Big Data

Study Big Data

Big Data

Refers to a large amount of data that exceeds the capacity of a single computer, requiring specialized techniques for handling and analysis.

Data

Information in the form of characters, symbols, or numeric values necessary for computer operations, which can be transmitted as electric signals and stored in various devices.

Structured Data

Data organized in a relational database with unique identifiers, typically existing in rows and columns for easy analysis.

Unstructured Data

Data lacking a specific structure or order, making it challenging for analysis but potentially valuable for business intelligence.

Semi-Structured Data

Data with relational values and organization that can be analyzed, such as text marked up with descriptions like XML in a document.

Volume

The total quantity of data stored, which has rapidly increased, collected from various sources like business transactions and social media platforms.

Velocity

Refers to the speed at which data is created and collected, requiring processes and systems to cope with vast amounts of data.

Variety

The breadth of data sources analyzed, including different types of data related to customers, manufacturing processes, and industry.

Variability

Refers to inconsistencies in data that need to be identified for meaningful analytics, influenced by multiple data types and sources.

Veracity

Indicates the quality of data, emphasizing the importance of consistent and correct data for reliable analysis and decision-making.

Value

The most crucial characteristic of big data, highlighting the necessity of deriving value and achieving organizational goals through data analysis.

Data Fusion and Data Integration

Refers to the analysis of data from multiple sources to improve accuracy and results compared to single-source analysis.

Data Mining

Technique to extract useful information from large datasets, identifying trends and patterns for various applications like spam filtering and fraud detection.

Machine Learning

Subset of artificial intelligence using algorithms to make predictions based on large datasets, with models improving over time.

Natural Language Processing (NLP)

Technique using algorithms to analyze human languages, including translation, speech recognition, and question answering.

Statistics

Approach supporting data analysis, where statistical techniques can be applied to both small and large datasets.

Sampling

Process of taking a sample from a dataset to make estimates and predictions about the entire dataset.

Divide and Conquer

Method of dividing a dataset into smaller blocks for easier analysis, with results combined to analyze the whole dataset.

Big Data Visualization

Techniques to present data graphically for better understanding and communication, aiding decision-making processes.

Industry 4.0

Manufacturing concept using smart technologies and big data analysis to maximize production, reduce costs, and customize production based on demand.

Predictive Analytics

Utilizes big data to identify patterns that can predict future events, aiding in decision-making processes.

Big Data Implementation

Requires investment in solutions and hiring experts for data collection, storage, and processing.

Hyper-scale Computing Environments

Utilize dedicated servers, storage, and processing frameworks like Hadoop for big data storage and analysis.

Cloud Servers

Provide flexible storage options, though may impact latency, suitable for backup and scalable needs.

Descriptive Analytics

Involves data aggregation and mining to summarize findings and reveal underlying meanings in large datasets.

Predictive Analytics

Builds models on descriptive data to predict future outcomes based on current data trends.

Prescriptive Analytics

Goes beyond predictive analytics by suggesting multiple courses of action or possible outcomes for a specific goal.

Data Quality

Challenges in big data analysis related to the accuracy and relevance of data, influenced by veracity and data sources.

System Compatibility

Obstacles in data analysis due to the need to integrate data from various systems or processes.

Skills Gaps

Lack of skilled professionals in data analysis, emphasizing the importance of having the right competencies for effective implementation.

GDPR

The EU General Data Protection Regulation ensuring data protection and privacy compliance in data analysis.