As data volumes grow and business information needs change, graph analytics can be a viable alternative to traditional analytics for specific problems. To assess whether graph analytics fits into your enterprise, you need to understand what graph analytics is and where you can use it.
- By Troy Hiltbrand
- June 14, 2019
To alleviate the drudgery of data preparation for your data science team, look for solution providers that are augmenting their data management platforms by using artificial intelligence, machine learning, and advanced analytics.
- By Troy Hiltbrand
- May 20, 2019
With the advent of automated machine learning, data scientists will need to adapt their role in the data science life cycle.
- By Troy Hiltbrand
- April 12, 2019
In today’s world, organizational data literacy is a critical component of a successful data science project. Here are the skills your end users must develop to become data literate.
- By Troy Hiltbrand
- February 25, 2019
To get the greatest value out of your organization’s data, your data science team needs to play five distinct roles: innovators, explorers, prototypers, optimizers, and responders.
- By Troy Hiltbrand
- January 9, 2019
Selling AI as a powerful solution for your enterprise is not always effective. Here’s how to focus your pitch to improve the chances your AI project will be funded.
- By Troy Hiltbrand
- January 3, 2019
Analytics is a fundamental part of the future digital economy. To be successful, you must recognize growing trends that will impact your analytics program.
- By Troy Hiltbrand
- January 2, 2019
As society strives to master artificial intelligence, it is recognizing the need for explainable AI. This emerging trend will force organizations to create models that are effective and good for society.
- By Troy Hiltbrand
- November 26, 2018