Big Data Analytics Overview

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Questions and Answers

What is big data analytics?

The use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and unstructured data.

Which of the following are advanced analytics techniques? (Select all that apply)

Machine learning (correct)

Data mining (correct)

Text analytics (correct)

Basic monitoring

What does predictive modeling aim to determine?

Future outcomes.

Advanced analytics requires big data to function.

False Signup and view all the answers

What type of data can big data consist of?

Structured, semi-structured, and unstructured data. Signup and view all the answers

Which of the following is NOT a type of text data?

Photographs Signup and view all the answers

How do companies like Amazon and Google benefit from big data analysis?

They gain a competitive advantage. Signup and view all the answers

What is the purpose of operationalized analytics?

To integrate analytics into a business process. Signup and view all the answers

What is big data analytics?

The use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and unstructured data. Signup and view all the answers

Which of the following are advanced analytics techniques? (Select all that apply)

Predictive analytics Signup and view all the answers

Big data analytics only pertains to structured data.

False Signup and view all the answers

What is the purpose of basic analytics?

To explore data and identify its potential value. Signup and view all the answers

What does 'slicing and dicing' refer to in big data analytics?

Breaking down data into smaller, more manageable sets. Signup and view all the answers

What is predictive modeling?

A statistical or data-mining solution used to determine future outcomes. Signup and view all the answers

Which company is noted for mastering big data analytics?

Amazon Signup and view all the answers

In operationalized analytics, analytics is a part of a ____ process.

business Signup and view all the answers

Which of the following are examples of text data? (Select all that apply)

E-mails Signup and view all the answers

What is the signal-to-noise ratio in big data?

It can be low. Signup and view all the answers

Study Notes

Big Data Analytics

Big data analytics is the process of examining large and complex datasets to uncover patterns, trends, and insights.
It involves collecting, storing, processing, and analyzing massive amounts of data from various sources.
The goal is to extract valuable information that can be used to improve decision-making, optimize operations, and gain a competitive advantage.

Advanced Analytics Techniques

Advanced analytics techniques go beyond basic reporting and descriptive statistics. They use sophisticated algorithms and models to uncover hidden relationships and predict future outcomes.
Examples include:
- Predictive modeling: Predicting future events or outcomes based on historical data.
- Machine learning: Training algorithms to learn from data and make predictions or decisions.
- Deep learning: A type of machine learning that uses artificial neural networks to analyze complex patterns.
- Natural language processing (NLP): Understanding and processing human language.
- Prescriptive analytics: Providing recommendations for action based on data analysis.

Predictive Modeling

Predictive modeling aims to determine the likelihood of future events or outcomes.
It involves building statistical models that identify relationships between variables and use them to make predictions.
Examples:
- Predicting customer churn
- Forecasting product demand

Big Data

Big data is not limited to structured data. It can consist of various data types, including:
- Structured data: Organized in rows and columns like in a database.
- Semi-structured data: Has some organization but not as strict as structured data.
- Unstructured data: Does not follow a predefined structure.
Big data is characterized by its volume, velocity, variety, and veracity.

Text Data

Examples of text data include:
- Social media posts
- Customer reviews
- Email messages
- Website content
Audio is NOT a type of text data.

Benefits of Big Data Analysis

Companies like Amazon and Google benefit from big data analysis by:
- Personalizing customer experiences: Tailoring recommendations and offers to individual preferences.
- Improving operational efficiency: Identifying bottlenecks and optimizing processes.
- Developing new products and services: Gaining insights into market trends and customer needs.

Operationalized Analytics

Operationalized analytics integrates analytics into a business process to enable real-time decision-making.
It involves automating data collection, analysis, and reporting to ensure continuous insights and actionable information.

Basic Analytics

Basic analytics focuses on descriptive analysis, summarizing and visualizing data to understand past trends and patterns.
It includes reporting on key performance indicators (KPIs), creating charts, graphs, and dashboards.

Slicing and Dicing

'Slicing and dicing' refers to the ability to explore data from different angles using filters and dimensions.
It helps to uncover hidden patterns and understand the relationships between variables.

Google and Big data analytics

Google is particularly adept at big data analytics.
They use it for:
- Search engine optimization
- Targeted advertising
- Predictive maintenance

Signal-to-Noise Ratio

In big data, the signal-to-noise ratio refers to the proportion of meaningful data (signals) versus irrelevant data (noise).
A high signal-to-noise ratio indicates that the data contains valuable insights, while a low ratio implies that the data is mostly noisy and difficult to analyze.

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Description

This quiz explores the definition, introduction, and types of big data analytics. It highlights how businesses utilize advanced analytic techniques to derive insights from diverse datasets and the competitive advantages gained through such analysis. Test your knowledge on big data concepts and applications!