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).

Abstract (en): Dun's Review began publishing monthly data on business failures by branch of business during the 1890s. At that time, a business failure was defined as a concern which was involved in a court proceeding or voluntary action which was likely to end in loss to creditors. Liabilities of failed businesses were defined "as all liabilities except long-term publicly-held obligations, chiefly bonds." Dun's published data on failures by branch of business from 1895 through 1935. This dataset reconstructs that series and links it to its successors. The successor series include data on business failures by division of industry, which Dun and Bradstreet's published from 1934 through 1940. This study includes six parts. Part One contains aggregate liabilities in dollars, broken down by branch, month, and year. Part Two contains aggregate numbers of business failures broken down by branch, month, and year. Part Three contains aggregate liability in dollars broken down by division, month, and year. Part Four contains aggregate numbers of business failures broken down by division, month, and year. Part Five contains aggregate liabilities broken down by sector, month, and year. Part Six contains aggregate numbers of business failures broken down by sector, month, and year. Part One and Part Two contain 36 variables and 562 cases. Part Three and Part Four contain 51 variables and 60 cases. Part Five and Part Six contain 6 variables and 562 cases. This study allows for economic analysis of business failures. It is intended to provide a resource on business failure and liabilites from 1895 to 1940. Data originally collected from court filings at municipal, county, state, and United States district court houses throughout the United States from 1895 through 1940. Data published periodically by R. G. Dun and Company, Bradstreet's Company, and their successors through 1940. From their publications, the principal investigators collected, cleaned, compiled, and computerized the current data series. Variables include monthly, unadjusted, liabilities and monthly, unadjusted, number of failures for different branches, sectors, divisons. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Businesses that failed in the United States from 1895 through 1940. Smallest Geographic Unit: United States The data consist of the aggregate number of corporations filing for bankruptcy in various industries each month in the United States and the total liabilities of those corporations. Please refer to the codebook for sampling information in the "Original P.I. Documentation" section. Additional information can be found by visiting the National Bureau of Economic Research (NBER) Web site. For additional information on these datasets please see the National Bureau of Economic Research (NBER) Web site.The dates in the Original P.I. Documentation for Business Failures by Industry in the United States range from 1895 to 1939, however, the data range from 1895 to 1940. The title for ICPSR 34016 has been changed to reflect the data.

The Los Angeles BusinessSource Centers provide startup ventures and current small business owners various cost effective tools to make their business a success. Through these tools, small businesses can grow and remain competitive within the City of Los Angeles. Startups focuses on owners of businesses with five (5) or fewer employees, one of whom owns the enterprise, and have net operating income of less than Two Hundred Thousand Dollars ($200,000). This focus is particularly important as the majority of the businesses within the City may be categorized as “survivors,” and historically, many such businesses fail in their first two years of operation. The survival and growth of such businesses is still very important to the ongoing economic vitality of the City.

Many small businesses and startups struggle to adjust their operational plans to quickly changing market and financial situations. Traditional data-driven techniques often miss possibilities and waste resources. Our unique approach, Unified Statistical Association Validation (USAV), allows dynamic and real-time data association and improvement assessment to address this essential issue. USAV classifies and validates critical data associations based on business features to improve startup incubation and innovation decision-making. USAV analyses different financial eras using federated learning to find performance inefficiencies using a Kaggle dataset on small business success and failure. USAV recommends actionable improvements during innovation using non-recurrent statistical patterns, unlike standard models that use prior financial data. The framework allows real-time flexibility with continual statistical updates without data redundancy. The proposed approach achieved an improvement assessment score of 0.98, data association accuracy of 96%, statistical update efficiency of 0.97, modification ratio of 35%, and incubation analysis time reduction of 240 units in experimental evaluation. These findings demonstrate USAV’s ability to help strategic decision-making in dynamic corporate situations.

The Global Entrepreneurship Monitor (GEM) was designed to capture various aspects of firm creation and entrepreneurship across countries. The data have been collected over a number of years (1998-2003) and include responses from 4,685 experts in over 38 countries and three subnational regions. This study seeks to measure the national attributes considered critical for new firm births and small firm growth. The dataset is a harmonized file capturing the results from all of the surveys. The expert, or key informant, questionnaire was improved and adjusted each year to increase the reliability of multi-item indices and provide for the addition of new dimensions. For each version of the questionnaire, respondents completed 70-80 standardized items that were the basis for 12-15 multi-item indices. Respondents were initially asked a series of general questions pertaining to starting a business, such as whether they were currently trying to start a new business, whether they knew anyone who had started a new business, and whether they thought it was a good time to do so. Respondents were also asked about the process of starting up a new business; whether they had done anything to start a new business in the past 12 months; whether they would own all, part, or none of the new business; how many people would be involved with the new business; what sort of business they were starting; and what they would sell. In addition, respondents identified the total start-up costs, the various sources of the start-up money, and why they were involved in the start-up. Respondents then answered a set of questions to assess the national conditions influencing entrepreneurial activity in their own country. In this respect, respondents provided their opinions on business and entrepreneurial education, the integration of new technology in businesses, the availability of financial support through government policies and programs, the availability of subcontractors, yearly changes in the economic market, and the physical infrastructure in their country. Views were also elicited from respondents about their national cultures in regard to entrepreneurial efforts and opportunities, attitudes towards entrepreneurs in general, women entrepreneurs and the resources available to them, and citizens' knowledge and experience with new businesses. They also gave their views on the Intellectual Property Rights (IPR) legislation and its enforcement in their respective countries. Respondents were then queried on the technological strengths of their country by ranking the top five sectors in which there has been development of the greatest number of technology-intensive start-up companies in the past ten years. Finally, respondents were asked the same general questions as those used in the GLOBAL ENTREPRENEURSHIP MONITOR (GEM): ADULT POPULATION SURVEY DATA SET, 1998-2003 (ICPSR 20320) in order to ascertain whether the opinions and behaviors of the current "expert" respondents differ from those of the general population. These questions included whether they were starting a new business, if there were opportunities for new businesses, funding sources for a new business, skills required to start a new business, shutting down a business, and whether a fear of failure was preventing the start of a new business. The dataset also contains variables that describe the respondent's gender, age, educational attainment, labor force status, the entrepreneurial areas in which they feel they have strong expertise, and the month and year the survey was conducted.

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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.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt

***Licenses or restrictions***
CC-BY

This study is about what matters: predicting when microfinance institutions might fail, especially in places where financial stability is closely linked to economic inclusion. The challenge? Creating something practical and usable. The Adjusted Gross Granular Model (ARGM) model comes here. It combines clever techniques, such as granular computing and machine learning, to handle messy and imbalanced data, ensuring that the model is not just a theoretical concept but a practical tool that can be used in the real world.Data from 56 financial institutions in Peru was analyzed over almost a decade (2014–2023). The results were quite promising. The model detected risks with nearly 90% accuracy in detecting failures and was right more than 95% of the time in identifying safe institutions. But what does this mean in practice? It was tested and flagged six institutions (20% of the total) as high risk. This tool’s impact on emerging markets would be very significant. Financial regulators could act in advance with this model, potentially preventing financial disasters. This is not just a theoretical exercise but a practical solution to a pressing problem in these markets, where every failure has domino effects on small businesses and clients in local communities, who may see their life savings affected and lost due to the failure of these institutions. Ultimately, this research is not just about a machine learning model or using statistics to evaluate results. It is about giving regulators and supervisors of financial institutions a tool they can rely on to help them take action before it is too late when microfinance institutions get into bad financial shape and to make immediate decisions in the event of a possible collapse.

Annual data on births, deaths and survival of businesses in the UK, by geographical area and Standard Industrial Classification 2007: SIC 2007 groups.

With competition getting more stiffer in telecom direct-to-home operators, complaint management process will lead to the key outcome on business survival and growth.

As per the prevailing process and business setups, to cut down on cost companies prefer outsourcing of call centre operations and complaint management process.

Process Steps for handling customer complaints

Customer logs complaint from various modes like call at the call centre, visit retailer/company showroom/ website site/ App/ Email on customer care/ Social media (Facebook / Twitter) All major operators in business give complete attention and alertness on each and every customer complaint and after customer complaint getting logged in the system , same flows to back end team through CRM workflows Dedicated Service recovery teams made available in backend or service agencies Each and every case got assigned to the backend/service team for a customer visit and complaint closure There are broadly two types of transactions for complaints (FTR) First-time resolution and (NFTR) Non-first time resolution. For FTR cases, front end team like a call centre or showroom executive do the required troubleshooting and give resolution to customer and case closed as per customer satisfaction In NFTR cases, backend operation team aligned and visit done at customer premises and closure done by rectifying hardware, product or Outdoor unit. Some operators give delight code/ Happy code to the customer on logging of NFTR complaints and same code need to be provided to the engineer if complaint got resolved as per customer satisfaction Major Challenges in handling customer complaints

During sudden technical failure or any natural calamity, there will be a high flow of complaints, which takes time to manage and close the complaint to customer expectation. These instances bring challenging time in telecom/ DTH operators as a customer not ready for any delay in resolution As per business requirements, there has been a lot of fresh hiring done for call centre advisors and a lot of efforts being put on their training but due to the initial learning curve, basic mistakes done by new hires leading to irrelevant and wrong complaints being raised in the system. This sometimes leads to delay in resolution and telecom/DTH operator undergo firefighting scenarios Managing social media errors is also one of the challenging tasks, sometimes operator’s reputation goes to stake due to small negligence of any employee or any process failure

content

This dataset consists of almost 2224 rows and 11 columns which belongs to all complaints can be raised by user

Acknowledgements

Reference : https://github.com/Kavyapriyakp/Telecome-Consumer-Complaints-Data-Analytics-PYTHON

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These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

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