View LSEG's extensive Economic Data, including content that allows the analysis and monitoring of national economies with historical and real-time series.

Graph and download economic data for Gross Domestic Product: Implicit Price Deflator (GDPDEF) from Q1 1947 to Q2 2025 about implicit price deflator, headline figure, inflation, GDP, and USA.

1/ Historical labor force and jobs data revised. For details, see Hawaii DLIR http://www.hiwi.org/cgi/dataanalysis/?PAGEID=94 .
2/ Data from January 1999 have been revised and consist of domestic and international air arrivals. They are not comparable to Eastbound and Westbound series.
Source: Hawaii Department of Labor & Industrial Relations; Hawaii Department of Taxation; Hawaii Department of Business, Economic
Development and Tourism; county building departments; Honolulu Board of REALTORS® compiled by Harvey Shapiro, Title Guaranty of
Hawaii and Realtors® Association of Maui, Inc. Final tables compiled by Statistics and Data Support Branch, READ, DBEDT

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When examining the intricate relationship between economic conditions and purchasing decisions, the utilization of practice datasets can offer invaluable insights. This particular artificial dataset comprises three main components: a dimension table of ten companies, a fact table documenting purchases from these companies, and a set of data points regarding economic conditions. These elements are meticulously designed to mimic real-world scenarios, enabling analysts to dissect and understand how fluctuations in the economy can influence the purchasing behavior of different types of companies.

The dimension table serves as the foundation, listing ten distinct companies, each potentially operating in varied sectors. This diversity allows for a comprehensive analysis across a spectrum of industries, highlighting sector-specific sensitivities to economic changes. The fact table of purchases acts as a historical record, offering detailed insights into the buying patterns of these companies over time. Analysts can observe trends, frequencies, and the magnitude of purchases, correlating them with the economic conditions presented in the third component of the dataset.

The economic conditions data is pivotal, as it encompasses a variety of indicators that can affect purchasing decisions. These may include inflation rates, interest rates, GDP growth, unemployment rates, and consumer confidence indices, among others. By examining the interplay between these economic indicators and the purchasing data, analysts can identify patterns and causations. For instance, an increase in interest rates might lead to a decrease in capital-intensive purchases by companies wary of higher borrowing costs.

Through this dataset, researchers can employ statistical models and data analysis techniques to uncover how economic fluctuations impact corporate purchasing decisions. The findings can offer valuable lessons for businesses in terms of budgeting, financial planning, and risk management. Companies can use these insights to make informed decisions, adjusting their purchasing strategies in anticipation of or in response to economic conditions. This proactive approach can help businesses maintain stability during economic downturns and capitalize on opportunities during favorable economic times.

Ultimately, this practice dataset not only aids in academic and educational pursuits but also serves as a practical tool for business analysts, economists, and corporate strategists seeking to better navigate the complex dynamics of the economy and its effects on corporate purchasing behaviors.

Gross Value Added: Year to Date: sa: YoY: State of São Paulo: Industry data was reported at 0.934 % in Feb 2025. This records a decrease from the previous number of 1.345 % for Jan 2025. Gross Value Added: Year to Date: sa: YoY: State of São Paulo: Industry data is updated monthly, averaging 1.316 % from Jan 2003 (Median) to Feb 2025, with 266 observations. The data reached an all-time high of 19.883 % in Jan 2010 and a record low of -14.562 % in Jan 2009. Gross Value Added: Year to Date: sa: YoY: State of São Paulo: Industry data remains active status in CEIC and is reported by State System of Data Analysis Foundation. The data is categorized under Brazil Premium Database’s National Accounts – Table BR.AH022: SNA 2008: Gross Value Added: Southeast: São Paulo: State System of Data Analysis Foundation. [COVID-19-IMPACT]

Nighttime Lights Economic Indicators Market Outlook

According to our latest research, the Global Nighttime Lights Economic Indicators market size was valued at $2.1 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 15.2% during 2024–2033. One of the primary drivers fueling this remarkable growth is the increasing reliance on real-time, objective data for economic analysis and urban development, especially as satellite and remote sensing technologies become more accessible and sophisticated. Nighttime lights data, derived from satellite and aerial imagery, has emerged as a crucial proxy for economic activity, infrastructure development, and disaster response, empowering governments, financial institutions, and urban planners to make more informed decisions in an ever-evolving global landscape.

Regional Outlook

North America currently holds the largest share of the Nighttime Lights Economic Indicators market, accounting for approximately 38% of the global value in 2024. This dominance is attributed to the region’s mature technological infrastructure, strong investment in satellite and remote sensing capabilities, and a well-established ecosystem of data analytics firms. The United States, in particular, benefits from robust federal and state-level initiatives supporting geospatial data utilization for urban planning, economic forecasting, and disaster management. The presence of major space agencies and private satellite operators further enhances data availability and quality, enabling a wide spectrum of end-users, from government agencies to financial institutions, to leverage nighttime lights as a reliable economic indicator. Additionally, North America's advanced regulatory frameworks and public-private partnerships have fostered a climate ripe for innovation and early adoption of cutting-edge geospatial analytics solutions.

The Asia Pacific region is anticipated to be the fastest-growing market for Nighttime Lights Economic Indicators, with a projected CAGR of 18.7% from 2024 to 2033. This acceleration is driven by rapid urbanization, burgeoning smart city initiatives, and significant investments in satellite and remote sensing technologies across countries such as China, India, and Japan. Governments and urban planners in the region are increasingly leveraging nighttime lights data to address challenges related to infrastructure development, population migration, and environmental monitoring. The proliferation of low-cost satellite launches and the expansion of national space programs have democratized access to high-resolution imagery, while regional collaborations and public-private partnerships are catalyzing the integration of geospatial analytics into mainstream economic planning. Furthermore, the Asia Pacific’s growing research community and technology startups are contributing to the development of innovative applications, further propelling market growth.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing Nighttime Lights Economic Indicators, although adoption is tempered by challenges such as limited technical expertise, data accessibility issues, and inconsistent regulatory support. Nevertheless, there is a growing recognition of the value that satellite-derived economic indicators can bring to addressing localized challenges such as informal settlements, disaster response, and resource allocation. In Africa, for instance, nighttime lights data is increasingly used to monitor electrification progress and urban expansion. Latin American countries are leveraging such indicators for disaster management and urban planning, particularly in regions prone to natural calamities. While these regions currently account for a smaller share of the global market, targeted policy reforms, international collaborations, and investments in capacity building are expected to accelerate adoption, bridging the gap between developed and developing markets.

Report Scope

This filtered view presents Real Gross Domestic Product for the real estate and rental and leasing sector and its subsectors in the State of Iowa by year beginning in 1997.

Gross domestic product (GDP) is the measure of the market value of all final goods and services produced within Iowa in a particular period of time. In concept, an industry's GDP by state, referred to as its "value added", is equivalent to its gross output (sales or receipts and other operating income, commodity taxes, and inventory change) minus its intermediate inputs (consumption of goods and services purchased from other U.S. industries or imported). The Iowa GDP a state counterpart to the Nation's GDP, the Bureau's featured and most comprehensive measure of U.S. economic activity. Iowa GDP differs from national GDP for the following reasons: Iowa GDP excludes and national GDP includes the compensation of federal civilian and military personnel stationed abroad and government consumption of fixed capital for military structures located abroad and for military equipment, except office equipment; and Iowa GDP and national GDP have different revision schedules. GDP is reported in millions of current dollars.

Real GDP is an inflation-adjusted measure of Iowa's gross product that is based on national prices for the goods and services produced within Iowa. The real estimates of gross domestic product (GDP) are measured in millions of chained dollars, but have been multiplied by 1,000,000 to display in dollars for visualization purposes. Values are only accurate to the nearest $100,000.

Graph and download economic data for Shares of gross domestic product: Personal consumption expenditures (DPCERE1Q156NBEA) from Q1 1947 to Q2 2025 about Shares of GDP, PCE, consumption expenditures, consumption, personal, GDP, and USA.

Brazil Gross Value Added Index: 12 Mos: YoY: State of São Paulo: Services: Others data was reported at 2.381 % in Sep 2022. This records a decrease from the previous number of 3.111 % for Jun 2022. Brazil Gross Value Added Index: 12 Mos: YoY: State of São Paulo: Services: Others data is updated quarterly, averaging 3.689 % from Dec 2003 to Sep 2022, with 76 observations. The data reached an all-time high of 7.108 % in Dec 2007 and a record low of -1.847 % in Dec 2015. Brazil Gross Value Added Index: 12 Mos: YoY: State of São Paulo: Services: Others data remains active status in CEIC and is reported by State System of Data Analysis Foundation. The data is categorized under Brazil Premium Database’s National Accounts – Table BR.AH023: SNA 2008: Gross Value Added: Southeast: São Paulo: State System of Data Analysis Foundation: Quarterly. [COVID-19-IMPACT]

Snapshot img

Alternative Data Market Size 2025-2029

The alternative data market size is valued to increase USD 60.32 billion, at a CAGR of 52.5% from 2024 to 2029. Increased availability and diversity of data sources will drive the alternative data market.

Major Market Trends & Insights

North America dominated the market and accounted for a 56% growth during the forecast period.
By Type - Credit and debit card transactions segment was valued at USD 228.40 billion in 2023
By End-user - BFSI segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 6.00 million
Market Future Opportunities: USD 60318.00 million
CAGR from 2024 to 2029 : 52.5%

Market Summary

The market represents a dynamic and rapidly expanding landscape, driven by the increasing availability and diversity of data sources. With the rise of alternative data-driven investment strategies, businesses and investors are increasingly relying on non-traditional data to gain a competitive edge. Core technologies, such as machine learning and natural language processing, are transforming the way alternative data is collected, analyzed, and utilized. Despite its potential, the market faces challenges related to data quality and standardization. According to a recent study, alternative data accounts for only 10% of the total data used in financial services, yet 45% of firms surveyed reported issues with data quality.
Service types, including data providers, data aggregators, and data analytics firms, are addressing these challenges by offering solutions to ensure data accuracy and reliability. Regional mentions, such as North America and Europe, are leading the adoption of alternative data, with Europe projected to grow at a significant rate due to increasing regulatory support for alternative data usage. The market's continuous evolution is influenced by various factors, including technological advancements, changing regulations, and emerging trends in data usage.

What will be the Size of the Alternative Data Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Alternative Data Market Segmented ?

The alternative data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Credit and debit card transactions
  Social media
  Mobile application usage
  Web scrapped data
  Others


End-user

  BFSI
  IT and telecommunication
  Retail
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.

Alternative data derived from credit and debit card transactions plays a significant role in offering valuable insights for market analysts, financial institutions, and businesses. This data category is segmented into credit card and debit card transactions. Credit card transactions serve as a rich source of information on consumers' discretionary spending, revealing their luxury spending tendencies and credit management skills. Debit card transactions, on the other hand, shed light on essential spending habits, budgeting strategies, and daily expenses, providing insights into consumers' practical needs and lifestyle choices. Market analysts and financial institutions utilize this data to enhance their strategies and customer experiences.

Natural language processing (NLP) and sentiment analysis tools help extract valuable insights from this data. Anomaly detection systems enable the identification of unusual spending patterns, while data validation techniques ensure data accuracy. Risk management frameworks and hypothesis testing methods are employed to assess potential risks and opportunities. Data visualization dashboards and machine learning models facilitate data exploration and trend analysis. Data quality metrics and signal processing methods ensure data reliability and accuracy. Data governance policies and real-time data streams enable timely access to data. Time series forecasting, clustering techniques, and high-frequency data analysis provide insights into trends and patterns.

Model training datasets and model evaluation metrics are essential for model development and performance assessment. Data security protocols are crucial to protect sensitive financial information. Economic indicators and compliance regulations play a role in the context of this market. Unstructured data analysis, data cleansing pipelines, and statistical significance are essential for deriving meaningful insights from this data. New

This dataset simulates real-time data collection and decision-making scenarios where edge computing and Internet of Things (IoT) technologies are used to optimize resource allocation, reduce latency, and support adaptive decision-making processes in economic management.

The dataset includes multiple attributes that reflect key performance metrics from IoT devices deployed in a network. These metrics, such as transaction volume, market behavior indices, and financial indicators, are processed using edge computing systems to enable fast and accurate decision-making. Additionally, the dataset features resource utilization and workload distribution efficiencies at the edge, which are critical for improving the performance of economic systems in real-time.

Key features of the dataset:

Real-Time Data Analysis: Simulates the use of IoT devices and edge computing to collect, process, and analyze economic data. Decision Accuracy: Includes a target variable (decision_outcome) that reflects the effectiveness of decisions made based on the collected data. Performance Metrics: Includes edge computing and system performance metrics such as latency, throughput, and resource utilization, which are essential for effective economic decision-making.

Graph and download economic data for Trade Balance: Goods and Services, Balance of Payments Basis (BOPGSTB) from Jan 1992 to Jul 2025 about BOP, balance, headline figure, trade, services, goods, and USA.

Graph and download economic data for Federal government current tax receipts: Taxes on production and imports: Customs duties (B235RC1Q027SBEA) from Q1 1959 to Q2 2025 about receipts, imports, tax, federal, production, government, GDP, and USA.

This summary view provides quarterly per capita personal income estimates for State of Iowa produced by the U.S. Bureau of Economic Analysis .

Personal income is defined as the sum of wages and salaries, supplements to wages and salaries, proprietors’ income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Personal income for Iowa is the income received by, or on behalf of all persons residing in Iowa, regardless of the duration of residence, except for foreign nationals employed by their home governments in Iowa. Per capita personal income is personal income divided by the Census Bureau’s midquarter population estimates.

This filtered view presents Real Gross Domestic Product for the accommodation and food services sector and its subsectors in the State of Iowa by year beginning in 1997.

Gross domestic product (GDP) is the measure of the market value of all final goods and services produced within Iowa in a particular period of time. In concept, an industry's GDP by state, referred to as its "value added", is equivalent to its gross output (sales or receipts and other operating income, commodity taxes, and inventory change) minus its intermediate inputs (consumption of goods and services purchased from other U.S. industries or imported). The Iowa GDP a state counterpart to the Nation's GDP, the Bureau's featured and most comprehensive measure of U.S. economic activity. Iowa GDP differs from national GDP for the following reasons: Iowa GDP excludes and national GDP includes the compensation of federal civilian and military personnel stationed abroad and government consumption of fixed capital for military structures located abroad and for military equipment, except office equipment; and Iowa GDP and national GDP have different revision schedules. GDP is reported in millions of current dollars.

Real GDP is an inflation-adjusted measure of Iowa's gross product that is based on national prices for the goods and services produced within Iowa. The real estimates of gross domestic product (GDP) are measured in millions of chained dollars, but have been multiplied by 1,000,000 to display in dollars for visualization purposes. Values are only accurate to the nearest $100,000.

This filtered view provides annual estimates developed by the U.S. Bureau of Economic Analysis on consumer spending in the State of Iowa beginning in 1998 for goods. Personal consumption expenditures (PCE) is the value of the services purchased by, or on the behalf of, Iowa residents. PCE is divided by the Census Bureau’s annual midyear (July 1) population estimates to calculate per capita PCE.

Services include household consumption expenditures (for services) and final consumption expenditures of nonprofit institutions serving households (NPISHs). Household consumption expenditures include: housing and utilities, health care, transportation services, recreation services, food services and accommodations, financial services and insurance, and other services. NPISH is the gross output of nonprofit institutions less receipts from sales of goods and services by nonprofit institutions.

This dataset provides quarterly personal income estimates for State of Iowa produced by the U.S. Bureau of Economic Analysis . Data includes the following estimates: personal income, per capita personal income, proprietors' income, farm proprietors' income, compensation of employees and private nonfarm earnings, compensation, and wages and salaries for wholesale trade. Personal income, proprietors' income, and farm proprietors' income available beginning 1997; per capita personal income available beginning 2010; and all other data beginning 1998.

Personal income is defined as the sum of wages and salaries, supplements to wages and salaries, proprietors’ income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Personal income for Iowa is the income received by, or on behalf of all persons residing in Iowa, regardless of the duration of residence, except for foreign nationals employed by their home governments in Iowa. Per capita personal income is personal income divided by the Census Bureau’s midquarter population estimates.

Proprietors' income is the current-production income (including income in kind) of sole proprietorships, partnerships, and tax-exempt cooperatives. Corporate directors' fees are included in proprietors' income. Proprietors' income includes the interest income received by financial partnerships and the net rental real estate income of those partnerships primarily engaged in the real estate business.

Farm proprietors’ income as measured for personal income reflects returns from current production; it does not measure current cash flows. Sales out of inventories are included in current gross farm income, but they are excluded from net farm income because they represent income from a previous year’s production.

Compensation to employees is the total remuneration, both monetary and in kind, payable by employers to employees in return for their work during the period. It consists of wages and salaries and of supplements to wages and salaries. Compensation is presented on an accrual basis - that is, it reflects compensation liabilities incurred by the employer in a given period regardless of when the compensation is actually received by the employee.

Private nonfarm earnings is the sum of wages and salaries, supplements to wages and salaries, and nonfarm proprietors' income, excluding farm and government.

Private nonfarm wages and salaries is wages and salaries excluding farm and government. Wages and salaries is the remuneration receivable by employees (including corporate officers) from employers for the provision of labor services. It includes commissions, tips, and bonuses; employee gains from exercising stock options; and pay-in-kind. Judicial fees paid to jurors and witnesses are classified as wages and salaries. Wages and salaries are measured before deductions, such as social security contributions, union dues, and voluntary employee contributions to defined contribution pension plans.

More terms and definitions are available on https://apps.bea.gov/regional/definitions/.

Graph and download economic data for Government Consumption Expenditures and Gross Investment (GCE) from Q1 1947 to Q2 2025 about investment, gross, consumption expenditures, consumption, government, GDP, and USA.

Graph and download economic data for FOMC Summary of Economic Projections for the Personal Consumption Expenditures Inflation Rate, Central Tendency, Midpoint (PCECTPICTM) from 2025 to 2028 about projection, PCE, consumption expenditures, consumption, personal, inflation, rate, and USA.

Gross Value Added Index: Year to Date: YoY: State of São Paulo: Taxes Net of Subsidies data was reported at 4.980 % in Dec 2024. This records a decrease from the previous number of 5.479 % for Sep 2024. Gross Value Added Index: Year to Date: YoY: State of São Paulo: Taxes Net of Subsidies data is updated quarterly, averaging 2.868 % from Mar 2003 (Median) to Dec 2024, with 88 observations. The data reached an all-time high of 17.666 % in Jun 2021 and a record low of -10.225 % in Mar 2003. Gross Value Added Index: Year to Date: YoY: State of São Paulo: Taxes Net of Subsidies data remains active status in CEIC and is reported by State System of Data Analysis Foundation. The data is categorized under Brazil Premium Database’s National Accounts – Table BR.AH023: SNA 2008: Gross Value Added: Southeast: São Paulo: State System of Data Analysis Foundation: Quarterly. [COVID-19-IMPACT]