Sentence examples for data-driven analytics from inspiring English sources
The phrase "data-driven analytics" is a valid and commonly used term in written English. It refers to the use of data and statistical analysis to drive decision making and inform strategies. You can use "data-driven analytics" in various contexts, such as in business, marketing, or research. For example: - Our company's success can be attributed to our use of data-driven analytics to identify customer needs and tailor our products accordingly. - The campaign's effectiveness was improved through the integration of data-driven analytics in our marketing strategy. - As a researcher, I rely on data-driven analytics to draw meaningful conclusions from my experiments.
Enthusiastic and rapid adoption of data-driven analytics has happened at the expense of meaningful public input, safeguards against misconduct and other basic procedures of accountability.
But it has made huge strides in helping America's non-profit sector manage social capital to transform good intentions and intangible outcomes into data-driven analytics and measurable results.
We demonstrate how data-driven analytics can help make informed decisions on innovation and economic growth.
The industrial big data pipeline focuses on supporting data-driven analytics applications for predictive and intelligent equipment maintenance.
Real-time and internet-aware pervasive networks, as well as highly integrated and intelligent data-driven analytics applications, are central to smart manufacturing.
These teams showed a positive attitude towards smart manufacturing approaches, with plans to integrate IoT, big data systems and data-driven analytics being a recurring theme.
But after he got to Congress, he dug deeper, and came to what he calls "a data-driven, analytic conclusion".
"This data-driven, analytics-based approach has proved effective in explaining mergers and acquisitions in the startup world and complements existing toolkits for measuring business proximity," the researchers say.
In Section "Enabling technologies", we discuss modern technologies along with machine learning techniques that enable data-driven crisis analytics.
This paper presents an industrial big data pipeline architecture, which is designed to meet the needs of data-driven industrial analytics applications focused on equipment maintenance in large-scale manufacturing.
Our analytical approach is evidence-based and data-driven, using network analytics, network metrics, and their visualizations to explain the relationship dimension of innovation ecosystems.
