Precisely

NOVEMBER 18, 2024

With the rise of cloud-based data management, many organizations face the challenge of accessing both on-premises and cloud-based data. Without a unified, clean data structure, leveraging these diverse data sources is often problematic. AI drives the demand for data integrity. Take a proactive approach.

Analytics 72

Analytics Analytics AI AI 72

Precisely

NOVEMBER 18, 2024

With the rise of cloud-based data management, many organizations face the challenge of accessing both on-premises and cloud-based data. Without a unified, clean data structure, leveraging these diverse data sources is often problematic. AI drives the demand for data integrity. Take a proactive approach.

Analytics 59

Analytics Analytics AI AI 59

Alation

JANUARY 20, 2022

Monitor and Measure with data quality remediation plans. These are useful in finding repeatable data issues, which will influence how you adapt your data governance framework. It also informs how you clean data and reeducate personnel at the data source within the data catalog.

Data Quality 94

Data Quality Data Governance Data Profiling Clean Data 94

DagsHub

AUGUST 23, 2024

Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while data scientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

JULY 24, 2024

Bias Systematic errors introduced into the data due to collection methods, sampling techniques, or societal biases. Bias in data can result in unfair and discriminatory outcomes. Read More: Data Observability vs Data Quality Data Cleaning and Preprocessing Techniques This is a critical step in preparing data for analysis.

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

Pickl AI

JULY 8, 2024

Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation. Step 3: Data Transformation Data transformation focuses on converting cleaned data into a format suitable for analysis and storage.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52