The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey.
Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values.

Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Introducing Job Posting Datasets: Uncover labor market insights!

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Snapshot img

Subscriber Data Management Market Size 2024-2028

The subscriber data management market size is forecast to increase by USD 4.08 billion at a CAGR of 16.9% between 2023 and 2028.

The market is experiencing significant growth due to the increasing adoption of target advertisement-based streaming apps. This trend is driven by the rising demand for personalized content and services, which necessitates effective management of subscriber data. Furthermore, the proliferation of 5G technology is fueling the need for faster and more secure data processing and transmission. However, this market is not without challenges. Data privacy and security risks continue to pose a significant threat, with subscriber data being a valuable asset for cybercriminals. Companies must invest in security measures to protect sensitive information and maintain customer trust. Additionally, regulatory compliance and data interoperability across multiple platforms are other challenges that market participants must navigate to capitalize on the opportunities presented by this dynamic market. Overall, the market offers significant potential for growth, particularly for those players who can effectively address the evolving needs of subscribers and mitigate the risks associated with managing large volumes of sensitive data.

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

Request Free SampleThe market in the US is experiencing significant growth due to the increasing number of mobile subscriptions and the shift towards cloud-based solutions. Telecom operators are prioritizing network functions virtualization (NFV) and long-term evolution (LTE) technologies to enhance their mobile networks, leading to an escalating demand for user data repositories and policy management systems. Telecommunication network providers are also focusing on data security to mitigate cyberattacks and ensure data privacy policies are adhered to. Moreover, the proliferation of Voice over LTE (VoLTE) and Volte services, as well as the integration of subscriber data management systems in telecom service providers' customer relationship management (CRM) and identity management solutions, is driving market expansion. The market is expected to continue growing as 5G subscriptions increase, with hybrid solutions gaining popularity among network carriers to optimize their on-premise and cloud-based offerings. The market's size and direction reflect the industry's commitment to delivering secure, efficient, and innovative subscriber data management solutions to meet the evolving needs of mobile subscribers.

How is this Subscriber Data Management Industry segmented?

The subscriber data management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeMobile networksFixed networksGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanMiddle East and AfricaSouth America

By Type Insights

The mobile networks segment is estimated to witness significant growth during the forecast period.The Subscriber Data Management (SDM) market is driven by the mobile networks segment, which accounts for a substantial revenue share. This growth is attributed to the widespread use of mobile devices and the escalating demand for high-speed data services, particularly with the emergence of 5G technology. Mobile networks necessitate advanced SDM solutions to manage the voluminous subscriber data, ensuring uninterrupted service delivery and superior user experiences. The integration of 5G has intensified the need for sophisticated SDM systems due to the complexities introduced in data management, including real-time processing, authentication, and security. Cloud-based SDM solutions with cloud-native design are increasingly popular due to their flexibility, scalability, and ability to handle large volumes of data. Identity management, data integration, and policy management are crucial components of these solutions. The Internet of Things (IoT) and Voice Over IP (VoIP) are additional areas driving the market, as they generate substantial subscriber data that needs to be managed effectively. Data security and privacy are paramount concerns, necessitating the adoption of advanced security solutions and adherence to stringent data privacy policies. Network Carriers, Telecom Operators, and Communication Service Providers (CSPs) are key players in the market, leveraging SDM systems to manage their subscriber data and enhance network performance. Network Functions Virtualization (NFV) and Long-Term Evolution (LTE) are key technologies enabling the deployment of SDM solutions in a hybrid environment, ensuring seamless integration with fixed networks and mobile networks. The market is further fueled by the increas

According to Cognitive Market Research, the global Data Quality Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS of

Data Quality Software

The Emergence of Big Data and IoT drives the Market

The rise of big data analytics and Internet of Things (IoT) applications has significantly increased the volume and complexity of data that businesses need to manage. As more connected devices generate real-time data, the amount of information businesses handle grows exponentially. This surge in data requires organizations to ensure its accuracy, consistency, and relevance to prevent decision-making errors. For instance, in industries like healthcare, where real-time data from medical devices and patient monitoring systems is used for diagnostics and treatment decisions, inaccurate data can lead to critical errors. To address these challenges, organizations are increasingly investing in data quality software to manage large volumes of data from various sources. Companies like GE Healthcare use data quality software to ensure the integrity of data from connected medical devices, allowing for more accurate patient care and operational efficiency. The demand for these tools continues to rise as businesses realize the importance of maintaining clean, consistent, and reliable data for effective big data analytics and IoT applications. With the growing adoption of digital transformation strategies and the integration of advanced technologies, organizations are generating vast amounts of structured and unstructured data across various sectors. For instance, in the retail sector, companies are collecting data from customer interactions, online transactions, and social media channels. If not properly managed, this data can lead to inaccuracies, inconsistencies, and unreliable insights that can adversely affect decision-making. The proliferation of data highlights the need for robust data quality solutions to profile, cleanse, and validate data, ensuring its integrity and usability. Companies like Walmart and Amazon rely heavily on data quality software to manage vast datasets for personalized marketing, inventory management, and customer satisfaction. Without proper data management, these businesses risk making decisions based on faulty data, potentially leading to lost revenue or customer dissatisfaction. The increasing volumes of data and the need to ensure high-quality, reliable data across organizations are significant drivers behind the rising demand for data quality software, as it enables companies to stay competitive and make informed decisions.

Key Restraints to

Data Quality Software

Lack of Skilled Personnel and High Implementation Costs Hinders the market growth

The effective use of data quality software requires expertise in areas like data profiling, cleansing, standardization, and validation, as well as a deep understanding of the specific business needs and regulatory requirements. Unfortunately, many organizations struggle to find personnel with the right skill set, which limits their ability to implement and maximize the potential of these tools. For instance, in industries like finance or healthcare, where data quality is crucial for compliance and decision-making, the lack of skilled personnel can lead to inefficiencies in managing data and missed opportunities for improvement. In turn, organizations may fail to extract the full value from their data quality investments, resulting in poor data outcomes and suboptimal decision-ma...

Population Health (BRFSS: HRQOL)

Examining Trends, Disparities and Determinants of Health in the US Population

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.

The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!

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How to use the dataset

This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.

Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.

Research Ideas

• Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
• Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
• Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...

Snapshot img

Big Data As A Service Market Size 2025-2029

The big data as a service market size is forecast to increase by USD 75.71 billion, at a CAGR of 20.5% between 2024 and 2029.

The Big Data as a Service (BDaaS) market is experiencing significant growth, driven by the increasing volume of data being generated daily. This trend is further fueled by the rising popularity of big data in emerging technologies, such as blockchain, which requires massive amounts of data for optimal functionality. However, this market is not without challenges. Data privacy and security risks pose a significant obstacle, as the handling of large volumes of data increases the potential for breaches and cyberattacks. Edge computing solutions and on-premise data centers facilitate real-time data processing and analysis, while alerting systems and data validation rules maintain data quality.
Companies must navigate these challenges to effectively capitalize on the opportunities presented by the BDaaS market. By implementing robust data security measures and adhering to data privacy regulations, organizations can mitigate risks and build trust with their customers, ensuring long-term success in this dynamic market.

What will be the Size of the Big Data As A Service Market during the forecast period?

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

The market continues to evolve, offering a range of solutions that address various data management needs across industries. Hadoop ecosystem services play a crucial role in handling large volumes of data, while ETL process optimization ensures data quality metrics are met. Data transformation services and data pipeline automation streamline data workflows, enabling businesses to derive valuable insights from their data. Nosql database solutions and custom data solutions cater to unique data requirements, with Spark cluster management optimizing performance. Data security protocols, metadata management tools, and data encryption methods protect sensitive information. Cloud data storage, predictive modeling APIs, and real-time data ingestion facilitate agile data processing.
Data anonymization techniques and data governance frameworks ensure compliance with regulations. Machine learning algorithms, access control mechanisms, and data processing pipelines drive automation and efficiency. API integration services, scalable data infrastructure, and distributed computing platforms enable seamless data integration and processing. Data lineage tracking, high-velocity data streams, data visualization dashboards, and data lake formation provide actionable insights for informed decision-making.
For instance, a leading retailer leveraged data warehousing services and predictive modeling APIs to analyze customer buying patterns, resulting in a 15% increase in sales. This success story highlights the potential of big data solutions to drive business growth and innovation.

How is this Big Data As A Service Industry segmented?

The big data as a service 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

  Data Analytics-as-a-service (DAaaS)
  Hadoop-as-a-service (HaaS)
  Data-as-a-service (DaaS)


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


End-user

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The Data analytics-as-a-service (DAaas) segment is estimated to witness significant growth during the forecast period. The data analytics-as-a-service (DAaaS) segment experiences significant growth within the market. Currently, over 30% of businesses adopt cloud-based data analytics solutions, reflecting the increasing demand for flexible, cost-effective alternatives to traditional on-premises infrastructure. Furthermore, industry experts anticipate that the DAaaS market will expand by approximately 25% in the upcoming years. This market segment offers organizations of all sizes the opportunity to access advanced analytical tools without the need for substantial capital investment and operational overhead. DAaaS solutions encompass the entire data analytics process, from data ingestion and preparation to advanced modeling and visualization, on a subscription or pay-per-use basis. Data integration tools, data cataloging systems, self-service data discovery, and data version control enhance data accessibility and usability.

The continuous evolution of this market is driven by the increasing volume, variety, and velocity of data, as well as the growing recognition of the business value that can be derived from data insights. Organizations across var

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

Health-2-Market was a 3-year long Coordination and Support Action, funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration (Grant Agreement No 305532). H2M aimed at providing training and individual support to Health / Life Sciences researchers in the process of translating their research results into successful new business ideas. With a view to properly adapting the training offer of the project to the needs of Health / Life Sciences researchers in terms of entrepreneurship and business skill development a Training Needs Analysis (TNA) was conducted. In this context, H2M launched an online survey targeted at Health / Life Sciences researchers who have been involved in EU health projects. In particular, the objectives of the survey were: To formulate a descriptive understanding of various aspects of commercialisation and training needs of the main target group of the project; To divide this target group into homogeneous sub-groups (clusters) along a number of key characteristics such as demographics, commercialisation attitudes and needs; To understand preferences and importance of different aspects and needs through the analysis of: Knowledge areas that can influence commercialisation behaviour; Training modalities that have an effect on the intention to participate and /or on the perception of the usefulness of a commercialisation training; Variations identified over different sub-groups. The survey was dispatched to a database composed of 7,991 unique contacts of participants in previous health projects, accessed through the Directorate General for Health and Food Safety of the European Commission. The initial aim of at least 50 complete responses was overwhelmingly surpassed: 637 respondents completed the survey in full. The “H2M survey data on commercialisation training needs of Health Researchers” dataset contains the raw, anonymised data that were collected from these respondents, along with the questionnaire items that were utilised.

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

These data sets include numerically coded answers to Likert-like scale questions concerning the importance and perceived current research data management competencies of doctoral students. Interviewees were 35 doctoral students and faculty members. Interview forms are attached. The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684

This file contains the national totals for high needs place funding. Data is rounded to the nearest million.

This data collection consists of the SPSS syntax used to recode existing variables and create new variables from the SURVEY OF INMATES OF LOCAL JAILS, 1996 [ICPSR 6858] and the SURVEY OF INMATES IN STATE AND FEDERAL CORRECTIONAL FACILITIES, 1997 [ICPSR 2598]. Using the data from these two national surveys on jail and prison inmates, this study sought to expand the analyses of these data in order to fully explore the relationship between type and intensity of substance abuse and other health and social problems, analyze access to treatment and services, and make estimates of the need for different types of treatment services in correctional systems.

Data Product Readiness Scoring Market Outlook

According to our latest research, the global data product readiness scoring market size reached USD 1.18 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.4% from 2025 to 2033. This dynamic growth is primarily driven by the accelerating demand for data-driven decision-making across industries, the increasing complexity of data ecosystems, and the critical need for organizations to assess the maturity and usability of their data products before deployment. By 2033, the market is forecasted to attain a value of USD 5.15 billion, reflecting the pivotal role of data product readiness scoring in the evolving digital landscape.

The surge in digital transformation initiatives across enterprises globally is a key growth factor for the data product readiness scoring market. Organizations are increasingly leveraging advanced analytics, artificial intelligence, and machine learning to gain actionable insights from their data assets. However, the success of these initiatives is heavily contingent upon the quality, governance, and integration of data products. As a result, businesses are adopting readiness scoring solutions to systematically evaluate whether their data products meet established standards for quality, compliance, and usability. This trend is further amplified by the growing recognition that data-driven innovation hinges on the reliability and maturity of underlying data assets, thus propelling the adoption of readiness scoring frameworks.

Another significant driver is the rising regulatory scrutiny and compliance requirements in sectors such as BFSI, healthcare, and government. Strict mandates around data privacy, integrity, and traceability have compelled organizations to implement rigorous data governance practices. Data product readiness scoring tools enable these organizations to ensure that their data products are compliant with industry regulations before deployment, thereby reducing the risk of non-compliance penalties and reputational damage. This compliance-centric approach is particularly pronounced in regions such as North America and Europe, where regulatory landscapes are highly mature and constantly evolving, making readiness scoring an indispensable part of the data lifecycle.

The proliferation of cloud computing and the increasing adoption of hybrid and multi-cloud environments have also played a crucial role in market expansion. As organizations migrate their data assets to cloud platforms, the complexity of managing and integrating disparate data sources has grown exponentially. Data product readiness scoring solutions help organizations navigate this complexity by providing a standardized framework to assess data readiness across diverse environments. This capability not only accelerates the time-to-insight but also ensures that data products are scalable, interoperable, and aligned with business objectives, further fueling market growth.

Regionally, North America continues to dominate the data product readiness scoring market, accounting for the largest share in 2024. This leadership is attributed to the strong presence of technology giants, early adoption of advanced data management practices, and a highly regulated business environment. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increasing investments in data infrastructure, and the rising awareness of data quality and governance in developing economies. Europe remains a key market, characterized by stringent data protection regulations and a mature enterprise landscape, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions accelerate their digital transformation journeys.

Component Analysis

The data product readiness scoring market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses a wide array of platforms and tools designed to automate the assessment of data product maturity, quality, and compliance. These solutions leverage advanced algorithms, machine learning, and artificial intelligence to provide real-time insights into the readiness of data products for deployment. The increasing sophistication of these tools, coupled with their ability to integrate seamlessly with existing data management systems, has made software the dominant component

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

The Data Analysis Services market is experiencing robust growth, driven by the exponential increase in data volume and the rising demand for data-driven decision-making across various industries. The market, estimated at $150 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an impressive $450 billion by 2033. This expansion is fueled by several key factors, including the increasing adoption of cloud-based analytics platforms, the growing need for advanced analytics techniques like machine learning and AI, and the rising focus on data security and compliance. The market is segmented by service type (e.g., predictive analytics, descriptive analytics, prescriptive analytics), industry vertical (e.g., healthcare, finance, retail), and deployment model (cloud, on-premise). Key players like IBM, Accenture, Microsoft, and SAS Institute are investing heavily in research and development, expanding their service portfolios, and pursuing strategic partnerships to maintain their market leadership. The competitive landscape is characterized by both large established players and emerging niche providers offering specialized solutions. The market's growth trajectory is influenced by various trends, including the increasing adoption of big data technologies, the growing prevalence of self-service analytics tools empowering business users, and the rise of specialized data analysis service providers catering to specific industry needs. However, certain restraints, such as the lack of skilled data analysts, data security concerns, and the high cost of implementation and maintenance of advanced analytics solutions, could potentially hinder market growth. Addressing these challenges through investments in data literacy programs, enhanced security measures, and flexible pricing models will be crucial for sustaining the market's momentum and unlocking its full potential. Overall, the Data Analysis Services market presents a significant opportunity for companies offering innovative solutions and expertise in this rapidly evolving landscape.

The Data Catalog Market is projected to grow at a CAGR of around 23.3% during 2023-28. Leading Companies - lation Inc., Alteryx, Ataccama ONE, Cloudera, Inc., Collibra, Google, IBM, Informatica, Microsoft Corporation, and Oracle.

This operations dashboard shows historic and current data related to this performance measure.Please note that in 2022, due to strategic transformational changes, the Strategic Management and Diversity Office was reorganized into the Strategic Management and Innovation Office and the Office of Diversity, Equity and Inclusion.The performance measure dashboard is available at 2.25 Employee Work Related Needs. Data Dictionary