Astera

MAY 9, 2023

Data Quality While traditional data integration tools have been sufficient to tackle data quality issues, up till now, they can no longer handle the extent of data coming in from a myriad of sources.

Healthcare 40

Healthcare Retail Finance Data Quality 40

Astera

MAY 4, 2023

This, in turn, enables businesses to automate the time-consuming task of manual data entry and processing, unlocking data for business intelligence and analytics initiatives. However , a Forbes study revealed up to 84% of data can be unreliable. Luckily, AI- enabled data prep can improve data quality in several ways.

Business Intelligence 40

Business Intelligence Business Intelligence Predictive Analytics Supply Chain 40

Astera

MAY 31, 2024

Here are the critical components of data science: Data Collection : Accumulating data from diverse sources like databases, APIs , and web scraping. Data Cleaning and Preprocessing : Ensuring data quality by managing missing values, eliminating duplicates, normalizing data, and preparing it for analysis.

Data Analytics 52

Data Analytics Data Analytics Data Warehouse Healthcare 52

Astera

MAY 26, 2023

In the highly competitive retail sector of today, time is of the essence. Manual data extraction processes are laborious, error-prone, and consume valuable resources that could be better utilized elsewhere. But this is where automated invoice data extraction comes to the rescue. days per invoice.

Retail 40

Retail Supply Chain Finance Data Quality 40

Astera

SEPTEMBER 20, 2023

Data Movement Data movement from source to destination, with minimal transformation. Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

SEPTEMBER 20, 2023

Data Movement Data movement from source to destination, with minimal transformation. Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

The BAWorld

NOVEMBER 22, 2021

Completeness is a data quality dimension and measures the existence of required data attributes in the source in data analytics terms, checks that the data includes what is expected and nothing is missing. Consistency is a data quality dimension and tells us how reliable the data is in data analytics terms.

Data Analytics 98

Data Analytics Data Analytics Data Warehouse Data Modelling 98