Corporate Profits in the United States decreased to 3203.60 USD Billion in the first quarter of 2025 from 3312 USD Billion in the fourth quarter of 2024. 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.

The number of small and medium-sized enterprises in the United States was forecast to continuously decrease between 2024 and 2029 by in total 6.7 thousand enterprises (-2.24 percent). After the fourteenth consecutive decreasing year, the number is estimated to reach 291.94 thousand enterprises and therefore a new minimum in 2029. According to the OECD an enterprise is defined as the smallest combination of legal units, which is an organisational unit producing services or goods, that benefits from a degree of autonomy with regards to the allocation of resources and decision making. Shown here are small and medium-sized enterprises, which are defined as companies with 1-249 employees.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Bankruptcies in the United States increased to 23309 Companies in the first quarter of 2025 from 23107 Companies in the fourth quarter of 2024. This dataset provides - United States Bankruptcies - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Employee turn-over (also known as "employee churn") is a costly problem for companies. The true cost of replacing an employee can often be quite large. A study by the Center for American Progress found that companies typically pay about one-fifth of an employee’s salary to replace that employee, and the cost can significantly increase if executives or highest-paid employees are to be replaced. In other words, the cost of replacing employees for most employers remains significant. This is due to the amount of time spent to interview and find a replacement, sign-on bonuses, and the loss of productivity for several months while the new employee gets accustomed to the new role.

In the past, most of the focus on the "rates" such as attrition rate and retention rates. HR Managers compute the previous rates try to predict the future rates using data warehousing tools. These rates present the aggregate impact of churn, but this is the half picture. Another approach can be the focus on individual records in addition to aggregate.

There are lots of case studies on customer churn are available. In customer churn, you can predict who and when a customer will stop buying. Employee churn is similar to customer churn. It mainly focuses on the employee rather than the customer. Here, you can predict who, and when an employee will terminate the service. Employee churn is expensive, and incremental improvements will give significant results. It will help us in designing better retention plans and improving employee satisfaction.

The HR dataset has 14,999 samples with various information about the employees. In the given dataset, we have two types of employee one who stayed and another who left the company. This given dataset will be used to predict when employees are going to quit by understanding the main drivers of employee churn.

We can describe 10 attributes (features) in detail as:

satisfaction_level : It is employee satisfaction point, which ranges from 0-1.

last_evaluation : It is evaluated performance by the employer, which also ranges from 0-1.

number_projects : How many of projects assigned to an employee?

average_monthly_hours: How many hours in averega an employee worked in a month?

time_spent_company : time_spent_company means employee experience. The number of years spent by an employee in the company.

work_accident : Whether an employee has had a work accident or not.

promotion_last_5years: Whether an employee has had a promotion in the last 5 years or not.

Departments : Employee's working department/division.

Salary : Salary level of the employee such as low, medium and high.

left : Whether the employee has left the company or not.

We present a dataset created from merged secondary sources of ExecuComp and CompuStat and then augmented with manual data collection through searches of news stories related to CEO turnover.

We start dataset construction with the ExecuComp executive-level data for the period from 1992 through 2020. These data are merged with the CompuStat dataset of financial variables. As the dataset is intended for research on CEO turnover, we exclude observations in which the CEO at the start of the fiscal year is not well-defined; these are cases when there were co-CEOs and cases when the CEO was shared across different firms. The data set also excludes firm/year combinations that involve a restructuring of the firm – spinoff, buyout, merger, or bankruptcy.

We identify the CEO at the start of each year for each firm. This also helps identify the last year an individual served as CEO. In order to identify CEO turnover based on changes in the CEO from year to year, we require firm observations to extend over at least six contiguous years for the firm to remain in the sample. Cases involving the last year the firm is in the sample are excluded. We also exclude from the dataset cases when there was an interim CEO who stayed in the position for less than 2 years. This results in a sample of 3,100 firms reflecting 41,773 firm/year combinations.

For this sample, we examine news articles related to CEO turnover to confirm the reasons for each CEO departure case. We use the ProQuest full-text news database and search for the company name, the executive name, and the departure year. We identify news articles mentioning the turnover case and then classify the explanation of each CEO departure case into one of five categories of turnover. These categories represent CEOs who resigned, were fired, retired, left due to illness or death, and those who left the position but stayed with the firm in a change of duties, respectively.

The published data file does not include proprietary data from ExecuComp and CompuStat such as executive names and firm financial data. These data fields may be merged with the current data file using the provided ExecuComp and CompuStat identifiers.

The dataset consists of a single table containing the following fields: • gvkey – unique identifier for the firms retrieved from CompuStat database • firmid – unique firm identifier to distinguish distinct contiguous time periods created by breaks in a firm’s presence in the dataset • coname – company name as listed in the CompuStat database • execid – unique identifier for the executives retrieved from ExecuComp database • year – fiscal year • reason – reason for the eventual departure of the CEO executive from the firm, this field is blank for executives who did not leave the firm during the sample period • ceo_departure – dummy variable that equals 1 if the executive left the firm in the fiscal year, and 0 otherwise

This dataset shows all active tow truck companies licensed by the NYC Department of Consumer and Worker Protection (DCWP) and the tow company's enrollment status in the Directed Accident Response Program (DARP) and/or the Rotation Tow Program (ROTOW). In New York City, licensed tow companies enrolled in DARP or ROTOW may tow vehicles without the vehicle owner's consent. Tow companies enrolled in DARP may tow vehicles that have been involved in an accident and cannot safely be driven under their own power. Tow companies enrolled in ROTOW may tow stolen or abandoned vehicles that have been recovered, and when a vehicle is blocking driveways. A business may apply to participate in DARP and ROTOW after holding a Tow Truck Company license for more than one year.

Lists all Coal and Metal/Non-Metal mines under MSHA's jurisdiction. Including current status of each mine (Active, Abandoned, Nonproducing, etc.), the current owner and operating company, commodity codes and physical attributes of the mine. Mine ID is the unique key for this data. (Includes Abandoned or Abandoned and Sealed Mines)

NoSQL Database Market Outlook 2032

The global NoSQL database market size was USD 5.9 Billion in 2023 and is likely to reach USD 36.6 Billion by 2032, expanding at a CAGR of 30% during 2024–2032. The market growth is attributed to the rising adoption of NoSQL databases by industries to manage large amounts of data efficiently.



Increasing adoption of digital solutions by businesses is augmenting the NoSQL database industry. Businesses continue using the unique capabilities that NoSQL databases bring to their data management strategies. The NoSQL solutions work without any predefined schemas, thus, offering more flexibility to businesses that need to handle and manage ever-evolving data types and formats.

The factors behind the accelerating growth of the NoSQL database market include the omnipresence of internet-related activities, a surge in big data, and others. NoSQL database solutions present exceptional scalability and offer superior performance while managing extensive datasets. Moreover, the shift from conventional SQL databases to NoSQL databases to handle big-data and real-time web application data augmented the market.

Impact of Artificial Intelligence (AI) on the NoSQL Database Market

Artificial Intelligence (AI) has a significant impact on the NoSQL databases market by creating a surge in data volume and variety. AI technologies, including machine learning and deep learning, generate and process vast amounts of data, necessitating efficient data management solutions. The integration of AI with NoSQL databases further enhances data analysis capabilities and enables businesses to acquire valuable insights and make informed decisions. Therefore, the rise of AI technologies is propelling the market.

Non-Relational Databases, commonly referred to as NoSQL databases, have gained significant traction in recent years due to their ability to handle diverse data types and structures. Unlike traditional relational databases, non-relational databases do not rely on a fixed schema, which allows for greater flexibility and scalability. This adaptability is particularly beneficial for businesses dealing with large volumes of unstructured data, such as social media content, customer reviews, and multimedia files. As organizations continue to embrace digital transformation, the demand for non-relational databases is expected to rise, further driving the growth of the NoSQL database market.

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List of alcohol licenses of businesses who have gone out of business

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.