Throughout the Ipiphany user interface you will encounter a number of technical terms. Here are the most common:

Dataset 

  • When a data file is imported into Ipiphany it is stored in a Dataset. 
  • Each Dataset contains records with the same types of attributes.
  • When creating a 'Discovery' you will need to select a specific Dataset.

Record

  • Data for one instance in your Dataset. 
  • Consists of values for named attributes
  • When importing data each row in the file is stored in a record (within a dataset)

Attribute

  • A named field in your Dataset
  • When importing data each column in the file is mapped to an attribute
  • Each attribute has a type of: Date, Number, Category, Score, Verbatim Text

Verbatim Text Fields

  • An attribute of type ‘Verbatim Text’
  • When importing data you have one or more columns in the file that store verbatim text e.g. A ‘Recommend Reason’ and ‘Improvement Suggestion’ comments
  • Each of these columns is mapped to an Attribute of type Verbatim Text – which we refer to as a Verbatim Text Field
  • Ipiphany provides the option of analysing all or just some of the Verbatim Text Fields

Verbatim

  • An individual value for a Verbatim Attribute e.g. a specific comment
  • When importing data each non-blank cell in a column that stores verbatim text data is imported as a Verbatim
  • When viewing patterns in Ipiphany example Verbatims will be displayed as examples

Verbatim Aspect

  • A feature or pattern derived from Verbatim text using natural language processing. Examples include Keywords, Word Associations, Themes and Concepts

Keyword

  • The base form of a word extracted from a Verbatim e.g. ‘fix’, ‘problem’
  • When processing Verbatim text Ipiphany extracts keywords by: Identifying the part of speech (POS) for all words, extracting those that are Nouns, Verbs, Adjectives and Adverbs and converting them to their base morphological form e.g. fixed -> fix, problems -> problem
  • Keywords may be used by Ipiphany to identify patterns within Verbatim attributes

Word Association

  • An association between two keywords that is derived from their use in a Verbatim
  • When processing Verbatim text Ipiphany extracts word associations by: Determining the syntax of the language used, identifying the roles that words play and the relationships between them e.g. In “He fixed my problem” the target of the word ‘fixed’ is ‘problem’, and extracting important relationships and storing them as an association between two keywords e.g. fix<->problem
  • Word Associations may be used by Ipiphany to identify patterns within Verbatim attributes

Word, Aspect or Concept Vector

  • A mathematical vector that stores semantic knowledge about a word, concept or other type of verbatim aspect.
  • When Ipiphany digests knowledge bases for use in semantic processing part of this knowledge is storedas compressed Word or Concept vectors
  • Word and Concept vectors are used by Ipiphany to understand that two words or concepts have a similar meaning or are related in some way e.g.
  • The word ‘problem’ is similar to the word ‘issue’ 
  • The word ‘solution’ is related to the word ‘problem’

Concept

  • A Verbatim Aspect that is derived directly from a curated Knowledge Source i.e. an Ontology or Knowledge Base e.g. ‘Problem Resolution’
  • Concepts are defined through natural language patterns that consist of keywords, word associations and other verbatim aspects
  • Concepts may be used by Ipiphany to identify patterns within Verbatim Text attributes

Ontology

  • A collection of concept definitions and relationships
  • Ipiphany includes a number of general and industry specific ontologies
  • Custom ontologies can be developed by Touchpoint Group for specific companies or domains

Knowledge Base

  • A public store of general or industry specific knowledge
  • Ipiphany uses knowledge derived from public knowledge bases such as WordNet, Wikipedia/DBPedia and Concept Net

Theme

  • A Verbatim Aspect that defines an object or action, that is automatically derived directly from understanding the meaning of the verbatim text
  • When mining patterns from Verbatim text, Ipiphany uses semantic processing to understand where different words or associations have a similar meaning
  • These are then clustered into Broad or Narrow Themes
  • Themes may be used by Ipiphany to identify patterns within Verbatim attributes

Frequency

  • Measures how often patterns occur in the data
  • Displayed as a Count or % 
  • Frequency % is calculated as: Pattern Count / Data Selection Count
  • Frequency is always calculated as a Record Count – Except within the Verbatim Panel where it is a Verbatim Count

Metric

  • A calculated statistic derived from a structured data attribute used as the main target metric for describing the importance of patterns with respect to your business objective e.g. NPS of Recommend Score, Average Call Time

Comparison

  • A secondary statistic derived from the target measure or pattern frequency that is used to aid in comparing the importance of patterns.
  • There are three comparison metrics available: Difference, Impact and Relevance

Difference

  • Calculates the difference between the metric calculated for a specific pattern and the metric for data selection
  • Sometimes referred to as 'Delta'

Impact

  • Takes into account Difference and relative frequencies to calculate the effect that a pattern has on the metric for the data selection i.e. how much a pattern pushes the overall data selection metric up or down

Relevance

  • Calculates how relevant a sub-pattern is with respect to either a pattern in your data, or to all your data. You can think of Relevance as finding the ‘uncommonly common’ sub-patterns that occur more frequently with the pattern than they do in your overall data selection – and calculating a score that combines both the uniqueness factor and the frequency. You will often find that sub-patterns with a high Relevance will contain important descriptive and root cause information.

Filter

  • A condition in the form Attribute=Value that selects a subset of your Dataset for analysis.
  • When querying data multiple filters are combined into a query
  • Filters for the same Attributes or Verbatim Aspects are combined using OR Filters for different Attributes or Verbatim Aspects are combined using AND

Search Term

  • A type of Filter that matches to Verbatim text. 
  • There are 4 types of Search Terms:
  • Words - match to similar words  e.g. “account” matches “account”, “accounts”
  • Phrase – matches quoted words to similar phrase  e.g. “close account”matches “closed account”, “closing accounts”
  • Wildcards – match to patterns e.g. “cl*d” matches “closed”, “cloud”
  • Expression – matches using boolean logic e.g. “open & account” will match to “I opened an account”.

Discovery

  • The results of an automated Discovery Analysis in Ipiphany.

Driver

  • An Attribute that is selected as a candidate input variable for a Discovery Analysis

Pattern

  • A resulting subset of data from the Discovery Analysis process that is described as having a strong impact on the target measure you want to improve.

KWIC

  • Keyword in Context – a way of visualising verbatim text so key aspects aligned and highlighted so they are quick to read and understand

 

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