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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

What is Data Lineage?: Insights, Tools, and Benefits

Parth Shukla
Parth Shukla Last Updated : 12 Apr, 2024
10 min read

Currently, most businesses and big-scale companies are generating and storing a large amount of data in their data storage. Many companies are there which are entirely data-driven. Businesses and companies are using data to get insights about the progress and future steps for business growth. In this article, we will study the data lineage and its process, the significant reasons behind businesses investing in it, and the benefits of it, with its core intuition. This article will help one understand the whole data lineage process and its applications related to business problems.

Table of contents

What is Data Lineage?

Data lineage is a process of getting an idea about where the data is coming from, analyzing it, and consuming it. It reveals where the data has come from and how it has evolved through its lifecycle. It traces where the data was generated and the steps in between it went through. A clear flowchart for each step helps the user understand the entire process of the data lifecycle, which can enhance the quality of the data and risk-free data management.

Data Lineage
Source: imperva.com

The main objective of it is to track the data from where it has been generated and the path it follows throughout its lifecycle. Some significant data-driven companies like Netflix, Google, Coca-cola, Microsoft, and Uber use Data provenance for many purposes.

  • Data lineage enables companies to track and solve problems in the path of the data lifecycle.
  • It provides a thorough understanding of the solutions to errors in the way of the data lifecycle with lower risk and easy solution methods.
  • It allows companies to combine and preprocess the data from the source to the data mapping framework.
  • Data lineage helps companies to perform system migration confidently with lower risk.

Top Data Lineage Tools

Data lineage tools help organizations manage and govern their data effectively by providing end-to-end Data provenance across various data sources, enabling data discovery, mapping, and Data provenance visualization, and providing impact analysis and data governance features.

Here are some of the top data lineage tools and their features:

Alation

Alation provides a unified view of data lineage across various data sources. It automatically tracks data changes, lineage, and impact analysis. It also enables collaboration among data users.

Collibra

Collibra provides end-to-end Data provenance across various data sources. It enables data discovery, data mapping, and data lineage visualization. It also provides a business glossary and data dictionary management.

Informatica

Informatica provides Data provenance across various data sources, including cloud and on-premise. It enables data profiling, data mapping, and Data provenance visualization. It also includes impact analysis and metadata management.

Apache Atlas

Apache Atlas provides data lineage for Hadoop ecosystem components. It tracks metadata changes, lineage, and impact analysis for data stored in Hadoop. It also enables data classification and data access policies.

MANTA

MANTA provides lineage for various sources, including cloud and on-premise. It enables data discovery, data mapping, and data lineage visualization. It also provides impact analysis and data governance features.

Octopai

Octopai provides automated data lineage for various sources, including cloud and on-premise. It enables discovery, mapping, and lineage visualization. It also includes impact analysis and data governance features.

Data Lineage Application Across Industries

Data lineage is a critical process across various industries. Here are some examples:

  • Healthcare: In the healthcare industry, Data provenance is important for ensuring patient data privacy, tracking data lineage for medical trials, and tracking data for regulatory compliance.
  • Finance: Data provenance helps financial institutions comply with Basel III, Solvency II, and CCAR regulations. It also helps prevent financial fraud, risk management, and transparency in financial reporting.
  • Retail: In the retail industry, data lineage helps in tracking inventory levels, monitoring supply chain performance, and improving customer experience. It also helps in fraud detection and prevention.
  • Manufacturing: In manufacturing, Data provenance tracks the production process and ensures the quality of the finished product. It helps identify improvement areas, reduce waste, and improve efficiency.
  • Government: Data lineage is critical for ensuring transparency and accountability. It supports regulatory compliance, public data management, and security.

Why Are Businesses Eager to Invest in Data Lineage?

Just the information about the source of the data is not enough to understand the importance of the data. Some preprocessing on data, error solution in between the path of data, and getting key insights from the data is also important for a business or company to focus on.

Knowledge about the source, updating of the data, and consumption of the data improves the quality of the data and helps businesses get an idea about further investing in it.

There are some advantages of Data provenance because of which businesses are investing in it.

  • Profit Generation: For every organization, generating revenue is the primary need to grow the business. The information tracked from Data provenance helps improve risk management, data storage, migration process, and hunting of some bugs in between the path of the data lifecycle, etc. Also, the insights from the data lineage process help the organizations understand the scope of profit and can generate revenue.
  • Reliance on the data: Good quality data always helps to keep the business running and improving. All the fields or departments, including IT, Human resources, and marketing, can be enhanced through data lineage, and companies can rely on data to improve and keep tracking things.
  • Better Data Migration: There are some cases where there is a need to transfer the data from one storage to another. The data migration process carries out very carefully as there is a high amount of risk involved in it. When the IT department needs to migrate the data, Data provenance can provide all the information about the data for the soft data migration process.

How to Implement Data Lineage?

Implementing Data provenance in an organization involves several steps. Here is a detailed guide on how to implement data lineage:

  1.  Identify Data Sources

    Start by identifying the data sources that are used in your organization. These could be databases, applications, or files.

  2. Define Data Elements

    Identify the data elements that are important to your organization. This could include customer information, financial data, or product data.

  3. Map Data Flows

    Once you have identified your data sources and elements, you can start mapping data flows. It involves identifying data movement across your organization, including how data is transformed and stored.

  4. Use Data Lineage Tools

    Several data lineage tools available in the market can help you automate the process of mapping data flows. These tools can also help you track changes in data over time.

  5. Establish Data Governance Policies

    Establish data governance policies to ensure data is accurately captured and maintained throughout its lifecycle. This includes defining data quality standards, data retention policies, and data access policies.

  6. Monitor Data Lineage

    Regularly monitor your data lineage to ensure that it remains accurate and up-to-date. This will help you identify any issues or inconsistencies in your data.

  7. Train your Staff

    Provide training to your staff on using data lineage tools and interpreting the data lineage information. 

Data Lineage to build transparency
Source: Y42

Benefits of Data Lineage

There are some obvious benefits of the Data provenance, which is why businesses are eager to invest in the same.

Some major benefits are listed below:

Data Lineage

 

1. Better Data Governance

Data governance is the process in which data is governed, and analysis of the source of the data, the risk attached to it, data storage, data pipelines, and data migration is performed. Better Data provenance can help conduct better data governance. Good quality of it can provide all this information about the data from its source to consumption and help achieve a better data governance process.

2. Better Compliance and Risk Management

Major data-driven companies have a huge amount of data, which is tedious to handle and keep organized. There are some cases where there is a need for data transformation or preprocessing data; during these types of processes, there is a huge risk involved lose the data. Better Data provenance can help the organization keep the data organized and reduce the risk involved in the process of migration or preprocessing.

3. Quick and Easy Root Cause Analysis

During the entire data lifecycle, many steps are in between, and many bugs and errors are involved. With a good-quality data lineage, it can help businesses to find the cause of the error easily and solve it efficiently with less amount of time.

4. Easy Visibility of the Data

In a data-driven organization, due to a very high amount of data stored, it is necessary to have easy visibility of the data to access it quickly while spending less time searching for it. Good-quality Data provenance can help the organization access the data quickly with easy data visibility.

5. Risk-free Data Migration

There are some cases where data-driven companies or organizations need the migrations of the data due to some errors occurring in existing storage. Data migration is a very risky and hectic process with a higher rate of data loss risk involved. It can help these organizations conduct a risk-free data migration process to transfer the data from one to another data storage.

Data Lineage Challenges

Lack of Standardized Data Lineage Metadata 

It becomes difficult to track Data provenance consistently across different systems and applications. Solution: Standardizing metadata and using common data models and schemas can help overcome this challenge.

Complex Data Architectures

With the growth of big data, there is an increase in the complexity of data architectures, making it harder to trace data lineage across multiple systems and platforms. Solution: Using advanced Data provenance tools and technologies designed to work with complex data architectures can help overcome this challenge.

Data Lineage Gaps

There can be gaps in Data provenance due to incomplete or inconsistent data, missing metadata, or gaps in the data collection process. Solution: Establishing a comprehensive data governance framework that includes regular data monitoring and auditing can help identify and fill data lineage gaps.

Data Lineage Security and Privacy Concerns

Data lineage information can be sensitive and require protection to avoid security and privacy breaches. Solution: Implementing appropriate security measures, such as data encryption and access controls, and complying with data privacy regulations can help to ensure Data provenance security and privacy.

Lack of Awareness and Training

Lack of awareness and training among data stakeholders on the importance and use of data lineage can lead to limited adoption and usage. Solution: Providing training and awareness programs to educate data stakeholders on the importance and benefits of Data provenance can help to overcome this challenge.

Data Lineage vs Other Data Governance Practices

Data lineage is a critical component of data governance and is closely related to other data governance practices, such as data cataloging and metadata management. However, data cataloging is the process of creating a centralized inventory of all the data assets in an organization. At the same time, metadata management involves creating and managing metadata associated with these assets.

Data provenance helps establish the relationships between data elements, sources, and flows and provides a clear understanding of how data moves throughout an organization. It complements data cataloging and metadata management by providing a deeper insight into data’s origin, quality, and usage.

While data cataloging and metadata management provide a high-level view of an organization’s data assets, data lineage provides a granular understanding of how data is processed, transformed, and used. Data provenance helps to identify potential data quality issues, track changes to data over time, and ensure compliance with regulatory requirements.

Data Mapping vs Data Lineage

Data MappingData Lineage
Focuses on identifying the relationships between data elements and their corresponding data sources, destinations, and transformations.Focuses on tracking the complete journey of data from its origin to its final destination, including all the data sources, transformations, and destinations in between.
Primarily used to understand data flow between systems and applications.Primarily used to understand the history and lifecycle of data within an organization.
Typically involves manual or semi-manual documentation of data flows.Can be automated or semi-automated using tools and platforms that capture and track metadata.
Often used for specific projects or initiatives, such as data integration or data migration.Used for ongoing data governance and compliance efforts, as well as for specific projects.
Helps ensure consistency and accuracy in data movement across systems.Helps ensure data quality and compliance with regulatory requirements by providing a clear understanding of data lineage.

Regulatory Compliance

  • Compliance with regulations like GDPR and CCPA requires companies to comprehensively understand their data.
  • Data lineage provides a detailed data usage history, making it easier to comply with regulations like GDPR and CCPA.
  • With Data provenance, organizations can easily identify where data is being stored, who has access to it, and how it is being used.
  • By maintaining a clear Data provenance, organizations can demonstrate compliance to regulatory bodies and provide evidence of their data privacy and security practices.
  • Data lineage can also help with compliance by enabling organizations to easily audit their data usage and identify areas that may be non-compliant.
  • Data lineage can be particularly useful in the case of data breaches, as it allows organizations to quickly identify what data was affected and take appropriate action to notify affected individuals and regulatory bodies.

Future of Data Lineage

  • Adoption by More Industries: As more industries recognize the importance of data governance, Data provenance will become more widely adopted as a critical tool for ensuring regulatory compliance and data quality.
  • Increased Automation: Automation will play a more significant role in Data provenance, reducing the amount of manual effort required to maintain Data provenance and providing more timely and accurate data lineage information.
  • Integration with Machine Learning and AI: Data lineage will be integrated with machine learning and artificial intelligence to enhance its capabilities for data discovery, quality management, and governance.
  • Improved Interoperability: Improved interoperability between data lineage tools and other data management systems will allow for more comprehensive data governance across organizations.
  • Greater Emphasis on Security: With increased concerns about data breaches and cyber threats, data lineage will be essential in ensuring data security by tracking data access and providing visibility into how data is used.
  • Emergence of Blockchain-based Data provenance: Blockchain technology is being explored to provide more secure and transparent Data provenance by creating an immutable record of data transactions.

Conclusion

To sum up, Data provenance is an essential procedure that helps companies and organizations comprehend the evolution and movement of their data. It offers enhanced data visibility, risk-free data migration, faster root cause investigation, and improved data governance, compliance, and risk management. The increasing complexity of data will lead to a wider adoption of automation and data lineage technologies, along with more integration with machine learning and artificial intelligence and a stronger focus on security and transparency. Businesses can reap major benefits from implementing a strong data lineage strategy, which can help them make better decisions and spur growth.

Frequently Asked Questions

Q1. What is lineage in ETL?

A. Lineage in ETL (Extract, Transform, Load) refers to tracking the origin and transformation history of data as it moves through various processes.

Q2. What are the two types of data lineage?

A. The two types of data lineage are:
a- Forward lineage: Tracks the path of data from its source to its destination.
b- Backward lineage: Tracks the path of data from its destination back to its source.

Q3. How do you create a data lineage?

A. To create data lineage, you typically:
a- Identify data sources and destinations.
b- Document transformation processes.
c.- Establish data lineage tracking mechanisms, such as metadata management tools or manual documentation.

Q4.What is an example of a lineage?

A. An example of data lineage is tracing a customer’s purchase order from the online store’s database (source) through various transformation stages (e.g., data cleaning, aggregation) to the data warehouse (destination) where it’s analyzed for business insights.

Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

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