Data quality in the context of "Data validation"

The System of National Accounts or SNA (until 1993 known as the United Nations System of National Accounts or UNSNA) is an international standard system of concepts and methods for creating national accounts. The system is nowadays used by almost all countries in the world. SNA-type national accounts are among the world's most important sources of macroeconomic statistics. They provide essential data for empirical macroeconomic models and economic forecasting.

SNA aims to maintain an integrated, complete and up-to-date system of standard national accounts, for the purpose of economic analysis, policymaking and decisionmaking. When individual countries use SNA standards to guide the construction of their own national accounting systems, it results in much better data quality and comparability (between countries and across time). In turn, that helps to form more accurate judgements about economic situations, and to put economic issues in correct proportion — nationally and internationally.

Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle. It is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context even under the same general umbrella of computing. It is at times used as a proxy term for data quality, while data validation is a prerequisite for data integrity.

Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.Preprocessing is the process by which unstructured data is transformed into intelligible representations suitable for machine-learning models. This phase of model deals with noise in order to arrive at better and improved results from the original data set which was noisy. This dataset also has some level of missing value present in it.

The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis. Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection.

The System of National Accounts or SNA (until 1993 known as the United Nations System of National Accounts or UNSNA) is an international standard system of concepts and methods for creating national accounts. The system is nowadays used by almost all countries in the world. SNA-type national accounts are among the world's most important sources of macroeconomic statistics. They provide essential data for empirical macroeconomic models and economic forecasting.

When governments use SNA standards to guide the construction of their national accounting systems, it results in much better data quality and data comparability (between countries and across time). In turn, that helps to form more accurate judgements about economic situations, and to put economic issues in correct proportion — nationally and internationally.