Notes on Binary Encoding, ASCII, and the DIKW Pyramid in Information Systems

Binary foundations: computers think in twos

• Binary means two; computers operate with two states: 0 or 1, on or off.
• This can be thought of as electricity flowing (on) or not flowing (off).
• Inside a computer is a complex network of wires and switches called transistors.
• Transistors function like light switches: they switch on or off.
• A pattern of switches being on/off represents information when electricity flows through them.
• By turning a set of switches on/off, we create patterns that encode numbers or other data.
• Example: a particular pattern corresponds to the value 85, calculated as $64 + 16 + 4 + 1 = 85.$ (patterns may be described as 64, 16, 4, 1 bits active.)
• Computers store data as sequences of ones and zeros; everything stored on a laptop, watch, or the Internet ultimately comes down to these bits.
• Data can be represented differently depending on the medium, but the underlying principle is the same: electricity flowing or not.
• Storage mediums vary:
  • Hard drives: data stored via electrical states across magnetic storage with a motor and read/write heads.
  • CDs: data encoded as pits that reflect light differently; the laser reads reflected light to recover bits.
  • USB drives: data stored in silicon chips as ones and zeros; read by electronic circuits when plugged in.
• The first step in computing is representing information as binary using physical states.

ASCII and character encoding: turning letters into numbers and back

• ASCII (American Standard Code for Information Interchange) maps every key on a keyboard to a numeric code.
• It covers uppercase A–Z, lowercase a–z, digits, and symbols, converting each to a number.
• Those numbers are then converted into a stream of ones and zeros for storage/communication.
• Example mapping and binary representation:
  • The letter A has ASCII code $65$ in decimal.
  • In binary (8-bit), this is $01000001$.
  • The letter B is $66$ decimal, which is $01000010$ in binary.
• In practice: you can convert any text to numbers, then to binary, store it, and read it back as text.
• Some transcriptions in lectures describe the process as turning text into numbers and then into binary for storage on various media.
• The general idea: character data is encoded as bytes (commonly 8 bits per character in modern systems).
• Equations and concepts to remember:
  • Value of a binary number with bits $b<em>{n-1}…b</em>1b<em>0$ is $V = \sum</em>{i=0}^{n-1} b_i \cdot 2^i.$
  • Example: the 8-bit representation of a character is a byte, e.g., for A: $V = 65 = 01000001_2.$
• ASCII enables words, sentences, books to be represented as a sequence of ones and zeros, which computers can store and manipulate.

Data, Information, Knowledge, Wisdom (the DIKW pyramid)

• Core idea: data are raw facts; information, knowledge, and wisdom add context and usefulness.
• Data (bottom): a collection of facts or observations with no inherent meaning.
  • Example: Data can be red (or white, etc.) without any context.
• Information (second level): data with context that gives it meaning.
  • Adding metadata (data about data) helps interpret the raw signals.
  • Example: Red might mean the stoplight is red when we know it’s a traffic signal.
• Knowledge (third level): the connections and relationships among data that provide understanding.
  • Visual metaphor: simple blue and green dots represent separate data points; knowledge connects related data pieces.
  • Knowledge answers questions like how and why — it starts to explain patterns and relationships.
• Wisdom (top): the application of knowledge to make informed decisions and take action in the real world.
  • Example: If a red stoplight is ahead, wisdom suggests stopping at a safe point; if school starts and sales rise, decide on restocking to meet demand.
  • Wisdom is the prescription for future action and strategic planning.
• Metadata and context matter: data alone are not valuable; information, knowledge, and wisdom emerge when context and meaning are added.
• Visual metaphor described in the talk: a process where data are independent points, information groups them, knowledge links them, and wisdom prescribes action.
• A common warning: conspiracy theory in data — finding patterns to fit a preconceived conclusion, which may not be supported by the data.

Data-to-information-to-knowledge-to-wisdom: step-by-step interpretation

• Data: raw facts, counts, observations with no interpretation.
  • Example data: a log of cupcake sales with dates and quantities.
• Information: data plus context (time, place, conditions) to give meaning.
  • Example: which cupcakes sold most, profitability today, or seasonal trends.
• Knowledge: connections and patterns that explain why things happened.
  • Example: recognizing that August (start of school year) increased traffic and sales, or that a holiday period boosted demand.
• Wisdom: using insights to decide and act (future-oriented).
  • Example: stock more cupcakes during the first week of school or adjust marketing and staffing based on trends.
• Major takeaway: data alone do not tell you what to do; the value lies in turning data into information, then knowledge, then wisdom to inform decisions.

Information systems: collecting data, generating insight, and supporting decisions

• Information systems exist to collect data and produce information and insight.
• Canvas example: an information system that stores data about courses, assignments, grades, and participation.
  • It provides access to data and can aggregate information, but by itself cannot prescribe what you should learn or how to prepare for the future.
• There are higher-level tools that aim to reach wisdom, such as decision support systems that integrate data from a data warehouse (inventory, sales, trends) and suggest courses of action.
• The broader message: IT infrastructure supports the data-information-knowledge-wisdom pipeline, enabling better decision-making and competitive advantage.

Real-world illustrations and practical implications

• Everyday data exist as points in space; without context, they’re just scattered data.
• Information adds meaning (e.g., red meaning a stop signal when tied to driving context).
• Knowledge organizes and connects information (e.g., linking that a red light and morning traffic patterns relate to rush-hour behavior).
• Wisdom turns knowledge into action (e.g., stop at the right place, restock inventory for the back-to-school period).
• Practical implication for businesses: collecting data (big data) is not enough; the value comes from processing it into information, knowledge, and ultimately wisdom to guide decisions and strategy.
• Ethical and practical considerations: the DIKW framework emphasizes responsible use of data and awareness that data can be misinterpreted (conspiracy-like patterns) if context and connections are not correctly established.

Examples used in the lecture and how they map to DIKW

• Stoplight example:
  • Data: the color red observed at a traffic light.
  • Information: the signal indicates to stop; context includes the vehicle’s movement toward the light.
  • Knowledge: recognizing the situation and that stopping is necessary in that moment.
  • Wisdom: braking at an appropriate distance and time, maintaining safe positioning.
• Cupcake sales example:
  • Data: transaction timestamps and counts.
  • Information: understanding which days or conditions boosted sales (e.g., start of school year).
  • Knowledge: linking school calendars, weather, and promotions to explain sales patterns.
  • Wisdom: adjusting inventory and staffing for peak periods (e.g., first week of school with a large student influx).
• Kent State University scenario: 25,000 more people in the city implies increased demand; plan restocking and staffing accordingly.

Tools, quizzes, and workflows mentioned

• Canvas as an information system example for class data, assignments, grades, and participation.
• A in-class quiz within Canvas titled something like "data information knowledge wisdom quiz" asks for examples of each DIKW level.
• The instructor describes grading for participation rather than strict accuracy; encourages submitting responses if unsure.
• Accessibility note: occasional trouble accessing the assignment was mentioned, with an intent to resolve it.

Key takeaways and implications

• Binary and hardware basics: computers compute using binary states (0/1), realized via transistors, and encoded into bits that can represent numbers, text, images, and more.
• ASCII provides a universal mapping from characters to numbers and to binary, enabling text storage and processing across devices.
• The DIKW pyramid explains how raw data become actionable knowledge and wisdom through context and processing.
• Data alone have limited value; the power comes from turning data into information, knowledge, and wisdom to inform decisions and actions.
• Information systems (like Canvas and decision support systems) support this pipeline, but the ultimate value lies in the decisions and actions enabled by the processed information.
• Be cautious of pattern-finding biases or conspiracy-style interpretations that fit data to preconceived conclusions without sufficient evidence.
• Real-world relevance: understanding DIKW helps in business analytics, IT system design, data governance, and responsible decision-making.

Quick reference: core formulas and numbers

• Binary value of a bit pattern: $V = \sum<em>{i=0}^{n-1} b</em>i \cdot 2^i, \quad b_i \in {0,1}.$
• ASCII example (8-bit):
  • A: $65\;\to\;01000001_2.$
  • B: $66\;\to\;01000010_2.$
• A binary digit (bit) is the fundamental unit of information; a string of bits forms a byte (commonly 8 bits).