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Questions and Answers

Which type of data visualization is most effective for illustrating the proportion of different budget allocations within a company?

  • Line Chart
  • Gantt Chart
  • Pie Chart (correct)
  • Bar Chart

Which of the following tools is best suited for a project manager who needs to monitor both actual and projected timelines for various project activities?

  • Gantt Chart (correct)
  • Bar Chart
  • Pie Chart
  • Line Chart

A marketing analyst wants to identify trends in website traffic over the past year. Which data visualization tool would be the most appropriate?

  • Line Chart (correct)
  • Pie Chart
  • Bar Chart
  • Gantt Chart

A retail company wants to compare the sales performance of different product categories in the last quarter. Which data visualization method would be most suitable?

<p>Bar Chart (C)</p> Signup and view all the answers

Which type of software is specifically designed to transform raw data into visually accessible graphical representations for quick understanding?

<p>Data Visualization Software (C)</p> Signup and view all the answers

Which of the following tools would be most effective for a business analyst who needs to explore data from various perspectives to identify key insights?

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

A company is trying to identify previously unnoticed patterns in their customer data to improve marketing strategies. Which analytical approach is most appropriate?

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

A project manager needs to assess whether current team resources are sufficient to meet upcoming project deadlines. Which tool will best support this evaluation?

<p>Gantt Chart (C)</p> Signup and view all the answers

Which of the following best describes the primary responsibility of a Data Owner?

<p>Implementing security controls and defining data access rules. (D)</p> Signup and view all the answers

A company wants to improve its strategic decision-making using big data. According to the information provided, what sequence of steps would most likely lead to improved decisions?

<p>New Insights -&gt; New Strategies -&gt; New Decisions -&gt; New Actions. (D)</p> Signup and view all the answers

When dealing with big data, ensuring that the data is verified based on accuracy and content relates to which of the '7Vs'?

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

A marketing company is gathering data from social media posts, online reviews, and customer surveys to understand consumer sentiment. Which characteristic of big data is most emphasized in this scenario?

<p>Variety (C)</p> Signup and view all the answers

An organization is analyzing sensor data from its manufacturing equipment in real-time to detect anomalies and predict maintenance needs. This scenario primarily highlights which characteristic of big data?

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

Which of the following is the best example of structured data?

<p>Sales orders and invoices stored in a company's database. (C)</p> Signup and view all the answers

A company decides to invest in big data analytics to gain a competitive advantage. From a strategic perspective, how should big data be viewed?

<p>As a new data asset and strategic asset. (D)</p> Signup and view all the answers

A data analyst is tasked with presenting findings from a large dataset to a non-technical audience. Which of the '7Vs' of big data is most relevant to this task?

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

Which of the following reflects the primary function of Business Intelligence (BI)?

<p>To analyze data for actionable insights that support better decision-making. (B)</p> Signup and view all the answers

Why is the absence of Business Intelligence (BI) potentially detrimental to an organization?

<p>It may result in missed opportunities for growth and potential business decline. (A)</p> Signup and view all the answers

Which of the following is NOT typically considered a component of Business Intelligence?

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

A company notices a sudden drop in sales in a specific region. How could business intelligence (BI) be used to address this issue?

<p>By using data mining tools to identify patterns and potential causes of the sales drop. (C)</p> Signup and view all the answers

Which BI tool would be most suitable for presenting complex sales data to the executive team in an easily understandable format?

<p>Data visualization software (A)</p> Signup and view all the answers

A retail company wants to identify customer segments with the highest purchase frequency. Which BI tool would be most effective for this task?

<p>Data mining tools to uncover patterns in customer behavior. (D)</p> Signup and view all the answers

A business is using spreadsheets for its initial BI efforts. What is a limitation they might encounter as their data volume and analytical needs grow?

<p>All of the above (D)</p> Signup and view all the answers

How can integrating data visualization software into a BI strategy enhance decision-making processes?

<p>By enabling stakeholders to quickly identify trends and insights through visual representations. (B)</p> Signup and view all the answers

Which of the following best describes the 'Discover' phase within the data life cycle?

<p>Preparing, exploring, and modeling data for analysis. (D)</p> Signup and view all the answers

In the context of big data, why is it important to understand that 'big data does not necessarily mean good data'?

<p>Because data quality and relevance are crucial for meaningful analysis. (B)</p> Signup and view all the answers

The data life cycle is represented as Data -> Analytics -> Insights -> Results -> Discussions. What is the primary goal of the 'Analytics' stage in this cycle?

<p>To cleanse, normalize, aggregate, extract, and perform analysis on the data. (C)</p> Signup and view all the answers

How does the size and complexity of an organization affect the definition of 'Big Data'?

<p>'Big Data' definition is subjective, varying based on the organization's capabilities and resources. (A)</p> Signup and view all the answers

What is the ultimate aim of the 'Results' stage in the data life cycle?

<p>Improving decisions, actions, and outcomes to achieve benefits. (C)</p> Signup and view all the answers

What distinguishes the 'Deploy' phase from the 'Discover' phase in the data life cycle?

<p>The 'Deploy' phase focuses on implementation, action, and evaluation, whereas the 'Discover' phase involves preparing, exploring, and modeling. (B)</p> Signup and view all the answers

In the context of the data life cycle, which stage directly precedes the 'Results' stage and feeds into improved decision-making?

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

Which activity most directly represents the 'Analytics' phase of the data life cycle?

<p>Cleaning and transforming raw sales data. (D)</p> Signup and view all the answers

Which scenario exemplifies the risk of 'analysis paralysis' in big data?

<p>A marketing team collects extensive customer data but struggles to derive actionable insights due to the sheer volume and complexity, delaying critical decisions. (C)</p> Signup and view all the answers

A company is using historical sales data to predict future demand for its products. Which type of data analytics is being employed?

<p>Predictive Analytics (C)</p> Signup and view all the answers

A hospital administrator is deciding between two different patient care strategies based on their potential outcomes. Which type of data analytics would be most helpful in making this decision?

<p>Prescriptive Analytics (A)</p> Signup and view all the answers

A retail company implements a system that uses machine learning to understand customer sentiment from social media posts and provide personalized recommendations. Which type of data analytics is being utilized?

<p>Cognitive Analytics (A)</p> Signup and view all the answers

Which of the following scenarios exemplifies a failure in data 'relevance' within a marketing campaign?

<p>A marketing team sends promotional emails to customers who have unsubscribed from their mailing list. (A)</p> Signup and view all the answers

A research study relies on data collected from a biased online survey. This scenario primarily threatens which aspect of data quality?

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

A global company struggles to combine sales data from different regional databases due to inconsistencies in formatting and units of measurement. Which data quality dimension is most affected?

<p>Coherence (C)</p> Signup and view all the answers

An organization's data is stored in a format that requires specialized software to access and interpret. This primarily affects which characteristic of data quality?

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

Which role is responsible for defining, specifying, and standardizing an organization's data assets across all functional areas?

<p>Data Governance Manager (A)</p> Signup and view all the answers

Which type of data primarily includes internal source documents like sales orders, invoices, and production records?

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

The characteristic of data that encompasses all forms and formats, whether generated internally or sourced externally, is best described as:

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

Which of the following is NOT typically considered a core Data Quality Standard?

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

SAMSUNG analyzes past revenue data (2022-2024) and uses trend and sensitivity analyses to project 2025 revenues to boost sales. What type of data analytics is this?

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

APPLE reports a consistently low employee turnover rate (5.1% from 2022-2024). HR is pleased and anticipates continued improvement. What type of data analytics does this scenario illustrate?

<p>Descriptive Analytics (C)</p> Signup and view all the answers

HUAWEI's failure to account for reworks in 2023 led to $10 million in losses and the subsequent replacement of the operations manager. Which type of data analytics BEST describes this situation?

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

VIVO implemented Robotics Process Automation (RPA), leading to a 45% reduction in production costs in 2024 due to improved efficiency. What type of data analytics is demonstrated here?

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

Flashcards

Business Intelligence (BI)

Collecting and analyzing internal and external data to uncover new insights.

BI as Decision Support

A technology-driven process for analyzing data to provide actionable information for better decision-making.

Components of Business Intelligence

Data mining, process analysis, performance benchmarking, and descriptive analytics.

BI Outputs

Easy-to-understand reports, performance measures, and trends derived from business data.

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Spreadsheets in BI

One of the most common and widely used BI tools for data analysis.

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Reporting Software

Software used to organize, filter, and display data in a structured format.

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Data Visualization Software

Software that transforms data into graphical representations for quick insights.

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Data Mining Tools

Tools that use AI, machine learning, and statistics to discover patterns in large datasets.

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OLAP (Online Analytical Processing)

Analyzing datasets from multiple angles based on different business viewpoints.

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Bar Chart

A visual representation comparing two or more items.

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Gantt Chart

A graphical tool illustrating a project's schedule, highlighting activities and timelines.

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Pie Chart

A chart representing parts of a whole, showing each item's percentage.

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Line Chart

A chart displaying trends and movements of variables over time.

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Business Intelligence

The process of collecting and analyzing data to uncover hidden insights.

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Bar Chart Use

Assessing if current resources meet demand or if actions need rescheduling

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Data Owner

The person or department responsible for safeguarding data, classifying it, and defining access rules.

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Data Steward/Custodian

The person or department responsible for data asset standardization and management.

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Big Data

Internal and external data leading to new possibilities.

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Structured Data

Internal documents like sales orders and invoices.

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Unstructured Data

External documents like online content and audio.

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Volume

The amount of data being created.

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Variety

Data from all forms and formats.

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Velocity

Data being generated extremely quickly.

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Data Life Cycle

The process that data undergoes, from generation to analysis and action.

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Discover (in Data Life Cycle)

Encompasses tasks in preparing, exploring, and modeling data.

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Deploy (in Data Life Cycle)

Encompasses tasks such as implementation and evaluation.

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Data (in Data Life Cycle)

Collection and generation of data ready for processing.

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Analytics (in Data Life Cycle)

Cleansing, normalizing, aggregating, extracting, and analyzing data.

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Results (in Data Life Cycle)

Improving decisions, actions, and outcomes to realize new benefits.

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Decision

Arriving at the appropriate course of action to solve a problem or answer a query

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Data Asset Steward

An employee who defines, specifies, and standardizes an organization's data assets across all functional areas.

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Variety (of Data)

The characteristic of data reflecting its diversity of forms and formats, including data generated internally and from external/public sources.

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Predictive Analytics

Analyzing past data to identify patterns and trends in order to forecast future outcomes.

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Risks of bad data

Incorrect or flawed information leading to poor decisions.

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Descriptive Analytics

Analyzing historical data to understand past performance, trends, and patterns.

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Impact of Bad Data

Discovering wrong or bad data that will in turn result in bad decisions

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Analysis paralysis

Becoming overwhelmed by data analysis, resulting in delayed or poor decision-making.

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Invalid data patterns

Believing false patterns exist in your data, leading to wasted time, money, or resources.

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Data Quality Standard

Relevance is the data quality standard.

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Prescriptive analytics

Recommending actions based on data insights.

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Lack of data governance

When data management isn't standardized, it leads to inaccurate and unreliable information

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Data Quality Standards

Data must be relevant, accurate, credible, timely, accessible, interpretable and coherent.

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Data Analytics

Using tools to extract insights and opportunities from big data.

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Cognitive Analytics

Using AI to enhance day-to-day business operations.

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Study Notes

  • Business Intelligence (BI) collects and analyzes internal and external data to provide new, previously unseen business insights.
  • Gathering BI data can create new business development opportunities and growth.
  • Not gathering BI data can lead to business downturns and declines.
  • Business intelligence (BI) is a technology-driven process that analyzes business data.
  • This BI data provides actionable information for executives and managers to make better-informed business decisions.
  • Business intelligence covers data mining, process analysis, performance benchmarking, and descriptive analytics.
  • BI parses all business-generated data and presents easy to digest reports, performance measures, and trends for management decisions.

Advantages of Business Intelligence Tools

  • Relevant and accurate reporting is a key advantage.
  • Gaining key insights into data is another significant benefit.
  • BI helps businesses stay ahead in the competitive landscape.
  • BI ensures quality and accurate data for decision-making.
  • Improved customer satisfaction is often a result of effective BI.
  • BI can improve growth patterns within a business.
  • Efficiency and accuracy are enhanced through BI implementation.
  • Faster decision-making processes are facilitated by BI tools.
  • BI leads to greater operational efficiency and increased revenue.
  • Ultimately, BI contributes to bigger profits for the organization.

Types of BI Tools and Software

  • Spreadsheets, such as Microsoft Excel and Google Docs, are widely used BI tools.
  • Reporting software is used to report, organize, filter, and display data.
  • Data visualization software translates datasets into easy-to-read, visually appealing graphical representations.
  • Data mining tools "mine" large amounts of data for patterns using artificial intelligence, machine learning, and statistics.
  • Online analytical processing (OLAP) tools allow users to analyze datasets from various angles based on different business perspectives.

Data Visualization Tools

  • Bar charts are commonly used for demonstrating comparisons between two or more things.
  • Bar charts allow managers evaluate whether existing resources can handle workload or if activities should be postponed.
  • Gantt charts are graphical illustrations of scheduling techniques.
  • Gantt charts highlight activities over the life of a project.
  • Gantt charts show a quick picture of a project's progress with timelines.
  • Pie charts represent a 100% total of two or more items.
  • Pie Charts shows the portion of one item over the whole.
  • Line charts are used to represent a trend between two or more items.
  • Line charts show the movements of two variables across points in time.

Big Data

  • Big Data means vast amounts of data collected from a variety of sources.
  • Big data is subjective, depending on an organization's size and complexity.

Data Life Cycle

  • Data Life Cycle = Discover + Deploy.
  • "Discover" focuses on tasks such as preparing, exploring, and modeling.
  • "Deploy" focuses on tasks such as implementation, action, and evaluation.
  • A detailed orientation of data life cycle includes: Data -> Analytics -> Insights -> Results -> Discussions
  • "Data" refers to the Data collection and generation which can then be processed.
  • "Analytics" means cleansing, normalizing, aggregating, extracting, and analyzing Data.
  • "Results" means improving decisions, actions, and outcomes to realize new benefits.
  • "Decision" means arriving at the appropriate course of action to solve a problem or answer a query.
  • Big data does not necessarily mean “good data”
  • Good data translates to Good outcomes, good results, good decisions
  • Bad Data translates to Bad outcomes, bad results, bad decisions

Data Owner and Data Steward

  • Data Owner is a person or department responsible for safeguarding or securing Data with security controls, classifying Data and defining Data access rules.
  • Data Steward or Data Custodian is a person, or department delegated the responsibility for managing a specific set of data resources.
  • This person defines, specifies, and standardizes the Data assets of an organization within and across all functional areas.
  • Big Data = Internal + External Data ->>> NEW OPPORTUNITIES
  • Big Data -> Big Decisions
  • Big Data -> New Insights -> New Strategies -> New Decisions -> New Actions
  • Big Data = New Data Asset = New Strategic Asset
  • Big Data -> Assurance Procedures and Consulting Services

Sources of Big Data

  • Structured Data include internal source documents such as sales orders and invoices, purchase requests and orders, operating expenses, production and service records, materials and labor records, etc.
  • Unstructured Data includes external source documents which are commonly found on human languages, audio and video such as public online, search engines, private online and research websites, government agency websites.

Characteristics of Big Data (7 Vs)

  • Volume refers to the amount of big Data being created
  • Variety comes from all forms and formats, including Data generated internally and from external sources
  • Velocity means that Data is being generated extremely quickly and continuously
  • Veracity means that Data must be verified based on accuracy and content
  • Variability means that big Data is extremely variable and always changing
  • Visualization means translating vast amounts of Data into readily presentable graphics and charts to increase end-user satisfaction.
  • Value means organizations, societies and consumers can benefit from the big Data.

Risks in Big Data

  • Discovering wrong and bad Data leads to making wrong and bad decisions .
  • Digging deeper into Data creating an "analysis paralysis" situation leading to being Data analytics rich but information poor.
  • Proceeding with invalid Data patterns and trends asuming that they are valid patterns and trends thus wasting resources
  • Poor Data governance standards leads to lack of high-quality Data and information
  • Lack of Data security

Data Quality Standards

  • Data must be qualified by the following characteristics:
  • Relevance
  • Accuracy
  • Credibility
  • Timeliness
  • Accessibility
  • Interpretability
  • Coherence

Data Analytics

  • Data Analytics involve applying quantitative and qualitative tools and techniques to big Data to gain new insights and new opportunities.

Types of Analytics

  • Predictive Analytics is the process of estimating future outcomes based on the analysis of past data or current data.
  • Descriptive Analytics describes what already happened including content analysis and context analysis.
  • Prescriptive Analytics helps management decide what should happen and thrive on big data through analization of potential outcomes for the best result.
  • Cognitive Analytics uses Artificial Intelligence (AI) as it applies to day-to-day business transactions to provide clear with more precision and speed.

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