# data management and data science

> field of research

**Wikidata**: [Q113212528](https://www.wikidata.org/wiki/Q113212528)  
**Source**: https://4ort.xyz/entity/data-management-and-data-science

## Summary  
Data management and data science is an academic discipline that studies how to collect, store, organize, retrieve, and analyze data. It sits at the intersection of computer science and information science, encompassing the sub‑fields of data management and data science.

## Key Facts  
- **Academic discipline**: Classified as an *instance of* “academic discipline” in Wikidata.  
- **Parent fields**: A *part of* both **information science** (which focuses on analysis, collection, classification, manipulation, storage, retrieval, and dissemination of information) and **computer science** (the study of computation).  
- **Sub‑fields**: Has two primary components – **data management** and **data science**.  
- **Subclass relationships**: Listed as a *subclass of* both **computer science** and **information science**.  
- **ANZSRC 2020 classification**: Identified by the codes **4605** (“Data management and data science”) and **460599** (“Data management and data science not elsewhere classified”).  
- **Wikidata description**: Defined as a “field of research”.  
- **Sitelink count**: The Wikidata entry links to **1** Wikipedia language edition (Ukrainian).  

## FAQs  
### Q: What does “data management and data science” study?  
A: It studies the full lifecycle of data—from collection and storage to organization, retrieval, and analytical processing—combining methods from computer science and information science.  

### Q: How is it different from “data science” alone?  
A: While data science focuses on extracting insights and building models from data, the broader field also includes data management, which deals with how data is structured, stored, and maintained.  

### Q: Why is it considered an academic discipline?  
A: Because it has a defined body of knowledge, distinct sub‑fields, and is recognized in research classification systems (e.g., ANZSRC 2020) as a formal area of study.  

## Why It Matters  
Data management and data science provides the foundational framework for turning raw data into usable information. By integrating principles of computer science (algorithmic processing, computation) with information science (organization, retrieval, dissemination), the field enables organizations, researchers, and governments to store data efficiently, ensure its quality, and derive actionable insights. This interdisciplinary approach supports everything from scientific research to business intelligence, helping decision‑makers handle the growing volume, variety, and velocity of modern data. Its recognition in research taxonomies (such as ANZSRC) underscores its role in shaping curricula, funding priorities, and scholarly discourse worldwide.  

## Notable For  
- **Interdisciplinary scope**: Bridges computer science and information science, uniting technical and informational perspectives.  
- **Dual focus**: Explicitly includes both data management (storage, governance) and data science (analysis, modeling).  
- **Official classification**: Recognized in the Australian and New Zealand Standard Research Classification (ANZSRC 2020) with dedicated codes.  
- **Distinct academic identity**: Listed as a separate “field of research” with its own Wikidata entry, separate from its parent disciplines.  

## Body  

### Definition  
Data management and data science is a **field of research** that examines how data is **collected, stored, organized, retrieved, and analyzed**. It treats the entire data lifecycle as a cohesive subject of study.  

### Relationship to Parent Disciplines  
- **Information science**: Provides the conceptual basis for handling information—classification, dissemination, and retrieval.  
- **Computer science**: Supplies computational methods and algorithms for processing data at scale.  

Both parent fields contribute core methodologies, making the discipline a hybrid of theory and practice.  

### Core Components  

#### Data Management  
- Focuses on **storage architectures**, **metadata standards**, **data governance**, and **quality assurance**.  
- Ensures that data remains **accessible**, **secure**, and **consistent** over time.  

#### Data Science  
- Emphasizes **statistical analysis**, **machine learning**, and **predictive modeling**.  
- Turns curated data into **insights**, **visualizations**, and **decision‑support tools**.  

### Classification and Recognition  
- **Subclass of**: Computer science; Information science.  
- **ANZSRC 2020 codes**:  
  - **4605** – “Data management and data science”.  
  - **460599** – “Data management and data science not elsewhere classified”.  
- **Wikidata entry**: Linked to a single Ukrainian Wikipedia page (sitelink count = 1).  

### Academic Context  
As an **academic discipline**, it appears in university curricula, research funding calls, and scholarly publications. Its dual nature encourages collaboration between **engineers**, **information specialists**, and **statisticians**, fostering interdisciplinary research projects.  

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*All statements are derived from the supplied source material and do not include external information.*