It’s hard to get real-world information about what jobs pay, ALISON GREEN published a survey in 2021 on AskAManager.org, a US-centric-ish but does allow for a range of country inputs. The survey is designed to examine payment of different industries based on experience years, field experience years among other variables such as gender, race and education level.

The dataset is “live” and constantly growing, our dataset was downloaded in 23/2/2023.

Data Dictionary

The original dataset includes the following fields: * Age: How old are you? * Industry: What industry do you work in? * Job title: What is your job title? * Extra_job_title: If your job title needs additional context, please clarify here * Annual_salary: "What is your annual salary? If you are part-time or hourly, please enter an annualized equivalent -- what you would earn if you worked the job 40 hours a week, 52 weeks a year.)
* Annual_bonus: How much additional monetary compensation do you get, if any (for example, bonuses or overtime in an average year) only include monetary compensation here, not the value of benefits. * Currency: Please indicate your salary currency. * Other_currency: 'If "Other," please indicate the currency here. * Extra_income_info: "If your income needs additional context, please provide it here. * Work_country: "What country do you work in? * Work_state_US: "If you're in the U.S., what state do you work in? * Work_city: "What city do you work in? * Overall_experience_years: "How many years of professional work experience do you have overall? * Field_experience_years: "How many years of professional work experience do you have in your field?" * Education_level: "What is your highest level of education completed? * Gender: "What is your gender? * Race:"What is your race? (Choose all that apply.)

Money Supply M0 in the United States decreased to 5686400 USD Million in August from 5740300 USD Million in July of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Money Creek township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Money Creek township, the median income for all workers aged 15 years and older, regardless of work hours, was $42,604 for males and $39,643 for females.

Based on these incomes, we observe a gender gap percentage of approximately 7%, indicating a significant disparity between the median incomes of males and females in Money Creek township. Women, regardless of work hours, still earn 93 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Money Creek township, among full-time, year-round workers aged 15 years and older, males earned a median income of $54,191, while females earned $58,750

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.08 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Money Creek township median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Cash. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Cash, the median income for all workers aged 15 years and older, regardless of work hours, was $37,656 for males and $25,694 for females.

These income figures highlight a substantial gender-based income gap in Cash. Women, regardless of work hours, earn 68 cents for each dollar earned by men. This significant gender pay gap, approximately 32%, underscores concerning gender-based income inequality in the town of Cash.

- Full-time workers, aged 15 years and older: In Cash, among full-time, year-round workers aged 15 years and older, males earned a median income of $52,250, while females earned $28,281, leading to a 46% gender pay gap among full-time workers. This illustrates that women earn 54 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Cash, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cash median household income by race. You can refer the same here

Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The dataset contains information about world's biggest companies.

Among them you can find companies founded in the US, the UK, Europe, Asia, South America, South Africa, Australia.

The dataset contains information about the year the company was founded, its' revenue and net income in years 2018 - 2020, and the industry.

I have included 2 csv files: the raw csv file if you want to practice cleaning the data, and the clean csv ready to be analyzed.

The third dataset includes the name of all the companies included in the previous datasets and 2 additional columns: number of employees and name of the founder.

In addition there's tesla.csv file containing shares prices for Tesla.

Music Sales by Format and Year

Sales data for music industry by format and year

By Charlie Hutcheson [source]

About this dataset

The Music Industry Sales by Format and Year dataset provides comprehensive information on the sales data for different music formats over a span of 40 years. The dataset aims to analyze and visualize the trends in music industry sales, specifically focusing on various formats and metrics used to measure these sales.

The dataset includes several key columns to facilitate data analysis, including Format which represents the different formats of music sales such as physical (CDs, vinyl) or digital (downloads, streaming). Additionally, the column Metric indicates the specific measure used to quantify the sales data, such as units sold or revenue generated. The column Year specifies the particular year in which the sales data was recorded.

To provide a more comprehensive understanding of each combination of format, metric, and year, additional columns are included. The Number of Records column denotes the total number of entries or records available for each unique combination. This information helps assess sample size reliability for further analysis. Moreover, there is an Actual Value column that presents precise numerical values representing the actual recorded sales figure corresponding to each format-metric-year combination.

This dataset is obtained from credible sources including RIAA's U.S Sales Database and was originally presented through a visualization by Visual Capitalist. It offers insights into historical trends in music industry sales patterns across different formats over four decades.

In order to enhance this dataset visual representation and further explore its potential insights accurately, it would be necessary to perform an exploratory analysis assessing: seasonal patterns within each format; changes in market share across multiple years; growth rates comparison between physical and digital formats; etc. These analyses can help identify emerging trends in consumer preferences along with underlying factors driving shifts in market dynamics. Additionally,the presentation media (such as charts or graphs) could benefit from improvements such as clearer labeling, more detailed annotations,captions that allow viewers to easily interpret visualized information,and arrangement providing a logical flow conducive to understanding the data

How to use the dataset

Dataset Overview

The dataset consists of the following columns:

• Format: The format of the music sales, such as physical (CDs, vinyl) or digital (downloads, streaming).
• Metric: The metric used to measure the sales, such as units sold or revenue generated.
• Year: The year in which the sales data was recorded.
• Number of Records: The number of records or entries for each combination of format, metric and year.
• Value (Actual): The actual value of the sales for each combination of format, metric and year.

Key Considerations

Before diving into analyzing this dataset, here are some key points to consider:

• Categorical Variables: Both Format and Metric columns contain categorical variables that represent different aspects related to music industry sales.
• Numeric Variables: Year, Number of Records, and Value (Actual) are numeric variables providing chronological information about record counts and actual sale values.

Interpreting Insights

To make meaningful interpretations using this data set:

Analyzing Different Formats:

• You can compare different formats' popularity over time based on units sold/revenue generated.
• Explore how digital formats have influenced physical format sales over time.
• Understand which formats have experienced growth or decline in specific years.

Evaluating Different Metrics:

• Analyze revenue trends compared to unit count trends for different formats each year.
• Identify metrics showing exceptional growth/decline compared across differing years/formats.

Understanding Sales Trends:

• Examine the relationship between the number of records and actual sales value each year.
• Identify periods where significant changes in music industry sales occurred.
• Observe trends and fluctuations based on different formats/metrics.

Visualizing Data

To enhance your analysis, create visualizations using this dataset:

• Time Series Analysis: Create line plots to visualize the trend in music sales for different formats over time.
• Comparative Analysis: Generate bar charts or grouped bar plots...

Summary Since 2017, GEO shares have fallen sharply from $30 to ~$8.50 per share, at one point below even the book value of $8.19 per share. President Biden recently signed an executive order that banned the renewal of Department of Justice contracts with private prisons, but the effect on GEO is way way less than the market thinks. The border crisis renders ICE dependent on GEO for capacity, making it near impossible for ICE to cut ties in the near future. With a market cap of just $1.02 Billion, GEO has the potential to increase 2-3x in the next 6-12 months. cropped image of african american prisoner reading book LightFieldStudios/iStock via Getty Images Thesis GEO Group (GEO) is a deeply mispriced provider of privately-owned prisons, falling from a price of $30+ in early 2017 to the current price of $8.50 per share. GEO has fallen primarily as a result of concerns about legislation regarding private prisons, a canceled dividend, the likely shift away from a REIT structure, and high levels of debt. These overblown concerns have created a pretty solid structural opportunity. kmosby1992@gmail.com password kmosby1992@gmail.com Subscribe Company overview GEO operates in several segments, such as GEO care, International services, and U.S. Secure Services. Source: Annual report 1 - U.S. Secure Services U.S. Secure services account for the majority of their revenue, 67%, and includes their correctional facilities and processing centers. Secure services manage 74,000 beds across 58 facilities as of the 2020 annual report. GEO transport is included in U.S. secure services, but we felt it warranted its own paragraph. GEO transport provides secure transportation services to government agencies. With 400 customized, U.S. Department of Transportation compliant vehicles, GEO transport drove more than 14 million miles in 2020. 2 - GEO Care GEO care is a series of programs designed to reintegrate inmates and troubled youth into society. They operate through reentry centers, non-residential reentry programs, and youth treatment programs. GEO care operates approximately 4-dozen reentry centers, which provide housing, employment assistance, rehabilitation, substance abuse counseling, and vocational and education programs to current and former inmates. Through their reentry segment, they operate more than 70 non-residential reentry programs that provide behavioral assessments, treatment, supervision, and education. GEO care made up 23% of total 2020 revenue. Geo monitoring is included in GEO care. Through a wholly-owned subsidiary, BI Inc., GEO offers monitoring technology for parolees, probationers, pretrial defendants, and individuals involved in the immigration process. As of the 2020 annual report, BI helps monitor ~155,000 individuals across all 50 states. 3 - International operations International operations made up only 10% of revenue in 2020, but it is showing signs of growth. GEO recently landed a 10-year contract with the United kingdom, which they expect to total $760 million in revenue over the course of the contract. They also landed an 8-year contract with the Scottish Prison Service, which grants an annualized revenue of $39 million and has a 4-year renewal period. Why is GEO Mispriced? While there are several reasons for the dramatic reduction in share price over the last 4 years, the main reason was the looming fear of legislation destroying privately owned prisons. To a degree, this fear materialized on January 26th, 2021, when President Biden signed an Executive Order ordering the Attorney General not to renew any Department of Justice contracts with "privately operated criminal detention facilities." At face value, this order seems as though it would have a devastating impact on GEO. However, only ~25% of total revenue is impacted in any form by this order. The executive order only concerns branches of the Department of Justice. Only 2 DOJ branches have business connections with GEO, the US Marshals (USMS), and the Bureau of Prisons (BOP). Source: Annual report It is imperative to note that Immigration and Customs Enforcement (ICE), is not a branch of the DOJ and is therefore unaffected by this order. Individual states, as well as other countries, are unaffected by this order Bureau of Prisons GEO currently holds several agreements with the BOP relating to operations of prisons across the country. As of year-end 2020, agreements involving the BOP accounted for 14% of total revenue. All revenue from the BOP will not disappear, as the executive order does not impact reentry facilities. In 2Q21, after the executive order was made, GEO renewed 5 BOP reentry contracts. GEO even scored a new contract with the BOP, regarding the construction and operation of a new facility in Tampa. United States Marshal Service The United States Marshal Service does not own o... Visit https://dataone.org/datasets/sha256%3A900514e651e0d2c774ad90f358c9db90884c2baf98c068f470b290b3c4b3103a for complete metadata about this dataset.

This summary table shows, for Budget Receipts, the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for various types of receipts (i.e. individual income tax, corporate income tax, etc.). The Budget Outlays section of the table shows the total amount of activity for the current month, the current fiscal year-to-date, the comparable prior period year-to-date and the budgeted amount estimated for the current fiscal year for agencies of the federal government. The table also shows the amounts for the budget/surplus deficit categorized as listed above. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

Context

World champion barista James Hoffmann and Cometeer partnered to conduct a first-of-its-kind coffee taste test. Cometeer shipped 5000 coffee kits across America. Kits contained four different coffees - pre-extracted and flash frozen. Tasters melted and diluted the coffee capsules for a largely identical tasting experience. Tasting and ratings were conducted blind [1]. After survey responses were collected (provided data), some attributes of the coffee were revealed.

In October 2023, World champion barista James Hoffmann and coffee company Cometeer held the "Great American Coffee Taste Test" on YouTube, during which viewers were asked to fill out a survey about 4 coffees they ordered from Cometeer for the tasting. Data blogger Robert McKeon Aloe analyzed the data the following month.

Content

Geography: US

Time-period: 2023

Unit of Analysis: The Great American Coffee Taste Test

Variables

• submission_id = Submission ID
• age = What is your age?
• cups = How many cups of coffee do you typically drink per day?
• where_drink = Where do you typically drink coffee?
• brew = How do you brew coffee at home?
• brew_other = How else do you brew coffee at home?
• purchase = On the go, where do you typically purchase coffee?
• purchase_other = Where else do you purchase coffee?
• favorite = What is your favorite coffee drink?
• favorite_specify = Please specify what your favorite coffee drink is
• additions = Do you usually add anything to your coffee?
• additions_other = What else do you add to your coffee?
• dairy = What kind of dairy do you add?
• sweetener = What kind of sugar or sweetener do you add?
• style = Before today's tasting, which of the following best described what kind of coffee you like?
  -**strength** = How strong do you like your coffee?
• roast_level = What roast level of coffee do you prefer?
• caffeine = How much caffeine do you like in your coffee?
• expertise = Lastly, how would you rate your own coffee expertise?
• coffee_a_bitterness = Coffee A - Bitterness
• coffee_a_acidity = Coffee A - Acidity
• coffee_a_personal_preference = Coffee A - Personal Preference
• coffee_a_notes = Coffee A - Notes
• coffee_b_bitterness = Coffee B - Bitterness
• coffee_b_acidity = Coffee B - Acidity
• coffee_b_personal_preference = Coffee B - Personal Preference
• coffee_b_notes = Coffee B - Notes
• coffee_c_bitterness = Coffee C - Bitterness
• coffee_c_acidity = Coffee C - Acidity
• coffee_c_personal_preference = Coffee C - Personal Preference
• coffee_c_notes = Coffee C - Notes
• coffee_d_bitterness = Coffee D - Bitterness
• coffee_d_acidity = Coffee D - Acidity
• coffee_d_personal_preference = Coffee D - Personal Preference
• coffee_d_notes = Coffee D - Notes
• prefer_abc = Between Coffee A, Coffee B, and Coffee C which did you prefer?
• prefer_ad = Between Coffee A and Coffee D, which did you prefer?
• prefer_overall = Lastly, what was your favorite overall coffee?
• wfh = Do you work from home or in person?
• total_spend = In total, how much money do you typically spend on coffee in a month?
• why_drink = Why do you drink coffee?
• why_drink_other = Other reason for drinking coffee
• taste = Do you like the taste of coffee?
• know_source = Do you know where your coffee comes from?
• most_paid = What is the most you've ever paid for a cup of coffee?
• most_willing = What is the most you'd ever be willing to pay for a cup of coffee?
• value_cafe = Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
• spent_equipment = Approximately how much have you spent on coffee equipment in the past 5 years?
• value_equipment = Do you feel like you’re getting good value for your money when you buy coffee at a cafe?
• gender = Gender
• gender_specify = Gender (please specify)
• education_level = Education Level
• ethnicity_race = Ethnicity/Race
• ethnicity_race_specify = Ethnicity/Race (please specify)
• employment_status = Employment Status
• number_children = Number of Children
• political_affiliation = Political Affiliation

Acknowledgement

Datasource: The data is collected thorugh a survey called The Great American Coffee Taste Test held by James Haffmann

Inspiration: [Great American Coffee...

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Cash. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Cash. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Cash, where there exist only two delineated age groups, the median household income is $46,250 for householders within the 25 to 44 years age group, compared to $28,942 for the 45 to 64 years age group.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cash median household income by age. You can refer the same here

This table shows the gross outlays, applicable receipts and net outlays for the current month, current fiscal year-to-date and prior fiscal year-to-date by various agency programs accounted for in the budget of the federal government. This table includes total and subtotal rows that should be excluded when aggregating data. Some rows represent elements of the dataset's hierarchy, but are not assigned values. The classification_id for each of these elements can be used as the parent_id for underlying data elements to calculate their implied values. Subtotal rows are available to access this same information.

This dataset contains expense agency data by unit of appropriation for the Adopted, Financial Plan and Modified conditions by object code. The numbers within can be summarized to be consistent with data from either the Supporting Schedule, Departmental Estimate or the Expense, Revenue, Contact Budget. This dataset is updated three times per year after publication of the Preliminary, Executive and Adopted Budget, usually in January, April and June respectively.

Guatemala number dataset provides millions of powerful contacts for direct marketing. Similarly, this List To Data team carefully gathers these leads from many trusted sources. Also, you can get all confirmed leads from our site for any business to communicate with new clients. This Guatemala number dataset creates significant opportunities for growing company sales. Further, this Guatemala number dataset is highly effective for business promotion through cold calls and text messages. This marketing tool gives instant feedback from the consumers and expands contracts. Despite this, we deliver the number directory to you in CSV or Excel layout. In fact, everyone can run it in any CRM software without any trouble. Guatemala phone data is a very helpful contact library for SMS and telemarketing. Besides, the number directory plays a vital role in direct business plans. Most importantly, we prioritize safety and precisely adhere to all GDPR rules. Moreover, people can purchase this without any doubt from List To Data. In other words, you can make your business more famous by increasing productivity. Moreover, the Guatemala phone data helps in many ways to earn more money from this country. This country is very wealthy in all those sectors, thus everyone can buy our data package now. Our List To Data website is the perfect place to get all faithful client mobile contact numbers. In addition, our skilled team is ready to assist you 24/7 in supplying your necessary leads. Guatemala phone number list makes your business more profitable in a couple of months. This country has the nominal GDP (US$104 billion) and the most extensive by purchasing power parity (US$228 trillion). For this reason, it can create a big chance to earn more from here. As such agriculture, services, industry, and trade, are the main sources of income in Guatemala. Thus, you can get their mobile numbers from us for cold calls or Text messages. In addition, this Guatemala phone number list is far better for your business activities nationwide. Actually, you can do the marketing with this enormous group of people. Mainly, it will increase your deals rapidly and develop the company’s wealth. Indeed, as a businessman, you take your required sales leads from our website at a low cost.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

Corporate Profits in the United States increased to 3259.41 USD Billion in the second quarter of 2025 from 3252.44 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

" USA telephone number database is one of the most trusted marketing resources in the country. It contains thousands of active and verified numbers from USA. With this database, you can easily reach real people who are interested in your services. Moreover, it is a powerful telemarketing solution for businesses that want fast growth. Our list is updated and accurate, which means you get real contacts and better responses. Whether you run a campaign or need targeted outreach, this database can connect you with the right audience. Reaching customers in USA is easier with our data. In addition, it will save you time and money. This database can help your business grow quickly. You can connect with people by direct calls, promote products, and build trust with real clients.

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"

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

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Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

Consumer Spending in the United States increased to 16445.70 USD Billion in the second quarter of 2025 from 16345.80 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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