Examples of using Data is often in English and their translations into Chinese
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Political
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Ecclesiastic
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Programming
Big Data is often available in real time.
The resulting loss of data is often immensely damaging.
Such data is often collected, used and then discarded.
The transformed/augmented data is often loaded into a data warehouse for analysis.
Data is often locked in isolated islands that make it costly to extract and use.
The transformed/augmented data is often loaded into a data warehouse for analysis.
Data is often siloed within organizations or inaccessible to the larger scientific community.
Obviously, the world is not simple, and data is often missing fields, or contains explicit nulls or empty arrays.
Data is often processed repeatedly, either iteratively by a single tool or by using a number of tools to surface different types of insights.
Comparative data is often of greatest use to managers.
Data is often produced by a multitude of disconnected stand-alone or redundant systems that were generally implemented by functional areas to address specific needs.
However, this data is often siloed within separate systems.
This data is often used to determine the inflation level.
The satellite data is often combined with other sources of information.
Yet data is often siloed, especially in this new world wheredata can be a moat.
Digital footprint: big data is often a cost-free byproduct of digital interaction.
Personal data is often compared to oil- it powers today's most profitable corporations, like fossil fuels energized those of the past.
Dell'Oro network equipment data is often the benchmark for recognized industry share market rankings.
The data is often heterogeneous and lives across multiple relational and non-relational systems, from Hadoop clusters to NoSQL databases.
At this point, the data is often from multiple and different types of sources.
This data is often erroneous or missing.
But this data is often locked inside a specialized system.
Internal data is often an easy aspect for your data strategy to focus on.
Secondly, the data is often unstructured and raw, needing to be processed before analysis.
Contextual data is often overlooked, which is a mistake as it can be incredibly effective.
Comparing and evaluating data is often time-consuming manual work, especially when it comes from different sources.
However, experimental data is often noisy; it contains experimental errors that we do not want to introduce into our simulations.
That's because text data is often sufficient for a user to interact with a page, where multimedia data may be more supportive or decorative.