Astera

MAY 31, 2024

Data science covers the complete data lifecycle: from collection and cleaning to analysis and visualization. Data scientists use various tools and methods, such as machine learning, predictive modeling, and deep learning, to reveal concealed patterns and make predictions based on data.

Data Analytics 52

Data Analytics Data Analytics Data Warehouse Healthcare 52

Astera

OCTOBER 19, 2023

While all data transformation solutions can generate flat files in CSV or similar formats, the most efficient data prep implementations will also easily integrate with your other productivity business intelligence (BI) tools. Manual export and import steps in a system can add complexity to your data pipeline.

IBM Support 40

IBM Support Data Quality IBM Maintenance Real-time Data 40

Astera

JULY 18, 2023

Data Integration and Compatibility: The tools support various file formats, databases, APIs, and data connectors, which simplify data integration from diverse sources. This feature helps you in understanding data distributions, identifying patterns, and detecting outliers or anomalies.

Data Quality 52

Data Quality Visualization Data Visualization Documentation 52

Data Pine

MAY 2, 2023

To work effectively, big data requires a large amount of high-quality information sources. Where is all of that data going to come from? Financial efficiency: One of the key benefits of big data in supply chain and logistics management is the reduction of unnecessary costs. Now’s the time to strike.

Supply Chain 110

Supply Chain Logistics Big Data Customer Experience 110

Smart Data Collective

OCTOBER 17, 2022

The final point to which the data has to be eventually transferred is a destination. The destination is decided by the use case of the data pipeline. It can be used to run analytical tools and power data visualization as well. Otherwise, it can also be moved to a storage centre like a data warehouse or lake.

Data Warehouse 330

Data Warehouse Big Data Procurement Data Visualization 330

Astera

MAY 6, 2024

Type of Data Mining Tool Pros Cons Best for Simple Tools (e.g., – Data visualization and simple pattern recognition. Simplifying data visualization and basic analysis. – Steeper learning curve; requires coding skills. Can handle large volumes of data. – Quick and easy to learn.

Data Mining 52

Data Mining Supply Chain Data Quality Visualization 52

Data Pine

OCTOBER 30, 2019

Usually created with past data without the possibility to generate real-time or future insights, these reports were obsolete, comprised of numerous external and internal files, without proper data management processes at hand. The rise of innovative report tools means you can create data reports people love to read.

Visualization 51

Visualization Retail Data Visualization Government 51