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Datafication: Impact, Examples & Ideology

Datafication is a technological trend turning many aspects of our life into data, which is then converted into information, thereby manifesting as a new form of value. This concept was introduced to the broader lexicon in 2013 by Kenneth Cukier and Viktor Mayer-Schönberger. Prior to this, datafication had been predominantly associated with the analysis…

Language: English [EN]
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Datafication topic overview

The analysis highlights Impact, Examples and Ideology as prominent areas in the source structure around Datafication.

Related topics
36
Source areas
4
Connected nodes
40
Extracted relationships
23
Concept neighborhoods
12
Bridge connections
40

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Impact · 24 topics
Examples · 6 topics
Overview · 5 topics
Ideology · 1 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Ideology

Examples

Impact

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Datafication connects Entity context

The extracted context around Datafication shows recurring relationship patterns in the source. For example, Datafication → Data, HR, LinkedIn, Other, Some, Twitter Another extracted example is Datafication → Clinical, Data-driven, Health, Self-care, The. Use these groups to spot repeated connection types before inspecting the individual relationships.

Datafication

Top relations

related to Examples · 6
Datafication → Data, HR, LinkedIn, Other, Some, Twitter
has impact · 5
Datafication → Clinical, Data-driven, Health, Self-care, The
related to External links · 4
Datafication → Digitization, From, Retrieved, SCOOP
is a · 2
Datafication → far broader activity, technological trend turning many aspects of our life into data

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

data information aspects life new digitization digital turning value broader impact media transformation big examples social communication citation needed technological

Datafication relationships Subject–Predicate–Object triples

TTTA extracted 23 structured relationships around Datafication. Examples in this analysis include Datafication → is a → technological trend turning many aspects of our life into data and Datafication → is a → far broader activity. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Dataficationis atechnological trend turning many aspects of our life into data0.90text
Dataficationis afar broader activity0.90text
risk taking profileinstance ofapps or social media usage is used to identify potential employees and their specific characteristics0.80text
personalityinstance ofapps or social media usage is used to identify potential employees and their specific characteristics0.80text
transportationinstance ofissues that can arise might be noticed and tackled in areas0.80text
waste managementinstance ofissues that can arise might be noticed and tackled in areas0.80text
logisticsinstance ofissues that can arise might be noticed and tackled in areas0.80text
and energyinstance ofissues that can arise might be noticed and tackled in areas0.80text
Dataficationhas impactThe0.60section
Dataficationhas impactData-driven0.60section
Dataficationhas impactClinical0.60section
Dataficationhas impactHealth0.60section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Datafication bring nearby vocabulary together. In this analysis, examples include Data, Aspects and Information. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Datafication
    • Data
    • Aspects
    • Information
    • Media
    • Citation
    • Communication
    • Examples
    • Life
    • Needed
    • Social
    • Turning
    • Value
  • datafication
    • Data
    • Aspects
    • Information
    • Media
    • Citation
    • Communication
    • Examples
    • Life
    • Needed
    • Social
    • Turning
    • Value
  • data
    • Datafication
    • Media
    • Big
    • Citation
    • Communication
    • Examples
    • Impact
    • Life
    • Needed
    • Social
    • Transformation
    • Turning
  • big data
    • Transformation
    • Datafication
    • Media
    • Impact
    • Big
    • Citation
    • Communication
    • Data
    • Examples
    • Life
    • Needed
    • Social
  • data collection
    • Datafication
    • Media
    • Big
    • Citation
    • Communication
    • Examples
    • Impact
    • Life
    • Needed
    • Social
    • Transformation
    • Turning
  • real-time data
    • Datafication
    • Media
    • Big
    • Citation
    • Communication
    • Examples
    • Impact
    • Life
    • Needed
    • Social
    • Transformation
    • Turning
  • impact
    • Big
    • Communication
    • Examples
    • Life
    • Social
    • Transformation
    • Turning
    • Value
    • Information
    • Media
    • New
  • examples
    • Media
    • Citation
    • Impact
    • Life
    • Needed
    • Social
    • Turning
    • Value
    • Information
    • New

Connections between topic areas Semantic bridges

For Datafication, one of the stronger structural bridges in this analysis connects Datafication with Impact. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Datafication — Impact · splits 16 ⟂ 25
Datafication — Examples · splits 34 ⟂ 7
Datafication — Overview · splits 35 ⟂ 6

Map overview Semantic statistics

Datafication

Nodes41
Edges40
Triples23
Avg. degree1.95
Density0.04878
Components1

Source & methodology

TTTA analyzes the structure around Datafication to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Impact, Examples & Ideology, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Datafication · EN edition · Analysis: TopicsToTalkAbout

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