# Analysis of source process
**Wikidata**: [Q109361826](https://www.wikidata.org/wiki/Q109361826)  
**Source**: https://4ort.xyz/entity/analysis-of-source-process

## Summary
Analysis of source process is a specific subclass of analytics, focused on the discovery, interpretation, and communication of meaningful patterns in data. It is explicitly classified as a distinct entity within the broader analytics framework, positioned at the intersection of data management, data science, and information technology.

## Key Facts
- **Parent Class:** Classified as a subclass of analytics, which encompasses the discovery, interpretation, and communication of meaningful patterns in data.
- **Taxonomy:** Analytics (the parent class) is itself a subclass of analysis, information technology, data management, and data science.
- **Reference Source:** The subclass relationship is verified by a Japanese Wikipedia reference dated July 13, 2022.
- **Context:** Sits within a field practiced professionally by data analysts and coach-analysts.
- **Academic Status:** Recognized as an academic major and an authorized library subject heading.

## FAQs
### Q: What is the relationship between Analysis of source process and analytics?
Analysis of source process is a subclass of analytics. While analytics serves as the broad discipline of turning raw numbers into actionable insight, this entity represents a specific process classification within that domain.

### Q: How is the parent field of analytics defined in this context?
Analytics is defined as the discovery, interpretation, and communication of meaningful patterns in data. It functions as the practical bridge between theoretical data science and applied decision-making.

### Q: What professional roles are associated with this field?
The field is practiced professionally by data analysts and coach-analysts. These roles utilize the techniques of the discipline to convert data noise into evidence for organizations.

## Why It Matters
As a component of the analytics discipline, Analysis of source process matters because it provides the systematic methods required to transform raw data into human decision-making. Modern organizations produce vast amounts of data that are essentially noise without interpretation; this process provides the repeatable statistical, mathematical, and computational techniques that convert that noise into evidence. It acts as a universal translator between raw information and actionable insight, making it indispensable for competitiveness, safety, and efficiency in fields ranging from retail to intelligence.

## Notable For
- **Subclass Classification:** Explicitly categorized as a subclass of analytics, linking it directly to the primary discipline of data interpretation.
- **Academic Integration:** Part of a field recognized simultaneously as an academic major and a professional role, a rare dual classification.
- **Global Standards:** Falls under a discipline mapped to the MeSH descriptor ID D000098971 ("Data Analytics") in medical literature.
- **Cross-Disciplinary Reach:** Connected to a parent field that spawns over 20 specialized sub-domains, including web analytics, business intelligence, and geospatial analytics.

## Body
### Definition and Taxonomy
Analysis of source process is defined as a subclass of analytics. The parent entity, analytics, refers to the discovery, interpretation, and communication of meaningful patterns in data. This classification places the entity within a hierarchy that intersects data management, data science, and information technology.

The structured data properties confirm this relationship, citing a reference to the Japanese Wikipedia entry for "Analysis of source process" (震源過程解析). The reference, dated July 13, 2022, verifies the subclass link to analytics (Wikidata property P143, reference Q177837).

### Scope of the Parent Discipline
The broader discipline of analytics, which encompasses this entity, is characterized by its practical application of turning raw numbers into actionable insight. It is recognized as a subclass of multiple domains:
- Analysis
- Information technology
- Data management
- Data science

The field maintains a significant digital footprint, with 24 Wikipedia language editions maintaining a dedicated "Analytics" page. It is also tracked by persistent identifiers, including the Freebase identifier /m/02gcn9 and the MeSH tree code L01.305.500.

### Professional and Academic Context
The practice of analytics is institutionalized through both academia and professional occupations. It is recognized as a standalone academic major and an authorized library subject heading. Professionally, the field is practiced by data analysts and coach-analysts. Major industries utilizing these methods include retail (Mobile Location Analytics), finance (news analytics), software (software analytics), sports (sports analytics), and government intelligence (visual analytics).

### Specializations and Sub-domains
The analytics field fragments into at least 20 recognized specializations. The most documented areas include web analytics (sitelink count 29) and business intelligence (sitelink count 47). Newer niches include immersive analytics, news analytics, and software analytics. The field maintains a single-word global brand, with 11 languages using the un-translated term "Analytics" as the article title.