DSC650: Business Motivations for Big Data
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Questions and Answers

What is a key benefit of collecting secondary data in a digitized medium for businesses?

  • It decreases infrastructure costs.
  • It enables customized marketing and product optimization. (correct)
  • It allows for better financial tracking.
  • It enhances employee satisfaction.

Which aspect of data science is involved in improving business decision-making?

  • Machine learning and predictive analytics. (correct)
  • Employee training and development.
  • Financial auditing and assessments.
  • Manual data entry processes.

What technological advancement has made processing large quantities of diverse data more accessible?

  • Proprietary software solutions.
  • Traditional mainframe computers.
  • Increased manual data processing.
  • Commodity hardware and open-source software. (correct)

What is one of the methods data scientists use to analyze collected data?

<p>Statistical estimation techniques. (D)</p> Signup and view all the answers

How can businesses benefit from analyzing the 'voice of the customer'?

<p>By improving services, increasing sales, and creating new products. (B)</p> Signup and view all the answers

Which factor does NOT significantly contribute to the need for data analytics and data science in businesses?

<p>High costs of traditional data storage. (A)</p> Signup and view all the answers

What is one key aspect of 'Marketplace Dynamics' in the context of big data adoption?

<p>It involves making decisions about corporate strategy. (B)</p> Signup and view all the answers

Which of the following best describes 'Business Process Management'?

<p>A description of how work is performed within an organization. (B)</p> Signup and view all the answers

Which process is part of data science utilized to tackle big data?

<p>Data cleansing and preparation. (C)</p> Signup and view all the answers

How does big data technology influence corporate strategy according to Marketplace Dynamics?

<p>By providing insights that can shape corporate priorities. (C)</p> Signup and view all the answers

What is a distinct feature of Business Process Management in relation to organizational structure?

<p>It combines business processes with organizational roles and resources. (A)</p> Signup and view all the answers

What role does feedback play in the interaction between stakeholders and big data adoption?

<p>Feedback creates a virtuous cycle that aligns the organization. (A)</p> Signup and view all the answers

Which of the following is NOT listed as a driver for big data adoption?

<p>Increased manual processes (A)</p> Signup and view all the answers

What is a primary goal of using data analytics in a business context?

<p>To inform decision-making based on data-driven insights. (C)</p> Signup and view all the answers

What is a consequence of effective Business Process Management using big data?

<p>Streamlined workflows that enhance efficiency. (D)</p> Signup and view all the answers

How does digitization relate to big data adoption in businesses?

<p>It simplifies the data collection process for analysis. (B)</p> Signup and view all the answers

How does digitization benefit businesses?

<p>By saving time and cost through digital delivery (D)</p> Signup and view all the answers

What opportunity does digitization provide through customer interactions?

<p>Collection of secondary data for further insights (A)</p> Signup and view all the answers

What is the main role of Big Data in the Internet of Everything (IoE)?

<p>To serve as the foundation for IoE functionalities (C)</p> Signup and view all the answers

What is an example of IoE in practice?

<p>GPS-controlled tractors in precision agriculture (B)</p> Signup and view all the answers

Which of the following is NOT a trend associated with digitization?

<p>Increased physical product distribution (A)</p> Signup and view all the answers

How does the Internet of Everything combine business processes?

<p>By integrating smart devices into meaningful business processes (A)</p> Signup and view all the answers

What distinguishes IoE from IoT?

<p>IoE includes a broader range of connected services (C)</p> Signup and view all the answers

What is a key advantage of using digitization for marketing?

<p>Ability to track customer interactions and preferences (D)</p> Signup and view all the answers

What is the primary role of a data analyst?

<p>To visualize data and communicate insights from data (D)</p> Signup and view all the answers

Which of the following best differentiates a data analyst from a data scientist?

<p>Data scientists forecast future patterns while data analysts provide answers to existing questions. (A)</p> Signup and view all the answers

What has led to an increased demand for techniques that extract meaningful information from data?

<p>The growth of computational approaches and data warehousing technologies (C)</p> Signup and view all the answers

How can businesses benefit from collecting secondary data?

<p>By enabling customized marketing and automated recommendations (C)</p> Signup and view all the answers

What are businesses increasingly interested in incorporating for better data analytics?

<p>Publicly available datasets from social media and external sources (B)</p> Signup and view all the answers

Which technology advancements have contributed to the affordability of data processing?

<p>Commodity hardware combined with open-source software (B)</p> Signup and view all the answers

What type of analysis is increasingly utilized to enhance customer service and marketing efforts?

<p>Voice of the customer analysis (B)</p> Signup and view all the answers

What infrastructure strategy is mentioned for better utilization of data analysis resources?

<p>Leveraging cloud computing to create on-premise clouds through virtualization (A)</p> Signup and view all the answers

Flashcards

Marketplace Dynamics

The dynamic interplay between organizations and their stakeholders, encompassing actions, reactions, and interactions within the market.

BI vs Data Analytics

Business Intelligence (BI) focuses on historical data and reporting to gain insights into past performance. Data Analytics emphasizes examining present and future data to identify trends and predict outcomes.

Virtuous Cycle in Organizations

A continuous cycle where data is collected, analyzed, used to make informed decisions, and then those decisions drive further data collection and analysis.

Business Process Management

The process of using data to understand, manage, and optimize the workflows and activities within an organization.

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Business Process

A detailed description of how a specific task or activity is performed within an organization.

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Digitization

The shift from physical to digital mediums for communications and delivery, leveraging pre-existing internet infrastructure for efficiency and cost savings.

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Secondary Data

The collection of data beyond the initial transaction, often through user interactions, surveys, or tracking website activity.

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Data Mining

The process of using secondary data to personalize marketing, make recommendations, and optimize product features.

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Internet of Everything (IoE)

The convergence of technology advancements, market trends, business processes, and business models to create interconnected systems.

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Big Data in IoE

The core of IoE, enabling the collection, analysis, and processing of vast amounts of data from connected devices.

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Internet of Things (IoT)

Smart, connected devices that provide data and functionality to IoE systems.

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IoE for Business Processes

Transforming IoT data into actionable insights, enabling efficient business processes and personalized value propositions.

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Precision Agriculture

A specific IoE application in agriculture, where data from sensors and devices optimizes farm productivity and cost efficiency.

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What is Data Science?

The process of gathering, cleaning, preparing, and analyzing large datasets using various techniques, including machine learning, predictive analytics, and sentiment analysis, to extract meaningful insights and drive business decisions.

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What is Data Analytics?

The practice of examining raw data to extract meaningful information and insights, often using statistical methods to identify patterns, trends, and relationships within the data.

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Public Data Sources

Businesses are increasingly interested in using publicly available datasets from social media and other external sources to gain deeper insights about customer behavior, market trends, and competitor activity.

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Cloud Computing

Businesses are adopting cloud computing solutions to create on-premise clouds to more effectively utilize infrastructure resources, enabling them to scale storage and processing capabilities.

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Affordable Big Data Storage and Processing

The ability to store and process vast quantities of diverse data has become less expensive due to advancements in hardware and software technologies.

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Secondary Data Analysis

Mining data from digitized sources can allow businesses to personalize marketing campaigns, automate recommendations, and optimize product features based on individual customer preferences and behavior.

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Business Need for Data Analytics

The need for techniques and technologies that can extract meaningful information and insights from large datasets has increased significantly.

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What does a data scientist do?

Data scientists are focused on using algorithms and machine learning to build predictive models, forecast future trends, and create new solutions. Their main goal is to answer "why" questions and uncover hidden patterns.

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What does a data analyst do?

Data analysts primarily focus on answering "what" questions. They analyze existing data to understand trends, identify insights, and communicate findings effectively. They are skilled in data visualization and reporting.

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Why do businesses collect and analyze data?

Businesses collect and analyze data in digital formats to personalize marketing efforts, offer tailored recommendations, and optimize product features. This data-driven approach enhances customer experience and business outcomes.

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What has made data analytics and data science more accessible?

The increased availability of affordable technology, including commodity hardware and open-source software, has enabled easier storage and processing of large volumes of data, fueling the growth of data analytics and data science.

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How do businesses use publicly available datasets?

Social media and other external data sources are valuable for businesses to understand customer sentiment, improve services, target marketing campaigns, and develop new products that cater to specific needs.

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How does the Internet of Things (IoT) impact data analytics?

The increasing number of internet-connected devices (IoT) generates massive amounts of data. Leveraging cloud computing and virtualization enhances infrastructure, storage, and processing capabilities to handle this influx of data.

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Study Notes

Lecture 2: Business Motivations and Drivers for Big Data Adoption

  • Course: DSC650: Data Technology and Future Emergence
  • Lecturer: Dr. Khairul Anwar Sedek
  • Topics covered in this lecture include business motivations and drivers for big data adoption.

DSC650 Course Contents

  • Overview of Data Technology
  • Business Motivations and Drivers for Big Data Adoption
  • Data Storage Technology
  • Data Munging
  • NoSQL
  • Searching and Indexing Big Data
  • Big Data Technologies
  • Trend in Data Technology

Lecture 2: Business Motivations and Drivers for Big Data Adoption

  • Learning Objectives: Students should be able to demonstrate understanding of basic concepts and practices of big data technology.
  • Key Topics:
    • Marketplace Dynamics (2.1)
    • Business Process Management (2.2)
    • Data Analytics & Data Science (2.3)
    • Digitization (2.4)
    • Internet of Everything (2.5)

2.1 Marketplace Dynamics

  • Evolution of information systems (from 1970s to 2010s) shows a shift from operational reporting to operational, tactical, and strategic reporting, analysis, and visualization with predictive capabilities.
  • Limitations of previous systems included data silos, limited integration, and mostly printed reports.
  • Big data has facilitated democratization, providing more data to more people with better insights.
  • Mostly used by data scientists, the systems are mostly standalone, leading to a more interactive data experience.

2.2 Business Process Management

  • Business processes describe how work is performed within an organization, encompassing activities and relationships of actors within the organization.
  • Models are connected with models of roles, structure, entities, relationships, business rules, and user interfaces.
  • Detailed analysis can cluster customers by contact method, streamlining interactions with systems like the contact center, customer-facing systems, and back office systems.

2.3 Data Analytics & Data Science

  • The need for techniques and technologies to extract meaningful information and insights has increased.
  • Computational and statistical techniques have merged with data warehouses, introducing new tools for Big Data analysis.
  • Key skills: computer science, machine learning, software development, big data software (Hadoop, Spark), algorithm development, basic statistical analysis, programming languages (Java, Python, R, SQL), data mining, problem-solving, data storytelling.
  • Data analytics is the entry level of data science, focusing on descriptive statistics, data visualization, and communicating data to derive conclusions.

2.4 Digitization

  • Digital mediums replace physical mediums for communication and delivery, saving time and costs.
  • Digitization supports secondary data collection, enabling customized marketing, automated recommendations, and product optimization.
  • Examples: online banking, on-demand TV, streaming video.

2.5 Internet of Everything (IoE)

  • IoE is the convergence of advancements in information and communication technology, impacting business architecture and processes.
  • Big data is central to IoE.
  • IoE integrates services from smart connected devices into meaningful business processes.
  • Example: Precision agriculture (GPS-controlled tractors, sensors, on-demand watering, fertilization, and seeding equipment) to maximize field productivity and minimize costs.
  • IoE taxonomy includes reactive, adaptive, autonomous, and collaborative levels of smartness in increasing order.
  • IoE architecture involves networked connections of people, process, data, and things.
  • Key aspects: Turning data into more useful information for decision-making, delivering the right information to the right person or machine at the right time, connecting people in more relevant, valuable ways, and integrating physical devices and objects with the Internet for intelligent decision-making.

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Lecture 2: Business Motivations and Drivers for Big Data Adoption PDF

Description

This quiz focuses on the business motivations and drivers for big data adoption as covered in Lecture 2 of the DSC650 course. Participants will explore key concepts such as marketplace dynamics, business process management, and the role of data analytics in modern business practices.

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