Section 2 lesson 4

Page 1: Learning Objectives and the Data Pyramid

• Learning Objectives:

  • Differentiate between information and data.

  • Describe characteristics of valuable data.

• Data Pyramid:

  • Concept visualizing the hierarchy between data, information, knowledge, and wisdom (DIKW pyramid).

  • Layers of the Pyramid:

    • Data (Bottom Layer):

      • Defined as raw facts or observations.

      • Example: "Raw: 18:38, On, Off."

    • Information (Second Layer):

      • Processed data with meaning.

      • Example: "It is 6:38 PM, the light switch is turned off."

    • Knowledge (Third Layer):

      • Contextual application of information.

      • Example: "The room I am working on is getting dark."

    • Wisdom (Top Layer):

      • Applied knowledge indicating actions.

      • Example: "It is dark; I'd better turn the light on."

  • Information is essential for decision-making, derived from aggregating and processing data.

Page 2: Characteristics of Information and Knowledge

• After organizing data, information is created.

• Meaning is the key term for information, which can be structured or unstructured.

• Knowledge:

  • Dynamic combination of experience, values, and contextual information.

  • Embedded in organizational routines and norms.

  • Key term is context.

• Wisdom:

  • Knowing the right action to take based on knowledge.

Page 3: Connectedness and Understanding in the DIKW Pyramid

• Connectedness Wisdom Figure:

  • Graph shows progression from data to wisdom via understanding and connectedness.

  • Elements:

    • Data (0,0)

    • Information: Understanding relations.

    • Knowledge: Understanding patterns.

    • Wisdom: Understanding principles.

  • Connectedness wisdom involves leveraging technology to make informed decisions.

Page 4: Examples of Data and Context

• Data:

  • Observations such as time from a clock (e.g., "18:30").

  • Status indicators (e.g., "On" or "Off").

• Contextual Information:

  • Transforms data into meaningful information.

  • Knowledge example: "The room is getting dark; turn on the light."

Page 5: Flipped Pyramid in Organizations

• Organizations gather data on customer purchasing habits (e.g., who bought what).

• Data Storage:

  • Captured data stored in data centers or the cloud.

  • Attributes of sales data include customer name, payment method, etc.

Page 6: Tools for Decision-Making and Big Data

• At the information level, organizations can analyze customer spending to inform inventory management.

• Big data helps predict future needs by analyzing past data.

• Examples:

  • Links between customer features (e.g. wealth) and behaviors (e.g. golfing).

• Data vs Information:

  • Data includes specific metrics (e.g., "18:23, 45 mph, 15% humidity").

  • Information provides context (e.g., "It's hot outside").

Page 7: Structured vs Unstructured Data

• Decision-Making Tools:

  • More data does not inherently mean better decisions; management and analysis are crucial.

• Data Types:

  • Structured Data:

    • Easily analyzable, coded (e.g., names, contact info).

  • Unstructured Data:

    • More complex, not easily analyzed (e.g., text, videos).

  • Importance of integrating both structured and unstructured data for effective analysis.

Page 8: Big Data and Information Systems

• Big Data Market Growth:

  • Estimated to grow 45% annually.

• Use of Data Science:

  • Companies like Amazon and Netflix utilize data science for customer experience enhancement.

• Information Systems:

  • Collections of data and information to inform decision-making, ranging from simple to complex systems.

Page 9: Types of Cloud-based Systems

• Cloud Models:

  • IaaS (Infrastructure as a Service): Provides virtualized resources.

  • PaaS (Platform as a Service): Supports application development.

  • SaaS (Software as a Service): Software accessible via subscriptions.

• Data Extraction:

  • Requires integration of data from various organizational activities to analyze business processes.

Page 10: Data Quality and Business Value

• Importance of Data Quality:

  • Good data improves marketing effectiveness and decision-making.

  • Bad data can result in financial losses.

• Benefits of Quality Data:

  • Analyzing financial states, improving efficiency, developing new processes, gathering competitive information, making evidence-based decisions.

Page 11: Data Hygiene and Bad Data Types

• Data Hygiene:

  • Processes to ensure data cleanliness; addressing duplicates, incompleteness, and errors.

• Types of Bad Data:

  • Duplicate, conflicting, incomplete, invalid, and unsynchronized data.

Page 12: Attributes of Quality Data

• Quality Attributes:

  • Precise, valid, reliable, timely, and complete data are essential for effective decision-making.

  • Precision varies by industry; data relevance is time-sensitive.

  • Incomplete data can lead to flawed understanding and poor decisions.