Knowledge Management – Data, Information, Knowledge & Analytics

DATA – Fundamental Concepts

• Definition

  • Raw facts, figures, symbols, or characters collected without context or interpretation.

  • Can be quantitative (numerical) or qualitative (descriptive/categorical).

  • Considered meaningless until processed; therefore often called "unprocessed facts" or "inputs."

• Key characteristics

  • Objective, context-free, and unorganized.

  • Susceptible to overload when massive quantities are gathered without structure.

• Examples from transcript

  • Strings such as 12012012 (a date hidden in an unrecognized format).

  • Numbers 100 or 5\% presented without explanation.

  • Environmental readings: gage height, precipitation volume.

• Transformation cue

  • Data requires contextualization and processing to evolve into information.

INFORMATION – Processed & Contextualized Data

• Definition

  • Data that has been organized, interpreted, structured, or presented to provide meaning to a user.

  • Described as “processed facts” or “contextualized data.”

• Properties

  • Purpose-specific, has meaning, and supports decision-making.

  • Still objective yet now framed within a relevant context.

  • Subject to information overload when excessive but organized facts are delivered without filtration.

• Illustrative examples

  • Trend analysis of daily temperatures revealing seasonal patterns.

  • Website visitor counts month-over-month showing growth or decline.

  • Warehouse inventory plotted across time to flag supply-chain bottlenecks.

  • $5\%$ annual interest applied to a $\$100$ principal returning $\$105$ after one year.

KNOWLEDGE – Actionable Understanding

• Definition

  • Accumulated insights, experience, and skills that enable interpretation of information and facilitate decisions.

  • Unique to each individual; shaped by past encounters and stored expertise.

• Key traits

  • Subjective and value-laden; draws on authority and capacity to act.

  • Non-quantifiable; no concept of “knowledge overload.”

  • Connects multiple pieces of information, answering “how” questions.

• Flow feedback

  • Knowledge may expose gaps or redundancies in collected data, prompting new data-collection or processing methods.

• Practical examples (water case study)

  • Converting stream-gage heights to streamflow ➜ knowledge triggers withdrawal restrictions if below $7Q10$.

  • Identifying long-term precipitation growth ➜ prioritize flood-plain mapping investments.

  • Mapping lead contamination to customer data ➜ issue alerts for health compliance.

DIKW PYRAMID – Hierarchical Progression

• Levels (bottom ➜ top)

  • Data: Raw signals.

  • Information: Answers who, what, when, where.

  • Knowledge: Answers how (application and relations).

  • Wisdom: Answers why / what is best (guiding action).

• Insights

  • Each ascent adds value by enriching meaning.

  • Data & information focus on past descriptions; knowledge & wisdom guide present actions and future goals.

DATA TYPES

1. Qualitative (Categorical)

• Generally non-numeric; describe attributes.

• Two sub-types:

  • Nominal: Pure labels, no inherent order (e.g., hair color, nationality).

  • Ordinal: Ordered categories, differences unknown (e.g., satisfaction levels, socio-economic status, “123 = Beginner < Intermediate < Advanced”).

2. Quantitative (Numerical)

• Represent counts or measurements.

• Two sub-types:

  • Discrete: Countable, indivisible values (e.g., number of children, coin-flip heads).

  • Continuous: Measurable, infinitely divisible (e.g., weight, price, height, annual income).

DATA ANALYSIS & ANALYTICS

• Definition of Data Analysis

  • Working with data to extract useful information enabling informed decisions.

  • Involves cleaning, transformation, modeling, and communication.

Four Analytics Categories (Complexity ↑)

• Visual ratios (from slide): $70\%$–$80\%$ of analytics effort often remains descriptive/diagnostic.

Descriptive Statistics Toolkit

• Measures of frequency (counts, percentages).

• Central tendency: Mean, Median, Mode.

• Dispersion: Range, Standard Deviation, Variance, Quantile ranks.

Diagnostic Methods (Deep-Dive)

1. Root-cause analysis.

2. Correlation & regression.

3. Fishbone (Ishikawa) diagrams.

4. Pareto (80/20) analysis.

Predictive Analytics

• Uses historical & current data to model future outcomes.

• Example: Cash-flow forecasts for the next month.

Prescriptive Analytics

• Combines diagnostic + predictive insights to suggest optimal actions.

• Evaluates multiple simulated outcomes to pick best path.

FIVE-STEP DATA ANALYSIS PROCESS

1. Define the question(s)

   • Ex: “What is the yearly sales trend?” “Are we gaining corporate customers?”

2. Collect data

   • First-party, Second-party, Third-party sources.

3. Clean the data

   • Remove errors/duplicates/outliers, fix structure, fill gaps.

4. Analyze the data

   • Employ descriptive → diagnostic → predictive → prescriptive techniques.

5. Share/Visualize results

   • Dashboards, reports, interactive visuals; ensure clarity for stakeholders.

KNOWLEDGE MANAGEMENT (KM) & DATA-DRIVEN CULTURE

• Goals of KM platforms

  • Centralize data ➜ convert to accessible, actionable information.

  • Increase data accuracy & reliability.

  • Embed informed decision-making across all organizational levels.

• Benefits

  • Streamlined processes, heightened agility, engaged employees.

  • Fosters innovation & improves customer engagement.

• Distinction

  • Technology supplies tools; knowledge management supplies strategy—organizing, interpreting, applying data.

CULTIVATING A DATA-DRIVEN ORGANIZATION

• Implement KM systems that:

  • Collect & transform data into coherent, timely information.

  • Provide universal access to insights.

  • Support continuous feedback loops (data ⇆ information ⇆ knowledge).

• Outcomes

  • Enhanced operational efficiency.

  • Evidence-based policy & strategy development.

  • Competitive advantage through superior knowledge and wisdom.

CONCEPTUAL PROGRESSION SUMMARY (DATA ➜ WISDOM)

• Data: Symbols without context.

• Information: Contextualized answers to basic questions.

• Knowledge: Integrated how-to understanding and capacity to act.

• Wisdom: Judicious application answering why and determining best actions.