DSC650: Data Technology Overview

<p>It is hierarchical or graph-based. (D)</p> Signup and view all the answers

<p>Unstructured data (D)</p> Signup and view all the answers

<p>Velocity (B)</p> Signup and view all the answers

<p>The large amount of data (D)</p> Signup and view all the answers

<p>Simplicity (D)</p> Signup and view all the answers

<p>Structured, semi-structured, and unstructured data (A)</p> Signup and view all the answers

<p>It must be turned into something valuable for insights (D)</p> Signup and view all the answers

<p>Data management solutions and services (D)</p> Signup and view all the answers

<p>Multiple, often unrelated sources (A)</p> Signup and view all the answers

<p>Data Analyst (D)</p> Signup and view all the answers

<p>They need to be updated to remain effective. (C)</p> Signup and view all the answers

<p>Prescriptive analytics prescribes actions based on reasons. (A)</p> Signup and view all the answers

<p>Structured data. (D)</p> Signup and view all the answers

<p>Banking transactions. (C)</p> Signup and view all the answers

<p>Prescriptive analytics. (D)</p> Signup and view all the answers

<p>Web logs. (D)</p> Signup and view all the answers

<p>Determining the best selling price for a product. (A)</p> Signup and view all the answers

<p>Operational optimization (A)</p> Signup and view all the answers

<p>Unstructured data. (B)</p> Signup and view all the answers

<p>A collection where each member shares the same attributes (B)</p> Signup and view all the answers

<p>To support better decision making (A)</p> Signup and view all the answers

<p>By linking sales data to trends like temperature (C)</p> Signup and view all the answers

<p>An extract of rows from a database in CSV format (D)</p> Signup and view all the answers

<p>Verification (B)</p> Signup and view all the answers

<p>Finding facts, relationships, patterns, insights and trends (D)</p> Signup and view all the answers

<p>Using predictive analytics to optimize ice-cream orders (C)</p> Signup and view all the answers

<p>To understand past events. (D)</p> Signup and view all the answers

<p>Diagnostic analytics (D)</p> Signup and view all the answers

<p>Descriptive, Diagnostic, Predictive, Prescriptive (D)</p> Signup and view all the answers

<p>Why have sales declined in Q2? (C)</p> Signup and view all the answers

<p>To identify the best course of action. (B)</p> Signup and view all the answers

<p>Forecasting future events (C)</p> Signup and view all the answers

<p>Diagnostic analytics (B)</p> Signup and view all the answers

<p>The management of the complete data lifecycle. (B)</p> Signup and view all the answers

The use of technology to manage, analyze, and create value from data, encompassing areas like marketing and advertising.

The collection, processing, and analysis of massive datasets, often diverse and rapidly changing, to uncover valuable insights.

The different types and formats of data that make up Big Data, including structured, semi-structured, and unstructured data.

The speed at which data is generated, collected, and processed in Big Data.

The sheer size and scale of data in Big Data, often exceeding the capabilities of traditional data management systems.

The accuracy and reliability of data in Big Data, often a challenge due to inconsistencies and uncertainty.

Describes the potential value that can be extracted from Big Data, such as improved decision-making, new business models, and enhanced efficiency.

A combination of related datasets from different sources, analyzed together to reveal hidden patterns and insights.

Data that does not conform to a predefined data model or schema. It lacks a rigid structure. Examples include text files, images, audio, and video.

Data that has some level of structure but is not relational. It's organized hierarchically or in a graph-like manner. Examples include XML and JSON files.

Data that is organized in a structured format, often in rows and columns. It's easily analyzed using traditional methods. Examples include databases.

The process of analyzing large datasets to identify patterns, trends, and insights. This helps businesses make better decisions and solve problems.

The ecosystem encompasses the tools, technologies, and processes used for managing, analyzing, and extracting value from large datasets.

Understanding and interpreting patterns, trends, insights, and relationships within data to make informed decisions.

A collection of related data points with similar characteristics.

Optimizing processes, improving efficiency, and enhancing performance based on data insights.

Discovering new potential customers, markets, or opportunities based on data analysis.

Using data to predict future outcomes, trends, or events.

Analyzing data to detect unusual patterns or anomalies that might indicate errors, fraud, or security threats.

Making informed, data-backed choices to enhance decision-making quality.

Predictive analytics uses past data to forecast future outcomes.

Predictive models rely on existing conditions. If those conditions change, the models need updating.

It goes beyond predictions, recommending specific actions to take.

Prescriptive analytics considers not only the best action but also the reasons behind it.

This type of data follows a specific structure and format, often stored in tables.

Examples include web logs, sensor data, and machine telemetry.

Examples include social media posts, emails, and blog comments.

Structured data often comes from business applications like ERP and CRM systems.

These are categorized as Descriptive, Diagnostic, Predictive and Prescriptive.

Descriptive analytics focuses on understanding past events by summarizing and organizing information. It helps answer questions about what happened.

Descriptive analytics tools extract data from operational systems and present it through reports and dashboards to provide insights.

Diagnostic analytics delves into the reasons behind past events by uncovering the cause and effect relationships. It focuses on understanding 'why' things happened.

Diagnostic analytics can be used to perform drill-down (deeper analysis) and roll-up (summarizing) analysis to understand data patterns.

Predictive analytics aims to predict future events by identifying trends and patterns from past data. It helps make predictions about what might happen.

Prescriptive analytics provides recommendations and actionable strategies based on predictive insights. It aims to advise on the best course of action to take.