Sentence examples for as data expands from inspiring English sources

As data expands, customer experience takes center stage, and as technological complexity mounts, operations need to be optimized to ensure greater real-time visibility into project and workflow management, precise and predictive metrics for clients and powerfully visualized data about the overall performance of the marketing and sales funnel.

As the assembled data expands, it will become possible to discern patterns between phenotypes and come to more holistic conclusions about categories of genes.

As deep sequencing data expands, this global approach will undoubtedly uncover additional editing events and underlying determinants that will enhance our understanding of this post-transcriptional crosstalk.

As the amount of data expands exponentially, nearly all of it carries someone's digital fingerprints.

As the volume of data expands and the dependence on data increases, there's a higher likelihood of companies being potentially handcuffed by data if they're not disciplined and attentive.

As RNA-sequencing expression data expands from discovery into clinical applications, the sources and magnitudes of bias and variability must be carefully understood and quantified.

As the data expand, it becomes increasingly necessary to sort and display them in versatile ways in order to view phenotypes of particular interest.

Although the 'seed categories' were important in the early stages to guide interviews and analysis, their influence waned as the data expanded and the analytic approach became more inductive.

As the concept of health data expands (Vayena and Gasser, 2016; Vayena et al., 2016; World Health Organization 2017), further research is required to begin to understand the potential extent and force of a duty to participate in digital epidemiology across different sectors.

As each organization's pool of data expands and deepens, let's follow in the footsteps of Sir William Perkins and empower more people to embrace our digital generation's purple data.

As the amount of gene expression data expands exponentially and the volume of stored data available in repositories both public and private surges, it becomes increasingly important to develop methods which can accelerate the process of hypothesis testing at multiple levels of either the individual experiment, across projects, and even within an entire database [ 34].