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Internet of Things (IoT) Data 
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Psychology Data 
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- Data Categories
- Agriculture Data
- B2B Data
- Biometric Data
- Company Data
- Conservation Data
- Demographic Data
- Economic Data
- Environmental Data
- Geospatial Data
- Healthcare Data
- Individual Data
- Industry-Specific Data
- Internet of Things (IoT) Data
- Legal and IP Data
- People and Employment Data
- Political Data
- Psychology Data
- Real Estate Data
- Retail & Commerce Data
- Social Data
- Space Exploration Data
- Sports & Entertainment Data
- Web Data
What Is Bankruptcy Data?
Businesses declare bankruptcy to either eliminate or repay their debts under the guidance and protection of a bankruptcy court. As a result, businesses are either liquidated entirely reorganized. Bankruptcy data contains all public details about the legal status of a business as a result of its declared bankruptcy. This may include the business’s financial statements, debts, creditors, and more.
Where Does Bankruptcy Data Come From?
Bankruptcy information comes from a number of sources. The best source is, of course, a government database. Governments record accurate and reliable information but the data may not be complete. Therefore, your should also try other sources, such as one of the data vendors found on our site.
Media outlets are also good sources of this data: a business going bankrupt is become public information.
What Types of Columns/Attributes Should I Expect When Working with This Data?
A database that records information about entities that have just filed for bankruptcy will have the company name, the date of the bankruptcy declaration, and its current status. In contrast, though, a larger database that collects bankruptcy information over a period of years will have more detail, such as the specific bankruptcy procedure. Additional information may include the scope of the debts and the details of repayment, when applicable.
What Is This Data Used For?
Bankruptcy data dramatically affects the future of a company and all parties who are related to it. You can consult bankruptcy data before signing a business deal, for example, so you can make a fully informed decision about the risks of doing so.
You can also use the data to study the best practices for your venture. By learning from the mistakes of others, you can improve the chances of your own success.
How Should I Test the Quality of Bankruptcy Data?
The best way to check the quality of bankruptcy data is to consider the source. Government agencies, for example, generally provide good quality data—though they do not always provide it freely.
You should also ensure that the data is frequently and recently updated. The more up-to-date the information, the more meaningful decisions you can make.
Interesting Case Studies and Blogs to Look Into
BankruptcyData
Eurofund: Restructuring in bankruptcy: recent national case examples
Tangible Examples of Impact
Historically, bankruptcies have closely tracked the business cycle and contemporaneous unemployment rates. However, this relationship has reversed during the COVID-19 crisis thus far. While aggregate filing rates were very similar to 2019 levels prior to the severe onset of the pandemic, filings by consumers and small businesses dropped dramatically starting in mid-March, contrary to media reports and many experts’ expectations. The total number of bankruptcy filings is down by 27 percent year-over-year between January and August.
Micro-Lending Worldwide
Relevant datasets
Jinni Entertainment Genome
The Jinni Entertainment Genome powers Jinni’s entertainment recommendation engine and marketing campaign platform. The Genome tracks TV and movie content as well as viewer data, ratings, reviews, and much more; they can even apply up to sixty meta-tags to each content title.
In addition, Jinni provides movie ticket purchases, viewer scheduling data, and other information across devices. All this data goes on to drive their targeted marketing campaign platforms.
AthleteMonitoring Data
AthleteMonitoring Data primarily consists of internal data from user biometrics and questionnaire feedback. Users can also import data from other sources, such as EMR documents.
AthleteMonitoring also stays up-to-date on disease, injury prevention and management, and sport data and best practices.
PredictHQ Demand Intelligence
PredictHQ Demand Intelligence combines a company’s historical transaction data with external events data that impacts customer demand
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The Arabesque Group
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Black Box Intelligence Consumer Intelligence
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