Precisely

MARCH 7, 2024

From documenting losses and damages to verifying that a claim submission meets all the necessary criteria, each step requires meticulous attention to detail and often entails reviewing lengthy narrative documents such as accident reports, medical records, and legal demands letters.

AI 72

AI AI Data Governance Data Quality 72

Precisely

APRIL 11, 2023

Let’s break it down: Step 1: Find the data and request access You can search previously cataloged datasets with key terms like “mailing address” to bring up a list of matching data using the data catalog. Easily search, explore, understand, and collaborate across your critical data assets.

Data Observability 52

Data Observability Cloud Data Data Quality Data Governance 52

Alation

MAY 24, 2022

This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. This kit offers an open DQ API, developer documentation, onboarding, integration best practices, and co-marketing support.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

The MLOps Blog

JUNE 27, 2023

User support arrangements Consider the availability and quality of support from the provider or vendor, including documentation, tutorials, forums, customer service, etc. Check out the Kubeflow documentation. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Precisely

SEPTEMBER 19, 2024

Badulescu cites two examples: Quality rule recommendations: AI systems can analyze existing data to understand data ranges, anomalies, relationships, and more. Then, this information can be used to suggest new quality rules that will help prevent data issues proactively.

Data Governance 78

Data Governance Data Quality AI AI 78

DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Precisely

SEPTEMBER 11, 2023

Here are some of the key capabilities, and what they mean for you: Auto metadata discovery: Use data profiles to enable the collection and detection of an extensive range of metadata upon data ingestion or on a scheduled basis. Context-based bots expedite information retrieval from documentation, knowledge bases, or metadata.

Data Quality 52

Data Quality AI AI ML 52

Precisely

OCTOBER 12, 2023

Documenting what data is most important , then understanding what policies apply, where that data is, and how it fits into the overall compliance picture for financial services. Monitoring and improving business quality rules and technical quality rules to define what “good” looks like.

Data Quality 64

Data Quality Data Governance ML ML 64

Precisely

OCTOBER 5, 2023

When we think about the big picture of data integrity – that’s data with maximum accuracy, consistency, and context – it becomes abundantly clear why data enrichment is one of its six key pillars (along with data integration, data observability, data quality, data governance, and location intelligence).

Data Quality 69

Data Quality Analytics Analytics Database 69

Pickl AI

JUNE 7, 2024

Open-Source Community: Airflow benefits from an active open-source community and extensive documentation. IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. More For You To Read: 10 Data Modeling Tools You Should Know.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

IBM Journey to AI blog

JULY 3, 2024

As data types and applications evolve, you might need specialized NoSQL databases to handle diverse data structures and specific application requirements. This approach ensures that data quality initiatives deliver on accuracy, accessibility, timeliness and relevance.

AI 45

AI AI Data Quality Database 45

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

phData

FEBRUARY 22, 2023

Create Standard Process Patterns As a team defines the various stages of data as it flows through their Snowflake environment, it is essential to document a few standard patterns. A pattern in this instance, can be understood as a logical combination of objects and data movement that fit together. What is a Pattern?

SQL 52

SQL Data Quality Database ETL 52

The MLOps Blog

AUGUST 3, 2023

You have the function docstring because with procedural code generally in script form, there is no place to stick documentation naturally. With Hamilton] you’re not overwhelmed, you have the docstring, a function for documentation, but then also everything’s unit testable by default – they didn’t have a good testing story.

ML 52

ML ML Data Scientist Machine Learning 52

Precisely

FEBRUARY 28, 2025

Finally, the project team may identify a need for external datasets to enrich your internal customer data with demographic, lifestyle, and geospatial information all of which provide essential context. Data governance would enable you to answer essential questions about your data usage, impact, and lineage.

Data Quality 69

Data Quality Data Governance Data Observability Analytics 69

Pickl AI

MARCH 19, 2025

It is widely used for storing and managing structured data, making it an essential tool for data engineers. MongoDB MongoDB is a NoSQL database that stores data in flexible, JSON-like documents. Apache Spark Apache Spark is a powerful data processing framework that efficiently handles Big Data.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52