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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

The Data-Information-Knowledge-Wisdom (DIKW) Pyramid

Aayush Tyagi
Aayush Tyagi Last Updated : 14 Jun, 2024
5 min read

Introduction

In today’s information age, data is king. But simply having data is not enough. To truly excel in this data-driven world, we need to be able to transform that data into actionable insights. This is where the DIKW, or Data-Information Knowledge Wisdom Pyramid, comes in. The DIKW Pyramid is a robust framework that helps us understand the hierarchy of information, from raw data to the wisdom that guides our decisions. By understanding the different levels of the pyramid and how data progresses through them, we can unlock the full potential of information and make better choices in every aspect of our lives.

Overview:

  • The DIKW Pyramid outlines a hierarchy from raw data to actionable wisdom, which is crucial for turning information into insights.
  • Data is unprocessed facts transformed into meaningful information through organization, analysis, and context application.
  • Information becomes knowledge when combined with experience and understanding, allowing deeper insights.
  • Wisdom involves using knowledge to make sound judgments, incorporating ethical considerations, and understanding consequences.
  • The DIKW Pyramid’s applications span marketing, SEO, and financial planning, demonstrating its practical value in decision-making.
The Data-Information-Knowledge-Wisdom Pyramid | DIKW Pyramid

Table of Contents

What is the Data-Information-Knowledge-Wisdom (DIKW) Pyramid?

The DIKW Pyramid, also known as the Data-Information-Knowledge-Wisdom Pyramid, is a framework used to illustrate the hierarchical relationship between different levels of information processing. It shows how raw data transforms into actionable insights and wise judgments.

Understanding the Levels of the DIKW Pyramid

Let’s understand the different levels of Information processing

1. Data

Data forms the foundation of the pyramid. It is unprocessed facts and figures and can be quantitative or qualitative.

Data itself has limited meaning. Without context, it has no meaning.

Examples: Sensor readings from a machine, customer names and addresses, website clickstream data, weather measurements, scientific observations, etc.

2. Information

Information is processed data that has been given context and meaning. It is the first step in getting value from raw data. Information allows us to answer “what” and “when” questions.

To convert data into information, we need to:

  • Organize: Arrange the data into tables or charts, etc, so that it makes some sense.
  • Analyze: Analyze the data’s patterns, trends, and relationships through statistical analysis or visualization.
  • Apply Context: Understand the data about a specific situation or question.

Examples: A sales report summarizing customer purchases by region, a website traffic analysis report identifying peak browsing times, and user demographics.

3. Knowledge

Knowledge is the application of information combined with experience and understanding. It represents the deeper insight gained by analyzing and integrating data with existing knowledge.

To extract knowledge from information, we must understand those patterns and Ask “why” and “how” questions. 

Examples: Understanding customer buying habits based on sales data and market research, identifying market trends based on historical data and industry reports.

4. Wisdom

Wisdom is at the top of the Data Information Knowledge Wisdom Pyramid. It is about using knowledge to make sound judgments and decisions. Wisdom allows us to ask “what should we do” based on a deep understanding of the situation, its potential consequences, and the ethical considerations involved.

Examples: Strategic decision-making in a business is based not only on market knowledge but also on understanding the impact on employees and society.

Real-life Applications of the Data-Information-Knowledge-Wisdom Pyramid

Example 1: Marketing Campaign

The DIKW Pyramid takes us from website clicks to intelligent marketing decisions.

  1. Data: We start with raw clicks and views from website tracking tools.
  2. Information: We organize this data to see patterns, like high click-through rates on a specific ad format.
  3. Knowledge: We dig deeper, comparing it to past campaigns, to understand customer preferences, such as their preferred ad formats or the times they engage most.
  4. Wisdom: Finally, with this knowledge, we make informed decisions. We target specific audiences with tailored messages, maximizing campaign ROI.

Example 2: SEO

Let’s see how each level plays a role in optimizing SEO:

  1. Data: Website analytics tools like Google Search Console collect data on website traffic, search queries used to find your site, and user behavior on your pages. This includes metrics like clicks, impressions, bounce rates, and time spent on the page.
  2. Information: SEO specialists analyze the data to identify trends and patterns. They might find that specific keywords drive more traffic but have a high bounce rate, while others have lower traffic but higher engagement. This information reveals which keywords people are searching for to find your site and how well your content addresses those searches.
  3. Knowledge: The SEO team combines the information with existing knowledge about SEO best practices and user intent. They understand which keywords represent informational searches and which ones indicate transactional intent.
  4. Wisdom: Based on the knowledge, the team optimizes content for high-traffic keywords with high bounce rates by creating more informative and engaging content. They also target long-tail keywords with lower search volume but higher conversion potential.

Example 3: Financial Planning

The DIKW Pyramid helps us understand how raw financial data transforms into wise financial decisions.

  1. Data: You receive your monthly bank statement showing a high amount spent on dining out.
  2. Information: You categorize the dining expenses and see it accounts for 20% of your monthly income.
  3. Knowledge: You understand you’re spending a significant portion of your time dining and analyze if it aligns with your financial goals. You compare it to past months and see a spending increase.
  4. Wisdom: Based on this knowledge, you decide to cut back on dining out to free up resources for saving or debt repayment. This wise decision helps you achieve your long-term financial goals.

Limitation Of Data-Information-Knowledge-Wisdom (DIKW) Pyramid

The DIKW Pyramid, while a valuable framework, has some limitations to consider:

  • Oversimplification: The pyramid portrays a linear progression, but knowledge creation can be messy and iterative. We might revisit information or data as we gain new knowledge.
  • Creativity: The DIKW Pyramid focuses on logical processing but undervalues the role of intuition and imagination in decision-making.
  • Hierarchy: While data is the foundation, the levels can be interdependent. New data might emerge during the knowledge or wisdom stage.
  • Focus on Explicit Knowledge: The pyramid emphasizes existing knowledge, neglecting the importance of knowledge gained through experience.

Conclusion

In conclusion, the DIKW, or Data-Information-Knowledge-Wisdom pyramid, offers a valuable framework for understanding how data is transformed into actionable insights and, ultimately, wise judgments. By recognizing the right way to use the DIKW pyramid and its limitations, we can leverage its strengths to make better decisions in various aspects of our lives.

Frequently Asked Questions

Q1. What are the 5 levels of the knowledge triangle?

A. The five levels of the knowledge triangle are Data, Information, Knowledge, Understanding, and Wisdom. These levels represent the progression from raw facts to actionable insights, guiding decisions and fostering deep comprehension.

Q2. What is a real life example of data, information, knowledge and wisdom?

A. In financial planning, Data is a bank statement showing dining expenses; information categorizes these expenses; knowledge recognizes dining out’s impact on finances; and wisdom decides to reduce dining costs to save or repay debt.

Q3. What are the benefits of the DIKW pyramid?

A. The DIKW Pyramid enhances decision-making by systematically transforming raw data into actionable wisdom, offering structured insights, and clarifying the value of each information processing stage. It aids in strategic planning and improves data-driven outcomes.

Q4. What is the difference between data, information, knowledge and wisdom with an example?

A. Data are raw numbers, like daily website visits. Information organizes this data to show peak traffic times. Knowledge analyzes these trends to understand user behavior. Wisdom uses this understanding to optimize content release schedules for maximum engagement.

Data-Information-Knowledge-Wisdom PyramidDIKWDIKW pyramid
Aayush Tyagi
Aayush Tyagi

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

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