The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights.

Data Governance 52

Data Governance High Quality Data Government Data 52

The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights.

Data Governance 52

Data Governance High Quality Data Government Data 52

Monte Carlo

JULY 11, 2023

When crucial information is omitted or unavailable, the analysis or conclusions drawn from the data may be flawed or misleading. Inconsistent data: Inconsistencies within a dataset can indicate inaccuracies. This can include contradictory information or data points that do not align with established patterns or trends.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Precisely

MARCH 14, 2023

New technologies are making it easier for customers to process increasingly large datasets more rapidly. First, private cloud infrastructure providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) began by offering more cost-effective and elastic resources for fast access to infrastructure. So how did providers respond?

Data Integration 52

Data Integration High Quality Data BI Data 52

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Knowledge Hut

JANUARY 25, 2024

What is Data Cleaning? Data cleaning, also known as data cleansing, is the essential process of identifying and rectifying errors, inaccuracies, inconsistencies, and imperfections in a dataset. It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Ascend.io

JUNE 8, 2023

Data sharing goes beyond simply making the data available. It can be thought of as the final “packaging” and disseminating of the data in a format that meets the specifications of different stakeholders, and is accessible, understandable, and usable across the organization. But how do you unlock these capabilities?

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Ascend.io

APRIL 27, 2023

Enhanced scalability: Data pipeline automation enables organizations to easily scale their data infrastructure to handle increasing data volumes and complexity, without compromising performance or agility. How to enhance data to meet reporting and analytics requirements.

Data Pipeline 52

Data Pipeline Pipeline-centric Data Validation Data Engineering 52