39422886
Beschreibung
Karteikarten von Ronan Kavanagh, aktualisiert vor 8 Monate
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Erstellt von Ronan Kavanagh
vor 8 Monate
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| Frage | Antworten |
| Why is "Big Data" only a general term? (1) | 1. Big Data means several different things owed to being a group of technologies |
| According to Lohr, how does Big Data change web analytics? (2) | 1. Potential revolution in measurement 2. Way of thinking - How should decisions be made? |
| According to De Mauro, what are the four main themes of Big Data? (4) | 1. Information 2. Technology 3. Methods 4. Impact |
| What does "information" as a theme of Big Data represent? (1) | 1. The digitisation and datafication revolution that fuels Big Data |
| What does "technology" as a theme of Big Data represent? (4) | The medium that facilitates the: 1. Collection 2. Processing 3. Transmission 4. Storage of Big Data |
| What does "methods" as a theme of Big Data represent? | 1. Techniques used to process Big Data 2. The interdisciplinary knowledge required to understand and apply Big Data (e.g. STEM + Humanities) |
| What does "impact" as a theme of Big Data represent? (2) | 1. The pervasive omnipresence of Big Data in our lives 2. The degree which Big Data affects our lives |
| Why does Big Data require technology to be interpreted? (2) | 1. High Volume, Velocity and Variety makes it difficult for humans to analyse effectively 2. Technology provides analytical methods to interpret it and gain Value |
| What are the "Four V's" of Big Data? | 1. Volume = Amount / Scale of data 2. Velocity = Speed of data generation / processing 3. Variety = Types of data (e.g. Structured, Unstructured, Semi-Structured) 4. Veracity = Accuracy and reliability of the data |
| Name and explain three V's besides the Four V's that could help describe Big Data | Variability: Inconsistencies / Disparencies Validity: Accuracy / Appropriateness Vulnerability: Security concerns of data or its use Volatility: How long before data becomes outdated Visualisation: Representing data visually helps humans interpret it Value: What purpose / insights of data |
| What are the six provocations of Big Data according to Boyd and Crawford? | 1. Changes the definition of knowledge 2. Claims to objectivity and accuracy are misleading 3. Bigger data ≠ Better Data 4. No context = No meaning 5. Accessible data ≠ Ethical data 6. Power of Big Data widens the Digital Divide |
| How can Big Data challenge our concept of what knowledge is? (2) | 1. Big Data cannot be interpreted w/o technology 2. Radical shift in how we think about / do research |
| Why can claims to objectivity and accuracy in Big Data be misleading? (3) | 1. Bias in how the data is collected 2. Bias on the forms/types of data collected 3. Bias in how the data is presented |
| Why does bigger data not always equal better data? (2) | 1. Volume is so large that little things can go unnoticed 2. Smaller data sets and outliers can tell us things big data may overlook |
| Why does big data require context for it to have Value? (2) | 1. Volume of data mandates explanation to understand connections between the data 2. Context grants insight into how the Value of the data can be applied |
| Why is accessible data not always ethical to use? Give an example. (2) | 1. Researchers should be accountable towards their research subjects (e.g. informed consent) 2. Examples: Gleaning data off of public social media profiles, using identifying details gained from public records etc. |
| What is the Digital Divide and how can Big Data worsen it? (2) | 1. Digital Divide: Advantages / Necessity of technology puts those without access to it at a disadvantage 2. Big Data holds so much power that those without it can be at a disadvantage |
| What are two concerns / issues that arise from Big Data? (2) | 1. Privacy and increased surveillance 2. Ethical use of data 3. Worsens the Digital Divide |
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