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Data preparation is the act of manipulating (or pre-processing) raw data (which may come from disparate data sources) into a form that can be readily and accurately analysed, e.g. for business purposes.
The analysis highlights Other examples of invalid data requiring correction, Self-service data preparation and Data specification as prominent areas in the source structure around Data preparation.
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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.
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The extracted context around Data preparation shows recurring relationship patterns in the source. For example, Data preparation → Cleanse, Common, Data, Discover, Gather, Store, Transform Another extracted example is Data preparation → act of manipulating. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
data preparation records specification business may sources formats step field information source geographical code cleaning errors numbers different come steps
TTTA extracted 13 structured relationships around Data preparation. Examples in this analysis include Data preparation → is a → act of manipulating and loading data or data ingestion → instance of → e.g. for business purposes.Data preparation is the first step in data analytics projects and can include many discrete tasks. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Data preparation | is a | act of manipulating | 0.90 | text |
| loading data or data ingestion | instance of | e.g. for business purposes.Data preparation is the first step in data analytics projects and can include many discrete tasks | 0.80 | text |
| data fusion | instance of | e.g. for business purposes.Data preparation is the first step in data analytics projects and can include many discrete tasks | 0.80 | text |
| data cleaning | instance of | e.g. for business purposes.Data preparation is the first step in data analytics projects and can include many discrete tasks | 0.80 | text |
| data augmentation | instance of | e.g. for business purposes.Data preparation is the first step in data analytics projects and can include many discrete tasks | 0.80 | text |
| and data delivery.The issues to be dealt with fall into two main categories | instance of | e.g. for business purposes.Data preparation is the first step in data analytics projects and can include many discrete tasks | 0.80 | text |
| Data preparation | related to Data preparation steps | Gather | 0.60 | section |
| Data preparation | related to Data preparation steps | Discover | 0.60 | section |
| Data preparation | related to Data preparation steps | Cleanse | 0.60 | section |
| Data preparation | related to Data preparation steps | Data | 0.60 | section |
| Data preparation | related to Data preparation steps | Common | 0.60 | section |
| Data preparation | related to Data preparation steps | Transform | 0.60 | section |
The concept neighborhoods around Data preparation bring nearby vocabulary together. In this analysis, examples include Preparation, Business and Datasets. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Data preparation, one of the stronger structural bridges in this analysis connects Data preparation with Other examples of invalid data requiring correction. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Data preparation to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Other examples of invalid data requiring correction, Self-service data preparation & Data specification, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Data preparation · EN edition · Analysis: TopicsToTalkAbout
