According to business managers in Italy, the main advantages of data-driven decisions were a faster decision-making process, improved management of business operations, and the development of new products and services. Only a minority of respondents, about 14 percent, saw data monetization as a benefit of making data-driven decisions.

This statistic depicts the results of a survey conducted in 2019 asking individuals about their opinion on gender related advantages in India. According to data published by Ipsos, a majority of respondents, around 38 percent, stated that there were more advantages in being a man. In contrast, about 24 percent of Indians perceived that there were more advantages in being a women.

According to a Capgemini survey conducted from December 2019 to February 2020, 46 percent of offline adults worldwide believed internet would benefit them by making them feel more connected to their friends and family. In comparison, 39 percent of offline respondents reported that having internet access would allow them to feel less left out by society.

The data shows net benefit cost ratio , benefit cost received and represented over monthly intervals.
Note:- (1) A chargeback, also referred to as a payment dispute, occurs when a cardholder questions a transaction and asks their card-issuing bank to reverse it.
(2) benefit cost ratio (BCR i)s the ratio of the benefits of a project or proposal, expressed in monetary terms, relative to its costs, also expressed in monetary terms. .

This comprehensive report chronicles the history of women in the military and as Veterans, profiles the characteristics of women Veterans in 2009, illustrates how women Veterans in 2009 utilized some of the major benefits and services offered by the Department of Veterans Affairs (VA), and discusses the future of women Veterans in relation to VA. The goal of this report is to gain an understanding of who our women Veterans are, how their military service affects their post-military lives, and how they can be better served based on these insights.

The aim of this survey was to chart how the universities in Finland have organised the depositing of digital research data and to what extent the data are reused by the scientific community after the original research has been completed. The respondents were professors of human sciences, social sciences and behavioural sciences in Finnish universities, and representatives of some research institutes. Opinions were also queried on the OECD guidelines and principles on open access to research data from public funding. First, the respondents were asked whether there were any guidelines or regulations concerning the depositing of digital research data in their departments, what happened to research data after the completion of the original research, and to what extent the data were reused. Further questions covered how often the data from completed research projects were reused in secondary research projects or for theses. The respondents also estimated what proportion of the data collected in their departments/institutes were reusable at the time of the survey, and why research data were not being reused in their own field of research. Views were also investigated on whether confidentiality or research ethics issues, or problems related to copyright or information technology formed barriers to data reuse. Opinions on the OECD Open Access guidelines on research data were queried. The respondents were asked whether they had earlier knowledge of the guidelines, and to what extent its principles could be implemented in their own disciplines. Some questions pertained to the advantages and disadvantages of open access to research data. The advantages mentioned included reducing duplicate data collection and more effective use of data resources, whereas the disadvantages mentioned included, for example, risks connected to data protection and misuse of data. The respondents also suggested ways of implementing the Open Access guidelines and gave their opinions on how binding the recommendations should be, to what extent various bodies should be involved in formulating the guidelines, and how the archiving and dissemination of digital research data should be organised. Finally, the respondents estimated how the researchers in their field would react to enhancing open access to research data, and also gave their opinion on open access to the data they themselves have collected. Background variables included the respondent's gender, university, and research field.

This statistic shows the benefits resulting from real-time customer analytics in the United States as of March 2018. According to the source, 85 percent of respondents cited improved customer experiences as one of the top three benefits resulting from real-time customer analytics.

For further background information on benefits statistics please follow links below: Benefit Statistics - Further Information Personal Independence Payment Statistics - Further information Geographical boundaries for Universal Credit statistics for the years 2017- 2023 are based on the Claimants postcode at 2023.

This data set contains information about any US government agency participating in the transit benefits program, funding agreements, individual participating Federal employees and details about commutes, supervisors and supervisory approvals, fare media in use, and transaction histories.

Annual benefits paid in each year from the Disability Insurance Trust Fund, by benefit type.

For further background information on benefits statistics please follow links below: Benefit Statistics - Further Information Personal Independence Payment Statistics - Further information

Geographical boundaries for Universal Credit statistics for the years 2017- 2023 are based on the Claimants postcode at 2023.

United States Benefit Payments: SE: Information data was reported at 25,220.000 USD th in 2015. This records an increase from the previous number of 20,201.000 USD th for 2014. United States Benefit Payments: SE: Information data is updated yearly, averaging 7,391.000 USD th from Sep 2001 (Median) to 2015, with 15 observations. The data reached an all-time high of 25,220.000 USD th in 2015 and a record low of 3,471.000 USD th in 2001. United States Benefit Payments: SE: Information data remains active status in CEIC and is reported by Pension Benefit Guaranty Corporation. The data is categorized under Global Database’s USA – Table US.G079: Single Employer Program Statistics.

United States Benefit Payments: SE: TP: Public Utilities data was reported at 124.000 USD th in 2015. This records a decrease from the previous number of 127.000 USD th for 2014. United States Benefit Payments: SE: TP: Public Utilities data is updated yearly, averaging 123.500 USD th from Sep 1996 (Median) to 2015, with 20 observations. The data reached an all-time high of 273.000 USD th in 2002 and a record low of 61.000 USD th in 1996. United States Benefit Payments: SE: TP: Public Utilities data remains active status in CEIC and is reported by Pension Benefit Guaranty Corporation. The data is categorized under Global Database’s USA – Table US.G078: Single Employer Program Statistics.

This dataset presents information from 2016 at the household level; the percentage of households within each Index of Household Advantage and Disadvantage (IHAD) quartile for Statistical Area Level 1 (SA1) 2016 boundaries. The IHAD is an experimental analytical index developed by the Australian Bureau of Statistics (ABS) that provides a summary measure of relative socio-economic advantage and disadvantage for households. It utilises information from the 2016 Census of Population and Housing. IHAD quartiles: All households are ordered from lowest to highest disadvantage, the lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided up into four groups, depending on their score. This data is ABS data (catalogue number: 4198.0) used with permission from the Australian Bureau of Statistics. For more information please visit the Australian Bureau of Statistics. Please note: AURIN has spatially enabled the original data.

This dataset presents information from 2016 at the household level; the percentage of households within each Index of Household Advantage and Disadvantage (IHAD) quartile for Local Government Area (LGA) 2017 boundaries. The IHAD is an experimental analytical index developed by the Australian Bureau of Statistics (ABS) that provides a summary measure of relative socio-economic advantage and disadvantage for households. It utilises information from the 2016 Census of Population and Housing. IHAD quartiles: All households are ordered from lowest to highest disadvantage, the lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided up into four groups, depending on their score. This data is ABS data (catalogue number: 4198.0) used with permission from the Australian Bureau of Statistics. For more information please visit the Australian Bureau of Statistics. Please note: AURIN has generated this dataset through aggregating the original SA1 level data (with calculated number of households/quartile) to LGA level.

United States Benefit Payments: SE: Finance, Insurance and Real Estate data was reported at 180,697.000 USD th in 2015. This records an increase from the previous number of 173,456.000 USD th for 2014. United States Benefit Payments: SE: Finance, Insurance and Real Estate data is updated yearly, averaging 53,750.000 USD th from Sep 1996 (Median) to 2015, with 20 observations. The data reached an all-time high of 224,606.000 USD th in 2010 and a record low of 2,026.000 USD th in 1996. United States Benefit Payments: SE: Finance, Insurance and Real Estate data remains active status in CEIC and is reported by Pension Benefit Guaranty Corporation. The data is categorized under Global Database’s United States – Table US.G079: Single Employer Program Statistics.