Samenvatting Business Intelligence, Analytics, and Data Science A Managerial Perspective

ISBN-10 1292220546 ISBN-13 9781292220543
397 Flashcards en notities
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Dit is de samenvatting van het boek "Business Intelligence, Analytics, and Data Science A Managerial Perspective". De auteur(s) van het boek is/zijn Ramesh Sharda Dursun Delen Efraim Turban David King. Het ISBN van dit boek is 9781292220543 of 1292220546. Deze samenvatting is geschreven door studenten die effectief studeren met de studietool van Study Smart With Chris.

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Samenvatting - Business Intelligence, Analytics, and Data Science A Managerial Perspective

  • 1 An Overview of Business Intelligence, Analytics, and Data Science

  • Study: You should study the Learning objectives, Content, Technology insights and the Chapter highlights
    Read: You should read the Opening vignette and the Application cases.
    Be aware: You should be aware that the book provides the other items in case you need some details
  • Plan of the book
  • Chapter Highlights
  • 3 types of Analytics
  • What are the Learning Objectives of the chapter "An overview of Business Intelligence, Analytics, and Data Science": (see flashcards below)
  • Why do we need computerized support of managerial decision making?
    Because the business is becoming more complex and is rapidly changing, making decisions more difficult.
    The time frame for making decisions is shrinking and the global nature of decision making is expanding.
  • Describe the BI methodology and concepts
    Bi uses a central repository. The Bi architecture includes a DW, business analytics tools used by end users and a user interface (e.g. dashboard)
  • Understand the different types of analytics? What selected applications are there?
    See chapter 1.5; 1.6
  • Understand the analytics ecosystem to identify various key players and career opportunities
    See chapter 1.8
  • How can we recognize the evolution of computerized support to the current state - analytics/data science?
  • 1.0 Chapter Highlights

  •  Big data is a term for data sets that are so large or comple that traditional data processing applications are inadequate to deal with them.
    Challenges include:
    - data caputuring, storage, data analysis
    - search, sharing, transfer, visualization
    - quering, updating and information privacy

    Big data ecosystm helps making more accurate analysis, better decision-making, greater operational efficiencies, cost reductions and reduced risks for the business.
  • 1.1 OPENING VIGNETTE: Sports Analytics - An Exciting Frontier for learning and Understanding Applications of Analytics

  • Dit is een hoofdstuk "te lezen"
  • Wat zijn PM's? Perfomance measures? p 32
  • What is a churn model?
    A Predictive Churn Model is a tool that defines the steps and stages of customerchurn, or a customer leaving your service or product. Having a predictive churn model gives you awareness and quantifiable metrics to fight against in your retention efforts
  • What are 3 factors that might be part of a PM for season ticket renewal?
  • What are the 2 techniques that football teams can use to do opponent analysis? (analyse van de tegenstander)
    • a cascade decision model to predict the next play 
    • heat maps of each oppponent's passing offensive, illustrating their tendencies to throw left or right and the defensive coverage zone 
    • time series analytics on explosive games 
  • How can wearables improve player health and safety? 
    Wearables give the trainer insights whether a player gives maximum effort during practices. Wearbles can measure:
    • heartbeat
    • body temperature
    • respiration rates
    • they contain also
    accelerometers that measure:
    • running distance
    • speeds
    • accelerations
    • deceleration
    What kind of new analytics can trainers use?

    • Injury models
    • Single leg squat hold test-core body stregth test
  • What other analytics uses can you envision in sports?
  • 1.2 Changing Business Environments and Evolving Needs for Decision Supoort and Analytics

  • Dit hoofdstuk is te studeren
  • What are some of the key system-oriented trends that have fostered IS-supported decision-making to a new level?
    - Group communication and collaboration software and systems
    - Improved data management applications and techniques
    - Data warehouses and Big Data for information collection
    - Analytical support systems
    - Growth in processing and storing information storage capabilities
    - Knowledge management systems
    - Support of all of these systems that is always available
  • What are some of the key system-oriented trends that have fostered IS-supported decisions making to a new level?
    • collect and analyze vast stores of data
    • move from transaction processing and monitoring activities to problem analysis and solution applications
    • cloud based technologies  
    • mobile devices
    • High speed network, networked information systems, wireless and non-wireless
    • analytics and BI-tools 
    in summary:  the growth of hardware, software and network capabilities
  • List some capabilities of information systems that can facilitate managerial decision making.
    - Ability to perform functions that allow for better communication and information capture
    - Better storage and recall of data
    - Vastly improved analytical models that can be more voluminous or precise
  • List some capabilities of information systems that can facilitate managerial decision making.
    Group communication and collaboration:
    • improve collaboration process; 
    • e.g. supply chain:know about changing patterns of demand near real time and thus react faster
    Improved data management: systems today can search, store and transmit needed data quickly, economically, securely and transparently.
    Managing Giant data warehouses and Big Data: cost of data storage and mining increased rapidly. Big data: massive data coming from a variety of sources and forms allow different view into the organizational performance.
    Analytical support: With such tools, decision makers can perform complex simulations.
    Overcoming cognitive limits in processing and storing information: Computerized systems enable people to overcome their limits by quickly accessing and processing vast amounts of stored information.
    Knowledge management: Knowledge management systems (KMS) are sources of formal and informal support for decision making to managers. e.g. text analytics, IBM Watson help to generate value from KMS.
    Anywhere, Anytime support: with help of wireless technology, mobile technologies, social media platforms and analytical tools. Data-driven support for any decision, mangers AND consumers!
  • How can a computer help overcome the cognitive limits of humans?
    Computer-based systems are not limited in many of the ways people are, and this lack of limits allows unique abilities to evaluate data. Examples include being able to store large amounts of data, being able to run extensive numbers of scenarios and analyses, and the ability to spot trends in vast datasets or models
  • How can a computer help overcome the cognitive limits a humans?
    Computerized systems enable people to overcome their limits by quickly accessing and processing vast amounts of stored information.
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Laatst toegevoegde flashcards

Describe new organizational units that are created because of analytics
Analytics department
BI department
data science department
new units within IT provides companies
They can spend more time on planning instead on putting out fires
Study "The Economist (Standage, 2016)" report mentioned in this section.What other impacts of automation did you find interesting?
Which jobs are most likely to change as a result of automation?
The job of middle manager: they mostly make routine decisions which can be fully automated.
How is cognitive computing affecting industry structure?
The technology enables many cognitive tasks to be done by computers. The speed of change of speed is so radical that the likely impact on organizations and society is unpredictable.
Identify other examples of analytics applications to redesign work space or team behavior
List the impact of analytics on other managerial tasks
It should free up managers and knowledge workers to do more challenging tasks
They gain more knowledge, experience and expertise and enhance the quality of their decision making
List the impact of analytics on decision making
Micro and macro impacts, short term and long term impact
  • particular individuals
  • jobs
  • work structures
  • department and units within the organization
  • organizational structures      
  • entire industries
  • communities
  • society as a whole   
Explore more transportation applications that may employ location-based analytics.
consumer-oriented applications: coupons for targeted users, based on location
augmented reality
How can geocoded locations assist in better decision making?
In understanding the true location-based impact.
What other applications can you imagine if you were able to access cell phone location data?
Waze: navigation app
ParkPGH: estimates parking avilability