data quality

USAGE SUMMARY

"data quality" is a correct and usable phrase in written English.
You can use this phrase if you want to refer to the overall level of accuracy and reliability of a data set. For example, "The company is dedicated to maintaining a high level of data quality in all its internal operations."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

58 human-written examples

Weidema, B. P. & Wesnæs, M. S. Data quality management for life cycle inventories: an example of using data quality indicators.

Nature

Assess data quality independently.

Harvard Business Review

Definition of Data Quality.

Massachusetts Institute of Technology

Data Quality Criteria and Contexts.

Massachusetts Institute of Technology

Data quality checks and heterozygosity patterns.

Nature

In addition, the data quality from feces is often poor.

The New York Times - Science

Her company calls the process "data quality management".

The New York Times - Tech

They also identified problems with the data quality and representativeness.

The Guardian

The major concern is the data quality.

Cornell University

Human-verified similar examples from authoritative sources

Similar Expressions

2 human-written examples

Table 4: Record description of the SHDI-Data-Quality file.

Nature

Improvement of Data Quality.

Massachusetts Institute of Technology

Expert writing Tips

Best practice

When discussing "data quality", specify the dimensions that are most relevant to your context, such as accuracy, completeness, or consistency, to provide a more precise understanding.

Common error

Avoid assuming that universally high "data quality" is always beneficial; focus on whether the data is fit for its specific purpose, even if it means prioritizing certain quality dimensions over others.

Linguistic Context

The phrase "data quality" functions primarily as a noun phrase, serving as a subject or object within a sentence. Ludwig examples show it's often used to describe attributes of datasets. As Ludwig AI confirms, the phrase follows standard grammatical rules and is usable in written English.

Expression frequency: Very common

Frequent in

Science

45%

News & Media

25%

Academia

15%

Less common in

Formal & Business

10%

Wiki

2%

Reference

2%

Ludwig's WRAP-UP

In summary, "data quality" is a common and grammatically sound noun phrase that refers to the degree to which data is fit for its intended purpose. As Ludwig AI points out, it is widely used across various domains including science, news media, and academia. While its usage is versatile, specifying the dimensions of quality (accuracy, completeness, etc.) can add precision. Remember to focus on contextual relevance rather than assuming universally high quality is always beneficial. Exploring alternatives like "accuracy of data" or "reliability of data" can help tailor your message.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

accuracy of data

Focuses specifically on the correctness and precision of the data.

reliability of data

Emphasizes the consistency and trustworthiness of the data over time.

integrity of data

Highlights the completeness and incorruptibility of the data.

validity of data

Indicates the extent to which the data measures what it is supposed to measure.

data precision

Refers to the level of detail and exactness in the data.

data correctness

Stresses the absence of errors and inaccuracies in the data.

data dependability

Highlights the trustworthiness and reliability of the data for decision-making.

quality of information

Broadens the scope to include information derived from the data.

data excellence

Emphasizes the superior quality and high standards of the data.

soundness of data

Indicates the robustness and reliability of the data for its intended purpose.

FAQs

How can I improve "data quality" in my organization?

You can improve "data quality" by implementing data validation rules, regularly cleaning and standardizing data, providing training on data entry, and establishing clear data governance policies.

What are the key dimensions of "data quality"?

The key dimensions of "data quality" include accuracy, completeness, consistency, timeliness, validity, and uniqueness. These dimensions help ensure data is reliable and fit for its intended use.

What is the impact of poor "data quality" on business decisions?

Poor "data quality" can lead to inaccurate insights, flawed business strategies, and ultimately, poor decision-making. Improving "accuracy of data" can help to prevent those outcomes.

How does "data quality" differ from data quantity?

"Data quality" refers to the accuracy and reliability of data, while data quantity refers to the amount of data available. A large quantity of data does not necessarily mean it is of high quality; it's essential to ensure both are adequate for effective analysis and decision-making.