WGU Introduction to IT - D322 - Section 2.4

Data Pyramid (DIKW Model)

- Data = raw facts/observations (ex: "18:30")

- Information = processed/organized data → meaning (ex: "18:30 = 6:30 pm")

- Knowledge = info + context, experience, values (ex: "Room is dark at 6:30")

- Wisdom = right action (ex: "Turn on light")

Data (DIKW)

raw facts/observations (ex: "18:30") (raw)

Information (DIKW)

processed/organized data → meaning (ex: "18:30 = 6:30 pm") (meaning)

inferred

who, what, when, where, how

provides context

Knowledge (DIKW)

info + context, experience, values (ex: "Room is dark at 6:30") (context)

Wisdom

right action (ex: "Turn on light") (action)

Human + tech = connected understanding.

Path: Data → Info → Knowledge → Wisdom.

Big Data

massive, fast-growing, hard-to-process data

Traditional pyramid

flipped because raw data is huge compared to knowledge/wisdom.

Data Science

finds patterns/clusters, unexpected insights (ex: golf → affluence).

Structured data

easy to analyze (names, DOB, $ amounts)

Unstructured

messy formats (texts, blogs, video, tweets)

big data tools

now analyze unstructured too

Information Systems (IS)

Collection of data + info to support decisions.

Can be paper-based, but usually computer-based.

Data Management

the process of collecting, storing, securing, and using an organization's data to support decision-making, ensure accuracy, maintain compliance, and enhance business value

Poor management

liability (PII risks)

Data Architecture

infrastructure to collect, store, analyze (in-house or cloud).

Cloud Models

IaaS

PaaS

SaaS

IaaS (infrastructure as a service)

provides access in a virtualized environment and the computing resources are composed of virtualized hardware.

includes: (network connections, virtual servers space)

OS licensed and back-end networking managed by client

PaaS (platform as a service)

cloud service provider is responsible for licensing the OS and back-end storage and networking

dev platform

SaaS (software as a service)

software is licensed to customers with subscriptions and central hosting (Gmail, Office 365).

Good data helps businesses

Analyze financials

Increase revenue (targeting/satisfaction)

Improve efficiency

Automate processes

Beat competitors

Make evidence-based decisions

Understand business value

Data hygiene

keep data clean + accurate.

Data scrubbing

fix/remove duplicates, errors, incomplete/outdated entries.

Types of Bad Data:

Duplicate (same record twice)

Conflicting (same record, different attributes)

Incomplete (missing info)

Invalid (out of standard range)

Unsynchronized (not updated across systems

Quality Data Attributes

Precise (healthcare needs high precision)

Valid (ex: age can't be negative)

Reliable (consistent across systems)

Timely (collected at right time)

Complete (full picture, not partial)

Connectedness wisdom

essentially “the path to connected understanding” when interpreting data

human element leveraging an intelligent technology component