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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

How AI is Improving the Data Management Systems?

Shweta Rawat
Shweta Rawat Last Updated : 29 Feb, 2024
8 min read

Introduction

Effective how ai is improving data management, in Data Management is crucial for organizations of all sizes and in all industries because it helps ensure the accuracy, security, and accessibility of data, which is essential for making good decisions and operating efficiently. Properly organizing and maintaining your data can help ensure that it is accurate and up to date. This is important because inaccurate data can lead to incorrect conclusions and poor decision-making. Well-managed data is easier to access and use, which can help you save time and reduce the risk of errors. In some cases, proper data management is required by law, such as the General Data Protection Regulation (GDPR) in the European Union.

Learning Objective

Database management system vendors are now deploying artificial intelligence, particularly machine learning, into the database itself. Diagnosis, monitoring, alerting, and protection of the database can now be done automatically by the software. 

In this session, we will cover the following objectives:

  • Why Management of data is very important, and how does it?
  • Importance of well-managed data for the better decision making
  • Role of AI in data management systems
  • How Automation saves plenty of time and plays a crucial role in data management systems?

In this DataHour, Avik has explained how AI is used efficiently for data management.

Table of contents

What Is the Role of AI in Modern Technology?

Artificial Intelligence, is like the brain of modern technology. It’s a field of computer science that aims to make machines think and learn like us humans. It’s all about creating smart machines capable of performing tasks that would normally require human intelligence. These tasks include learning, understanding language, recognizing patterns, problem-solving, and decision-making.

  • Learning and Adapting: Just like how we learn from our experiences, AI systems learn from data. They can adapt to new inputs, allowing them to perform tasks in a way that’s tailored to the individual user’s needs.
  • Understanding Language: AI plays a big role in understanding and interpreting human language. It’s the technology behind your voice assistants, chatbots, and translation services.
  • Recognizing Patterns: AI is excellent at recognizing patterns, much faster and more accurately than a human could. This is particularly useful in areas like fraud detection, where AI can spot suspicious activity based on patterns.
  • Problem-Solving and Decision-Making: AI can analyze a vast amount of data to make informed decisions. It’s used in healthcare to diagnose diseases, in finance to predict market trends, and in transportation for route optimization.
  • Automation: AI is a key player in automation. It can handle repetitive tasks, freeing up time for humans to focus on more complex and creative tasks.

The Disadvantage of Poorly Managed Data

We will start with a story for better understanding. There are 2 colleagues, Bob and Alice, work in different branches of the same company. Both of them are 500 miles apart from each other. Bob is an experimentalist in a systems biology project, and Alice is the Modeler in the same project.

Daily, Bob sends data to Alice. He normally puts it in a spreadsheet sent via email. Sometimes Alice gets a bit annoyed because the data looks different each time. Not the results but rather how the data is distributed on the sheet. Alice complains that she spends too much time writing software to make sense of the spreadsheets before actually starting to model the biological data contained in them.

Sometimes Alice has to ask Bob what he really means when he sends the data, like ‘what does the H in cell E1’ mean? And “* in cell F1”. Sometimes Alice has to ask Bob about old long forgotten experiments. He has to look up that information in the lab notebook. Sometimes Alice misunderstands the data representation and has to redo everything when the mistake is realized.

The lack of standardization and organization of data is not easy for Bob either. Bob often gets new students that he needs to compile and hand in the data, but it can take weeks to find everything and make it viewable for the new researcher. Bob had requests from other researchers about data from his papers; this data is archived and long forgotten.

He struggles to piece the original data together and has missed out on potential collaborations as a result. Bob and Alice’s bosses also don’t find this to be the perfect approach to work.

So, from the above story, we realized that data should be presented very simply so that it is easy to understand. Otherwise, it will impact the business.

Importance of Standardized Data

Benefits of Data Standardization

Data standardization is like tidying up a messy room. It’s the process of bringing data into a common format, making it easier to work with. Here are some of the key benefits:

  • Improved Data Quality: Just like cleaning up makes it easier to find things, standardizing data improves its quality. It helps eliminate duplicates, correct errors, and fill in gaps.
  • Easier Data Integration: Imagine trying to put together a puzzle with pieces from different boxes. That’s what it’s like working with non-standardized data. Standardization makes it easier to combine data from different sources.
  • Better Decision Making: With standardized data, you’re working with a ‘clean’ dataset. This means the insights and decisions based on this data are more accurate and reliable.
  • Increased Efficiency: Standardized data is easier to work with, saving time and resources. It’s like having everything in its right place.
  • Enhanced Compliance: Many industries have rules about how data should be handled. Standardization helps ensure that data is compliant with these rules.

Data standardization is a crucial step in managing and using data effectively. As we continue to generate more and more data, the importance of data standardization only grows.

The data formats can be predefined so that the identity of every cell of every column and row has an underlying identity known as a standardized format. The data sheets can be annotated with metadata so that all the information required to reproduce the experiment is packaged with the data itself. Standardized data improves Alice and Bob’s research collaboration by preventing misunderstandings. This data using these annotations can be stored in linked systems or common resources that allow colleagues, collaborators, and the public to find, access, combine and reuse this data whenever needed.

Data Management

We can say that AI engines and any other person are dependable on each other. Both have to be very organized and should have proper strings between them. So whatever one thinks, the other person should understand it. Here the AI engine needs to understand what Bob needs to do with his data.

Businesses need data management systems that run efficiently and at high performance. They should be capable of producing accurate results. This data needs to be accessible to data scientists for building the AI-enabled application. Hence, AI should be embedded in data management systems. If someone has the idea of how to use the data systematically, he/she can do it in 2 ways.

We always receive data from various sources with multiple formats. This data helps you predict the conclusions required for better decision-making. For this, you need to store and map the data to each other. It will connect such dots that can be described in the future.

Data Management

Always give the complete information/data to the engine. Otherwise, it would not give you the proper recommendations or predictions. The engine needs to learn from your data to give proper information. You can see there is raw data, processed data, and trusted data. Trusted data means you can use the data similarly, and this is validated data. Whatever the engine learns is validated by someone or some other engine.

Suppose you are going to use above mentioned data. We will use the entire data (present on LHS) for Data Visualization and Analytics. This data is very messy, unstructured, and raw. Hence, the data visualization tool will not give you the correct visualization.

How AI is Improving Data Management

Data Management to Data Fabric

Establishing enterprise AI capabilities requires expensive high-performance data architecture. In many organizations, creating a data ecosystem is nothing more than a five-dream event, i.e., the reality of budget limitation, legacy system, complexity, etc. This is where the concept of data fabric comes into use.

What is Data Fabric?

A distributed data management platform that can connect all the data points with all data management tools and services is known as Data Fabric. It serves as a unifying layer that enables data to be seamlessly accessed and processed.

AI-powered Data-Cleansing

Now, we will study AI-powered Data-Cleansing. Cleansing the data is very important because poor-quality data costs the companies badly. Bad data leads to bad decisions and hence causes loss.

As per the report, the average financial impact of poor data quality on organizations is 9.7 Million/year. In the US market, IBM found that businesses lose 3.1 trillion dollars annually due to poor data quality.

Data scientists are leveraging AI and its subset machine learning to automate and accelerate the data cleansing process.

Intelligent Enterprise Data Catalogs

Companies use data and digital management tools for inventory and organizing the data within their systems. For example, AWS azure provides many automated AI systems that will help a non-technical person use the data he needs.

AI and ML algorithms can also populate and update the data sets without human intervention. It reduces labor costs and manual work.

Autonomous vs. Autonomy in Data Management

Defining Autonomous Data Management

Autonomous Data Management is a technology that uses artificial intelligence (AI) and machine learning to automate the process of managing data. It’s like having a smart assistant that can handle various data-related tasks without needing much human intervention.

Autonomous Data Management is like a smart assistant for your data. It uses advanced technologies, such as AI, to manage data automatically.

  • Organizing Data: Autonomous Data Management systems can sort and store data on their own, just like how an assistant might organize files in an office.
  • Learning from Experience: These systems can learn and improve over time. They get better at their tasks the more they do them, much like how we humans learn from our experiences.
  • Spotting and Fixing Errors: These systems can identify potential problems in the data and correct them before they cause any issues. It’s like having a vigilant guard keeping an eye on your data.
  • Ensuring Security and Compliance: Autonomous Data Management systems can also help keep your data safe and ensure it meets any necessary regulations. They’re like a security guard and a compliance officer rolled into one.

The Balance of Autonomy in AI Data Management

As per Toby McClean, Forbes Council Member, Autonomy is self-sufficient and requires no human intervention. It can learn and adjust to dynamic environments and evolves as its environment changes. On the other hand, Autonomous is narrowly focused on specific tasks based on well-defined criteria and restricted to the certain tasks it can perform. Automation has played a key role in managing data for a long time.

Data Management

The four steps it uses to manage the data is Backup, automated discovery, protection, and workload balancing. It can analyze and predict the situation whenever there are chances of cyber attack and will heal itself.

Conclusion

Enterprises need to ensure whether their database systems are running efficiently or not. AI can help automate the management of queries based on their likely resource consumption. It reduces manual governance and work. AI improves query performance and accuracy. So, basically, it accelerates the productivity of Data scientists by handling most of the work itself. Hence, Automating the data management system is a crucial step.

Key Takeaways

  1. Well-managed data is crucial for better decision-making and avoiding business losses.
  2. Data stored in linked systems or common resources allows colleagues, collaborators, and the public to find, access, combine and reuse it whenever needed.
  3. AI helps in Data Fabric and Data Cleansing, which saves the productive time of Data Scientists.
  4. Automating data management systems saves time and manual labor, resulting in better business performance.

Frequently Asked Questions?

Q1. Why is data management important for organizations?

A. Data management ensures the accuracy, security, and accessibility of data, which are crucial for making informed decisions and operating efficiently across various industries.

Q2. What are the key benefits of well-managed data?

A. Well-managed data improves accuracy, saves time, reduces errors, enhances decision-making, and ensures compliance with regulations such as GDPR.

Q3. How does AI contribute to data management systems?

A. AI, particularly machine learning, is integrated into database management systems to automate tasks such as diagnosis, monitoring, alerting, and protection of the database, thereby improving efficiency and performance.

Q4. What role does data standardization play in effective data management?

Data standardization is like tidying up a messy room; it brings data into a common format, improving data quality, integration, decision-making, efficiency, and compliance.

Q5. How does AI enhance data management processes such as data cleansing and cataloging?

A. AI automates data cleansing processes, reducing errors and improving data quality. It also facilitates intelligent enterprise data cataloging, making data more accessible and organized.

The media shown in this article is not owned by Analytics Vidhya and is used from the presenter’s presentation.

Artificail Intelligencebusiness problemdata analysisdata managementdata visualisationstandardizationThe DataHour
Shweta Rawat
Shweta Rawat

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N K Gupta
N K Gupta

How to now proceed further to make use of AI in actually managing my Data & Record etc.

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