Sentence examples for ability to handle data from inspiring English sources
The phrase "ability to handle data" is correct and usable in written English.
You can use it when discussing someone's skills or competencies related to managing, processing, or analyzing data. Example: "Her ability to handle data effectively has greatly improved our team's productivity."
One of Tetra's original selling points was its ability to handle data as well as voice.
Prof Sir Ian Diamond, chair of the British Academy's quantitative skills strategy group, said: "Our ability to handle data and reason using numbers will not be transformed overnight.
Mr. Nilsson said Ericsson and its rivals, Huawei of China, Nokia Siemens Networks and Alcatel-Lucent, had continued to develop new, faster networks whose ability to handle data far outstripped current demand.
For Blundell, the ability to handle data, interpret it, respond and maintain a client's trust will be the hallmark of the marketer of the future: "Now the data is in real-time, we can look at the results as they come in.
As wireless phone networks become more advanced and their ability to handle data gets better, people may ditch their handheld computers for phones that do it all.
Microsoft would not certify NetApp machines as a platform for its SQL Server database and Exchange e-mail system until recently, when NetApp developed the ability to handle data at the block level, too.
Finally, to illustrate the ability of AAF to handle data for which it was designed – low-coverage data from poorly known species – we use AAF to reconstruct the phylogeny for 21 tropical tree species.
The elegance of the relational model lies in its conceptual simplicity, the availability of theoretical underpinnings (relational algebra), and the ability of its associated software to handle data relationships without the use of pointers.
While randomisation methods are often limited in their ability to handle missing data, the scope of data collection required by our method suggests that handling missing data and understanding how it impacts the allocations are important practical considerations.
Bioinformatics is a growth area, says Thornton, because "almost every experiment now involves multiple sources of data, requiring the ability to handle those data and to draw out inferences and knowledge.
However, the ability to handle spatial temporal data, visualize change, and explore the temporal dimension of spatial data is limited within conventional GIS.
