Data Governance, Data Profiling and Data Science

Advancing Data Fabric with Micro-segment Creation in IBM Knowledge Catalog

IBM Data Science in Practice

JANUARY 2, 2025

These SQL assets can be used in downstream operations like data profiling, analysis, or even exporting to other systems for further processing. Conclusion Creating microsegments represents a significant advancement in data fabric capabilities in CP4D. With this, businesses can unlock granular insights with minimal effort.

SQL

SQL Data Quality Data Profiling Data Preparation

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning

Machine Learning Machine Learning ML ML

Data integrity vs. data quality: Is there a difference?

IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Data science tasks such as machine learning also greatly benefit from good data integrity.

Data Quality

Data Quality Data Profiling Data Governance Machine Learning

Webinars

What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

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Unfolding the difference between Data Observability and Data Quality

Pickl AI

OCTOBER 10, 2023

Quality Data quality is about the reliability and accuracy of your data. High-quality data is free from errors, inconsistencies, and anomalies. To assess data quality, you may need to perform data profiling, validation, and cleansing to identify and address issues like missing values, duplicates, or outliers.

Data Observability

Data Observability Data Quality Data Governance Data Pipeline

How data engineers tame Big Data?

Dataconomy

FEBRUARY 23, 2023

It includes various processes such as data profiling, data cleansing, and data validation. Master data management: Master data management involves creating a single, unified view of master data, such as customer data, product data, and supplier data.

Big Data

Big Data Big Data Data Engineering Data Engineer

Data Hygiene Explained: Best Practices and Key Features

Pickl AI

JULY 19, 2023

By maintaining clean and reliable data, businesses can avoid costly mistakes, enhance operational efficiency, and gain a competitive edge in their respective industries. Best Data Hygiene Tools & Software Trifacta Wrangler Pros: User-friendly interface with drag-and-drop functionality. Provides real-time data monitoring and alerts.

Data Quality

Data Quality Data Profiling Data Governance Data Preparation

Unlocking the 12 Ways to Improve Data Quality

Pickl AI

OCTOBER 19, 2023

This proactive approach allows you to detect and address problems before they compromise data quality. Data Governance Framework Implement a robust data governance framework. Define data ownership, access rights, and responsibilities within your organization. How Do You Fix Poor Data Quality?

Data Quality

Data Quality Data Governance Data Warehouse Machine Learning

Data Observability Tools and Its Key Applications

Pickl AI

OCTOBER 11, 2023

While they provide various data-related tools, they may also offer features related to Data Observability within their platform. Informatica might enable organizations to monitor data flows and ensure data quality as part of their data management processes. It aims to address issues promptly as they arise.

Data Observability

Data Observability Data Quality Data Pipeline Data Governance

Data Quality Framework: What It Is, Components, and Implementation

DagsHub

AUGUST 23, 2024

We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. Data Governance Data governance is the foundation of any data quality framework. If any of these is missing, the client data is considered incomplete.

Data Quality

Data Quality Data Governance Machine Learning Machine Learning

Data architecture strategy for data quality

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Reduce data duplication and fragmentation.

Data Quality

Data Quality Data Lakes Data Warehouse Big Data

Understanding Data Migration: A Comprehensive Guide

Pickl AI

AUGUST 30, 2024

Data Quality Assessment Evaluate the quality of existing data and address any issues before migration. This may involve data profiling and cleansing activities to improve data accuracy. Testing should include validating data integrity and performance in the new environment.

Data Quality

Data Quality Data Governance Azure Database

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning

Machine Learning Machine Learning ML ML

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning

Machine Learning Machine Learning ML ML

Data Quality in Machine Learning

Pickl AI

JULY 24, 2024

Key Components of Data Quality Assessment Ensuring data quality is a critical step in building robust and reliable Machine Learning models. It involves a comprehensive evaluation of data to identify potential issues and take corrective actions. Conduct thorough data quality assessments to identify and prioritise issues.

Data Quality

Data Quality Machine Learning Machine Learning Clean Data

Avoid These Mistakes on Your Data Warehouse and BI Projects: Part 3

Dataversity

FEBRUARY 1, 2021

In Part 1 and Part 2 of this series, we described how data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations. Project sponsors seek to empower more and better data-driven decisions and actions throughout their enterprise; they intend to expand their […].

Data Warehouse

Data Warehouse Business Intelligence Business Intelligence Data Profiling

Data Science Current

Advancing Data Fabric with Micro-segment Creation in IBM Knowledge Catalog

MLOps Landscape in 2023: Top Tools and Platforms

Webinars

Trending Sources

Data integrity vs. data quality: Is there a difference?

Webinars

Unfolding the difference between Data Observability and Data Quality

How data engineers tame Big Data?

Data Hygiene Explained: Best Practices and Key Features

Unlocking the 12 Ways to Improve Data Quality

Data Observability Tools and Its Key Applications

Data Quality Framework: What It Is, Components, and Implementation

Data architecture strategy for data quality

Understanding Data Migration: A Comprehensive Guide

Capital One’s data-centric solutions to banking business challenges

Capital One’s data-centric solutions to banking business challenges

Data Quality in Machine Learning

Avoid These Mistakes on Your Data Warehouse and BI Projects: Part 3

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