Sentence examples for data analytics results from inspiring English sources

This non-accuracy will not dramatically affect the data analytics results.

The findings suggest that the visualization of the dam layout, components and geometry within 3D settings overlaid with sensor data (which could be queried based on engineers' discipline-specific needs) and data analytics results provide a better flexibility to engineers to understand the risk associated with potential failure modes.

One of the biggest challenges in data analytics is presenting results in a way that's meaningful to people who aren't data scientists.

They're learning that R&D driven by customer insights, empirical research and data analytics yields better results.

Although effect sizes are small, our quantitative data and analytic results were consistent with these anecdotal data.

Thus, the tool – resulting from engineering and implementation of the concept – is integrating plant automation with intelligent data analytics, as a result of close collaboration between Tenaris Dalmine and the Manufacturing group of the Department of Management, Economics and Industrial Engineering of Politecnico di Milano.

There is also an analogous history for psychiatric brain imaging, whereby rich spatiotemporal data sets were progressively integrated with clinical data, and spawned a whole new field around experimental design, data analytics, and interpretation of results.

Of course, these methods are constantly used to improve the performance of the operators of data analytics process.1 The results of these methods illustrate that with the efficient methods at hand, we may be able to analyze the large-scale data in a reasonable time.

Spice up your marketing strategy with a few changes and utilize data analytics to see stronger results.

Essentially, the retrofittable Oden device and resulting data analytics makes the existing factory floor smarter.

We abstracted and summarized the following data from all included articles: citation; country; objectives; study sample, response and drop-out rates (where applicable); study design (cross-sectional or longitudinal); data collection (administrative, survey, or other primary data); analytic methodology; outcome measure(s); and results.