Smart Data Collective

OCTOBER 20, 2021

The data integration landscape is under a constant metamorphosis. In the current disruptive times, businesses depend heavily on information in real-time and data analysis techniques to make better business decisions, raising the bar for data integration. Legacy solutions lack precision and speed while handling big data.

ML 133

ML ML Big Data Big Data 133

Tableau

APRIL 18, 2022

What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* Data modeling. Data preparation. Virtualization and discovery. Orchestration.

Tableau 102

Tableau Data Quality Analytics Analytics 102

Tableau

APRIL 18, 2022

What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* Data modeling. Data preparation. Virtualization and discovery. Orchestration.

Tableau 98

Tableau Data Quality Analytics Analytics 98

Cambridge Intelligence

DECEMBER 6, 2024

Thats where visual link analysis comes in. The graph data model is a natural fit, helping investigators make sense of even the most complex datasets. Analysts armed with traditional tools struggle to uncover useful insights, and get lost in time-consuming processes.

Data Silos 40

Data Silos Data Analysis Data Analysis Data Models 40

Pickl AI

DECEMBER 4, 2023

In the realm of Data Intelligence, the blog demystifies its significance, components, and distinctions from Data Information, Artificial Intelligence, and Data Analysis. Key Components of Data Intelligence In Data Intelligence, understanding its core components is like deciphering the secret language of information.

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

phData

SEPTEMBER 1, 2023

This integration ability is particularly important because it allows companies to avoid the costly and time-consuming process of replacing everything in their current data lifecycle. By combining data from disparate systems, HCLS companies can perform better data analysis and make more informed decisions.

Analytics 52

Analytics Analytics Data Analysis Data Analysis 52

DrivenData Labs

MARCH 30, 2023

Privacy-enhancing technologies (PETs) have the potential to unlock more trustworthy innovation in data analysis and machine learning. Federated learning is one such technology that enables organizations to analyze sensitive data while providing improved privacy protections. Sitao Min is pursuing his Ph.D. at Rutgers University.

Machine Learning 162

Machine Learning Machine Learning Data Science Algorithm 162