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Reading list

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

Get to Know About Modern Data Governance

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Guest Blog Last Updated : 24 Mar, 2022
6 min read

Modern Data Governance

 

Introduction

Given the world’s growing user base across devices and applications in recent years, we have seen a huge surge in not just the volume of data we are collecting but also in the number and variety of sources. The pandemic has certainly accelerated this trend even more and having high quality and consistency of data has become mission-critical to successfully drive business outcomes for both business and data leaders.  

If you are part of the data team in any capacity be it data engineer, data scientist, data product manager, data analyst etc., you would have heard of common data governance issues of different kinds based on the type of data you work with or its primary user groups. 

  • Data standardization and integrity: upstream data format changes making the data or its derivatives hence less usable 
  • Data definitions: misunderstood definitions of certain data fields/columns/attributes or duplicate or inconsistent definitions 
  • Data access and user personas: ambiguity in who is accessing data and for what purposes due to poor logging or insufficient tagging 

A data lake or data warehouse hosting data for consumer and producer user groups without proper governance in place leads to chaotic operations with unplanned emergencies very quickly. So controls are necessary for data, its content, structure, use, privacy and security. Every organization needs these controls at different levels based on the complexity of the datasets, types of data they handle and the regulatory requirements around both the data and usage patterns of this data by both producers and consumers of this data. 

Enabling these controls typically involves 3 main steps:

  1. Creation and agreement of policy framework with relevant stakeholders
  2. Consistent implementation of these policies 
  3. Commitment to continuous evaluation and adaptation 

Data governance as a concept covers all these aspects. Traditionally this is an operations function limited to defining the specification of decision rights and an accountability framework to ensure the appropriate behaviour in the valuation, creation, consumption and control of data and analytics. However, to build continuous data governance, the legacy policies and formal meetings to enforce these policies won’t help. Lately, data governance is viewed as a bureaucratic way to control data, which impedes its usage and impairs data-driven decision making culture. So instead of helping democratize data, it is seen as a blocking function. There is no doubt we need data governance to reduce the risk of non-compliance, costs with reusability, improve productivity and in general give confidence in decision making to data consumers. 

So, if we want to treat data as a strategic asset, modern data governance should keep technology at the centre and help drive people, processes and tools to enable organizations to formally manage reliability, accountability, usability, trust and compliance of data to support business objectives with as much automation and self-serve capabilities as possible. 

Recently DataOps also has emerged as a concept to help us move in this direction and reimagine data governance by bringing together data engineering, analysts, operations, data scientists, data stewards and business teams. Gartner defines DataOps as “a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization”

How can we Achieve this?

There are a few primary goals or objectives for good data governance in general. The following outlines the framework of data governance we currently utilize:

  1. Building trust and Reliability

Aligned single source of truth, including related change management.

  1. Accountability definitions:

This involves defining the owners, governance stewards, subject matter or functional experts, and support teams for the full lifecycle of all data. 

  1. Regulatory compliance:

Handling Legal and Privacy requirements as a centralized component

  1. Continuous communication 

Usability aspects are right from data discovery, profiling, business-friendly definitions, flexibility for users to bring in their own tools with computing and another menu of provisioning services as part of the tech stack.

All these functionalities should ideally be part of the data platform governance and cataloguing to support transparency and accountability of the platform and to maintain/update the tools and processes, making it a collective responsibility of all teams involved.

In this article, we will discuss in detail the “Trust and Reliability” aspect of the above Data Governance framework. 

Trust and Reliability

In order to build trust into a platform, it’s important to bring transparency and agreement to what constitutes truth and align when, how or where there is a change.

What constitutes truth?

  • Metadata management

Metadata management with proactive documentation is perhaps one of the hardest parts of the overall data management practice. 

There are 2 different types of metadata – business and technical. 

Business metadata involves the definition of each column from a use case perspective and maintaining a contextual data catalogue and documentation for easy use. 

Technical metadata involves assigning data types and defining the format of the collected data. This is typically part of the data schema and needs to be aligned with producers and consumers of this data or some business transformed version of the data.

It also helps to follow standardized naming conventions at a platform level for predictability and usability of the data.

  • Assess quality

Define metrics in advance to measure data quality including data freshness. There are many data observability frameworks to track the quality of data. Nobody likes stale data so it is important to keep the data fresh by keeping a regular cadence or a rolling window of a refresh to the dataset. Each dataset is different so technology to configure as per business need is a must build trust in the data. Examples below

  • Missing or invalid data in columns – % data with Null values or different format
  • Data Volume levels – Thresholds to assess unusual increase or decrease in volume of data 
  • Data freshness level – Keeping a stakeholder-aligned cadence or a rolling window of a refresh for each dataset  
  • For ad-hoc datasets which are one time uploads, monitoring the data quality statuses with archive functionality turned on as required.

 

When there is a Change?

  • Metadata changes 

A clear handshake and standardized process are necessary ahead of time when the format, structure, volume or definition of columns in the dataset changes are planned. Protocols need to be defined with clearly documented policies for change. This should inherently be part of the overall data team culture with buy-in from leadership teams as part of cross-team collaborations. 

  • To achieve reliability it’s important to define service level agreements not just on the last leg of data pipelines leading to the end consumer but go all the way upstream to the source (raw log). Data engineering and business teams need to align on service level agreements and objectives for data availability. Based on this, appropriate monitoring and alerting should be put in place. Each dataset is of different priority for each consumer so to reduce the number of alarms/alerts, provide configuration functions within the platform that can be self-subscribed.
  • Mechanisms to communicate and handle change within the platform should be aligned on and standardized. 

How to Handle the Change?

  • Tools and Technology

Assessing the impact of the change both from a business standpoint and technical standpoint is required. Traceability and data lineage are some of the features that help with version control both to assess the impact of any potential changes or for auditing or reset. 

  • Process

When the goal is to have a common source of truth across the board along with the flexibility to the consumers to collaborate, there is a high chance of misalignment. This is a complex problem. In the past, we have had similar challenges for software delivery. Dev ops as a function helped developers and operations teams to collaborate and deliver faster by automating workflows, infrastructure, code testing and continuously measuring the performance. 

Similarly, before we publish a dataset or a new source of truth, it’s important that this is validated from the data content or definition standpoint but also dependencies associated with these datasets in terms of  SQL query or scripts. This type of Dataops activity is slowly becoming part of the data team function to assess and bridge this type of gap and support data governance, similar to the DevOps teams for Software development.

Conclusion

Typically for data strategy, the focus is on how to get the most value out of the data. However, a better data strategy takes into account end to end data lifecycle management and not just the optimal usage patterns of data. A key aspect of this end to end strategy is proactive data governance instead of a reactive fix to the issue. This will better enable companies to not only measure, experiment, scale, leverage new data sources but also to create new data products and drive higher value end-user usage/adoption patterns from data quickly. Whatsmore, it creates a shared responsibility across both data producers and consumers across all data platform activities, elevating the data-driven culture across the company – the holy grail for leadership teams. 

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Responses From Readers

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Irfan ali
Irfan ali

Nice information about the data governance which is much needed in the modern world. I enjoyed and learnt many things on how to extract result from getting the data. Nowadays the data science has the future and currently it has become most important for all industries.

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Dataphu
Dataphu

Nice Article Very informative and helpful Content.

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Dataphu
Dataphu

Nice Article Very informative and helpful Content. As the world has entered the Ai technology our data has also increased and to do business we have to know how to use the data to build a quality Business.

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