2. Consolidate data in a cloud data warehouse. Everyone's extolling the virtues of MongoDB and Elasticsearch, but that's like buying a Ferrari when you're only planning to drive the speed limit.

Data analytics determines if patterns, trends, or insights can help inform you of your data set. Data analytics involves collecting, cleaning, and then compiling the data in certain ways. It ...

Also see: What is Data Analytics? What are Data Modeling Techniques? There are various techniques to achieve data modeling successfully, though the basic concepts remain the same across techniques ...

Learn Power BI basics in this beginner tutorial. Master the interface, connect data sources, clean data, and build your first data model.

In this article, I have taken Gartner’s four-phase data maturity model (pg. 9) and customized it to help assess the maturity of your data-driven customer retention program.

For effective data-driven businesses, data modeling and data analysis share a lot of common ground. They must both be aligned with business goals and priorities.

The best model for predictive analytics depends on several factors, such as the type of data, the objective of the analysis, the complexity of the problem, and the desired accuracy of the results.

This can happen even when the sparse data might be more predictive. Best machine learning model for sparse data To help combat these issues that arise with sparse data machine learning, there are a ...

Erika Glenn, Division Head of Sales, Care Management, CVS Health, addressed some of the latest innovations in predictive modeling and data analytics being leveraged by her organization in the ...