Business Intelligence Overview
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Questions and Answers

What type of analytics is primarily concerned with understanding past performance?

  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics
  • Descriptive analytics (correct)

    • Which type of analytics aims to answer the question 'What should be done to prevent future issues'?

  • Prescriptive analytics (correct)
  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics

    • Which analytics type focuses on explaining the reasons behind past events?

  • Predictive analytics
  • Descriptive analytics
  • Prescriptive analytics
  • Diagnostic analytics (correct)

    • What is the primary output of descriptive analytics?

    <p>Reports</p> Signup and view all the answers

    What differentiates data science from data analytics in terms of the nature of work?

    <p>Data science explores and investigates, whereas data analytics reports and optimizes.</p> Signup and view all the answers

    What is a characteristic of traditional Business Intelligence (BI)?

    <p>It primarily uses structured data.</p> Signup and view all the answers

    Which of the following best describes the capability of Big Data as compared to traditional BI?

    <p>It analyzes data in both real-time and offline modes.</p> Signup and view all the answers

    Which question aligns with typical inquiries made in Data Science?

    <p>What is the optimal scenario for our business?</p> Signup and view all the answers

    Which of the following represents a type of data used in Big Data?

    <p>Unstructured and semi-structured data.</p> Signup and view all the answers

    What is a common method used in Business Intelligence for reporting?

    <p>Ad hoc reporting and dashboards.</p> Signup and view all the answers

    What is the primary focus of predictive analytics in Data Science?

    <p>To forecast future events based on trends.</p> Signup and view all the answers

    Which statement about data warehouses is true?

    <p>Local systems are emerging as departmental warehouses.</p> Signup and view all the answers

    Which question is typically not asked in the context of Business Intelligence?

    <p>What if these trends continue?</p> Signup and view all the answers

    Which analytic techniques are commonly used in the Consumer Packaged Goods sector?

    <p>Multiple linear regression and random forest</p> Signup and view all the answers

    In wireless telecom, which of the following methods is NOT listed as an analytic technique used?

    <p>Multiple linear regression</p> Signup and view all the answers

    What is an important aspect of the model building process?

    <p>Creating a model that captures underlying patterns</p> Signup and view all the answers

    Which of the following tools is specifically mentioned for executing statistical tests and managing data?

    <p>SAS</p> Signup and view all the answers

    For model training, what is the typical division of data segments used?

    <p>70%-80% training, 20%-30% testing</p> Signup and view all the answers

    Which of these options incorrectly lists a tool used for advanced analytics?

    <p>Microsoft Excel</p> Signup and view all the answers

    What is the role of automatic relevance determination (ARD) in model planning?

    <p>To identify significant predictors in modeling</p> Signup and view all the answers

    What is a primary function of SQL in the context of data analytics?

    <p>Performing in-database analytics and mining functions</p> Signup and view all the answers

    What is a primary characteristic of a data warehouse?

    <p>Subject-oriented</p> Signup and view all the answers

    Which type of analytics involves understanding historical data trends?

    <p>Descriptive analytics</p> Signup and view all the answers

    What does the term 'non-volatile' refer to in data warehousing?

    <p>Data is not removed or changed</p> Signup and view all the answers

    What is the main purpose of prescriptive analytics?

    <p>To provide recommendations for future actions</p> Signup and view all the answers

    What characterizes operationalized analytics?

    <p>It is integrated into business processes.</p> Signup and view all the answers

    Which type of data is primarily sourced for data warehouses?

    <p>Structured and semi-structured data</p> Signup and view all the answers

    What key benefit does an analytic sandbox provide?

    <p>It allows for flexible, high-performance analysis.</p> Signup and view all the answers

    What are the two main types of analytics in the second school of thought?

    <p>Predictive and diagnostic</p> Signup and view all the answers

    What is a significant challenge associated with big data regarding storage?

    <p>The high cost of traditional RDBMS solutions</p> Signup and view all the answers

    Which role is focused on providing technical expertise for analytical projects in the big data ecosystem?

    <p>Support people</p> Signup and view all the answers

    What is a major concern about security in big data platforms?

    <p>Unauthorized access due to weak authentication</p> Signup and view all the answers

    Which aspect is crucial when maintaining data quality in a big data environment?

    <p>Ensuring data accuracy and completeness</p> Signup and view all the answers

    What describes the role of data analytical talent in the big data ecosystem?

    <p>Experts with advanced training in quantitative fields</p> Signup and view all the answers

    Which characteristic is essential for big data systems to manage hardware and software failures?

    <p>Partition tolerance</p> Signup and view all the answers

    What is a major issue regarding data retention in big data management?

    <p>Deciding how long to keep data based on its relevance</p> Signup and view all the answers

    Which emerging technology aids in managing infrastructure for big data?

    <p>Cloud computing</p> Signup and view all the answers

    Study Notes

    Business Intelligence (BI) and Big Data

    • BI focuses on straightforward reports and performance measures, aiding management decisions with recent trends.
    • Traditional BI relies on a central server and offline data analysis, managing structured data.
    • Big data operates on a distributed file system, offering real-time streaming and handling structured, semi-structured, and unstructured data.

    BI vs. Data Science

    • Key distinction lies in analytical techniques: Data Science emphasizes predictive analytics and data mining.
    • BI utilizes standard reporting, dashboards, alerts, and queries primarily on structured data from traditional sources.
    • Common questions explored in BI include historical performance, scenarios, and problem identification.

    Model Planning in Data Science

    • Loading data into a warehouse requires thorough understanding; local systems may emerge as departmental warehouses.
    • Data is analyzed via applications, and the need for either a single model or model ensemble is established.
    • Various analytic techniques are applied across industries, from linear regression in consumer goods to neural networks in telecommunications.

    Common Tools for Model Planning

    • SAS: High-quality interpretive models, statistical analysis, and quality plotting capabilities.
    • SQL: Enables in-database analytics for predictive modeling and data manipulation.
    • RapidMiner and Tableau Public: Advanced analytics with no programming required, supporting real-time data usage and sharing.

    Model Building Importance

    • Extracting insights informs business strategies and decisions.
    • Data is divided into training (60-80%), validation (10-20%), and testing data (10-20%) to develop predictive models.

    Data Warehousing Characteristics

    • Central repositories for structured and semi-structured data, facilitating regular analysis.
    • Key characteristics include being subject-oriented, integrated, non-volatile, and time-variant.

    Types of Analytics

    • Descriptive, Diagnostic, Predictive, and Prescriptive analytics offer various insights from data.
    • Classification of analytics ranges from basic insights to operationalized and monetized analytics.

    Evolution of Analytics

    • Analytics have evolved from descriptive and diagnostic analyses (2005-2012) to predictive and prescriptive analyses (2012-present).
    • The focus has shifted from historical reporting to forecasting and optimizing future actions.

    Data Science vs. Data Analytics

    • Data Science leans towards exploratory and predictive analytics, while Data Analytics focuses on reporting and optimization.
    • Outputs differ: Data Science aims for comprehensive data products, whereas Data Analytics yields reports and dashboards.

    Traditional BI vs. Big Data

    • BI encompasses data mining and performance benchmarking, relying on central servers for offline analysis.
    • Big data enables comprehensive analytics from multiple sources and supports large-scale data operations.

    Drivers and Ecosystem of Big Data

    • Emerging technologies include data devices, collectors, and aggregators, creating a new ecosystem for data usage.
    • Key roles in this ecosystem demand advanced training in quantitative disciplines, with a simultaneous need for data savvy professionals.

    Challenges of Big Data

    • Key challenges include scale, security, schema flexibility, and ensuring continuous availability.
    • Data volume increases exponentially, raising questions about usefulness and retention.
    • Organizations face a talent gap, requiring skilled professionals to manage and exploit big data analytics effectively.

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    Related Documents

    Introduction to Big data analytics and data analytics life cycle.pdf

    Description

    This quiz explores the differences between traditional business intelligence and big data analytics. It highlights key concepts like performance measures, data storage methods, and analysis modes relevant for management decisions. Test your knowledge on the latest trends in the BI landscape.

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