A/B testing, sometimes called split testing, is an assessment tool for identifying which version of something helps an individual or organization meet a business goal more effectively.

  • 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 to show the probability of relationships between data items within large data sets in various types of databases.

  • B

    big data analytics

    Big data analytics is the often complex process of examining large and varied data sets, or big data, to uncover information -- such as hidden patterns, unknown correlations, market trends and customer preferences -- that can help organizations make informed business decisions.

  • business analytics (BA)

    Business analytics (BA) is the iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. Business analytics is used by companies that are committed to making data-driven decisions.

  • 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 to identify patterns and trends in the larger data set being examined.

  • data science as a service (DSaaS)

    Data science as a service (DSaaS) is a form of outsourcing that involves the delivery of information gleaned from advanced analytics applications run by data scientists at an outside company to corporate clients for their business use.

  • 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.

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