The global data collector market is experiencing robust growth, driven by increasing automation across diverse sectors and the escalating demand for real-time data analysis. This market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated value of $25 billion by 2033. Key drivers include the expanding adoption of data analytics in precision agriculture, the rising prevalence of IoT devices generating massive datasets in industrial settings, and the growing need for advanced security systems relying on real-time data capture. The market is segmented by data collector type (portable and desktop) and application (agriculture, healthcare, security, industrial, communication, and others). The portable segment holds a significant market share due to its flexibility and ease of use in diverse field applications. North America and Europe currently dominate the market, but the Asia-Pacific region is poised for substantial growth fueled by increasing industrialization and technological advancements. However, factors such as high initial investment costs for advanced data collection systems and the need for skilled professionals to operate and interpret the data could act as market restraints. The competitive landscape features a mix of established technology giants like Microsoft and IBM alongside specialized data collector manufacturers like LUDECA, Inc., and PANalytical. These companies are actively engaged in research and development, focusing on improving data accuracy, speed, and integration capabilities. The increasing convergence of data collection with cloud computing and artificial intelligence is further shaping the market, creating opportunities for innovative solutions that enhance data analysis and decision-making across sectors. The market's future trajectory is closely tied to technological advancements in sensor technology, data storage, and communication networks, promising continued expansion and innovation throughout the forecast period.

This is a national data collection of data resources managed by the Bureau of Ocean Energy Management (BOEM) for the Outer Continental Shelf (OCS). The data collection is designated as a National Geospatial Data Asset (NGDA) and includes: OCS BOEM Offshore Boundary Lines (Submerged Lands Act Boundary, OCSLA Limit of “8(g) Zone,” and Continental Shelf Boundary), OCS Protraction Polygons - 1st Division, OCS Gulf of Mexico NAD27 Protraction Polygons - 1st Division, OCS Block Polygons - 2nd Division, OCS Gulf of Mexico NAD27 Block Polygons - 2nd Division, and Aliquot 16ths Polygons - 3rd Division.All polygons are clipped to the Submerged Land Act Boundary and Continental Shelf Boundaries reflecting federal jurisdiction. The NAD27 Gulf of Mexico Protractions and Blocks have a different protraction and block configuration when compared to the OCS Protraction Polygons - 1st Division and OCS Block Polygons - 2nd Division. The NAD27 Gulf of Mexico data is used for Oil and Gas leasing.These data were created in the applicable NAD83 UTM or NAD27 UTM/SPCS Projection and re-projected to GCS WGS84 (EPSG 4326) for management in BOEM"s enterprise GIS. However, the services in this collection have been published in WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857). Because GIS projection and topology functions can change or generalize coordinates,these data are NOT an OFFICIAL record for the exact boundaries. These data are to be used for Cartographic purposes only and should not be used to calculate area.Layers MetadataOCS BOEM Offshore Boundary LinesOCS Protraction Polygons - 1st DivisionOCS Gulf of Mexico NAD27 Protraction Polygons - 1st DivisionOCS Block Polygons - 2nd DivisionOCS Gulf of Mexico NAD27 Block Polygons - 2nd DivisionAliquot 16ths Polygons - 3rd Division

Abstract

Protracted and new displacements of large numbers of people as well as complex conflict dynamics continue to be a major issue in Darfur. In 2020, an estimated 2.5 million people were internally displaced and close to 400,000 Darfuris refugees resided in neighbouring countries. The political transition following years of conflict paved the way for the signing of the Juba Peace Agreement (JPA) in 2020. The peace agreement aims to address the root causes of conflict but also establishes durable solutions for displaced populations as a necessity for lasting peace in Darfur. In 2021, the Government furthermore initiated work on a National Strategy on Solutions, which will offer a critical strategic framework and operational roadmap towards solutions for displaced communities in Sudan.

In 2017, the Government of Sudan (GoS) and the international community agreed on the need to collectively support Durable Solutions for IDPs, returnees, and their host communities to end the situation of protracted displacement. The collaboration on Durable Solutions between the GoS and international community resulted in two Durable Solution pilots in respectively El Fasher (North Darfur) and Um Dukhun (Central Darfur). JIPS provided technical support for the scale-up of the durable solutions analysis across Darfur under the Central Emergency Relief Fund (CERF).

Focusing on nine localities, including urban areas, the data collection exercises build directly on the durable solutions analysis approach piloted in El Fasher in 2019. The Durable Solutions Working Group (DSWG) identified a joint evidence base and a collaborative approach as priorities and therefore undertook a joint area-based profiling exercise, focusing on the Abu Shouk and El Salaam IDP camps on the outskirts of El Fasher.

The focus was set on profiling of IDPs (in camp settlements and out of camps), IDP returnees, refugee returnees, and non-displaced. The profiling exercises are aimed at: i.Informing CERF programming and Action Plan development in each state/locality; ii.Provide the baseline of the agreed upon CERF outcome/output indicators (for later measurement of impact); and iii.Inform broader UNHCR programming beyond the Fund.

Geographic coverage

Kaas locality within South Darfur State. Considering the difference in the geographic context (urban vs. rural) within Kass, it was agreed to divide Kass into two separate clusters (urban and rural) and treat them as separate entities. Hence, each cluster is considered as a locality within Kass

Analysis unit

Households

Universe

All IDP returnees, refugee returnees, IDPs in camps and out of camps, and non-displaced populations across Kaas.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling followed a systematic simple random approach, through which the households were treated as the primary sampling unit. The sample size for each target group was identified proportionately based on the group's population size. The sampling is designed to produce results representative for each target group in the targeted area of the locality. Analysis at the settlement level is not possible. The selection of settlements included in each locality is based on a prioritization by partner agencies and local partners based on the programmatic scope of the CERF. The data is thus not representative of whole locality, but the specific geographic scope targeted within the locality.

The total sample included: 792 households (HHs), including IDPs in camps and the town (394 HHs), and non-displaced (398 HHs). In Kass rural cluster, the total achieved sample sizes included: 343 IDPs households residing outside of camps and 543 IDP-returnee households. Additionally, 66 non-displaced households and 50 return refugee households were captured but excluded from the analysis due to the small sample sizes.

The sample frame of the household survey was based on the population estimates of each target group, that were provided by key informants and validated through fieldwork missions.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Some households with over 14 members have had individuals removed from their household roster due to anonymization techniques.

Graph and download economic data for Total Revenue for Collection Agencies, All Establishments, Employer Firms (REVEF56144ALLEST) from 1998 to 2022 about collection, agency, employer firms, accounting, revenue, establishments, services, and USA.

Snapshot img

Debt Collection Software Market Size 2024-2028

The debt collection software market size is forecast to increase by USD 2.31 billion at a CAGR of 8.92% between 2023 and 2028.

The market is experiencing significant growth due to the increasing number of overdue invoices in various industries, particularly in financing and banking organizations. Advanced technologies, such as automated overdue invoice reminders and phone call systems, are being integrated into software to enhance productivity and streamline the money collection process. However, the high cost remains a challenge for smaller organizations. The integration of technology in collection journeys is becoming increasingly important as financial institutions seek to improve their debt recovery processes and reduce the amount of non-performing loans (NPLs) on their books. This market analysis report provides an in-depth examination of market growth factors, including the integration of advanced technologies and the rising need for efficient debt collection and settlement solutions.

What will be the Size of the Debt Collection Software Market During the Forecast Period?

Request Free Sample

The market is a significant segment within the larger financial technology landscape. This market caters to the business-specific needs of organizations seeking to streamline their loan recovery process and improve productivity in debt collection. Deploying modern software solutions can offer economies of scale, enabling organizations to manage their ecosystem more effectively. By implementing these systems, businesses can overcome the complexities associated with managing overdue invoices, borrower data, and online payment collection. Multichannel communication is a crucial aspect, and modern software solutions support various channels, including phone calls, emails, and text messages. This flexibility allows organizations to engage with borrowers through their preferred mode of communication, enhancing the effectiveness of debt recovery efforts. Two primary segments within the market are services and solutions. This services involve outsourcing the entire debt recovery process to third-party providers, while these solutions enable organizations to manage their debt collection in-house.
Whereas, when organizations consider deploying the software, they must weigh the affordability of the solution against the potential return on investment. Training and implementation work are essential considerations, as is ensuring the software integrates with existing core business systems, such as accounting and CRM platforms. Legacy systems can pose challenges during the implementation of new debt collection software. However, with proper planning and execution, these challenges can be mitigated, allowing organizations to reap the benefits of modern applications. Banks and financial organizations are significant users of debt collection software. Effective debt recovery is essential for their core business operations, and these institutions can leverage debt collection software to streamline their loan recovery process, reduce delinquencies, and improve overall productivity. In summary, the market offers organizations a valuable tool for managing their debt collection ecosystem. By addressing the complexities of debt recovery through modern software solutions, businesses can improve their productivity, enhance borrower engagement, and ultimately, more effectively collect outstanding debts.

How is this Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud-based


Industry Application

  Small and medium enterprises
  Large enterprises


Software Component

  Software
  Service


Geography

  North America

    US


  Europe

    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premises software solutions hold a substantial position In the market In the United States, addressing the demands of businesses that value internal control, data security, and customization. These solutions are installed and operated within an organization's premises, granting users extensive autonomy over their processes. Large corporations, particularly those in regulated sectors like finance and healthcare, frequently opt for on-premises software to maintain tight oversight of confidential debtor information and adhere to stringent data protection regulations. For instance, companies such as InterProse Corporation provide on-premises solutions that enable organizations to automa

Established to gain a better understanding of the current situation with regard to NHS wheelchair services in England and to support commissioners and providers to improve services.

Abstract

This survey was conducted in Slovak Republic between January 2013 and March 2014 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS V), a joint initiative of the World Bank Group and the European Bank for Reconstruction and Development. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Data from 276 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The survey topics include firm characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement collaboration, security, government policies, laws and regulations, financing, overall business environment, bribery, capacity utilization, performance and investment activities, and workforce composition.

In 2011, the innovation module was added to the standard set of Enterprise Surveys questionnaires to examine in detail how introduction of new products and practices influence firms' performance and management.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not common practice, apart from the construction and agriculture sectors which are not included in the survey.

Regional stratification was defined in four regions (city and the surrounding business area) throughout Slovak Republic.

The database "Albertina Company Monitor" was used as the frame for the selection of a sample with the aim of obtaining interviews at 270 establishments with five or more employees.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 1.9 % (31 out of 1,613 establishments).

In the dataset, the variables a2 (sampling region), a6a (sampling establishment's size), and a4a (sampling sector) contain the establishment's classification into the strata chosen for each country using information from the sample frame. Variable a4a is coded using ISIC Rev 3.1 codes for the chosen industries for stratification. These codes include most manufacturing industries (15 to 37), retail (52), and (45, 50, 51, 55, 60-64, 72) for other services.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different versions of the questionnaire were used. The basic questionnaire, the Core Module, includes all common questions asked to all establishments from all sectors. The second expanded variation, the Manufacturing Questionnaire, is built upon the Core Module and adds some specific questions relevant to manufacturing sectors. The third expanded variation, the Retail Questionnaire, is also built upon the Core Module and adds to the core specific questions.

The innovation module was added to the standard set of Enterprise Surveys questionnaires to examine how introduction of new products and practices influence firms' performance and management.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether, while the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of contacted establishments per realized interview was 0.14. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.56.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

Delineation of Australia's domestic and international maritime limits and boundaries. These boundaries include a variety of jurisdictional, economic, regulatory and legal boundaries.

Value: Used by national governments, businesses, organisations in determining boundaries for zones governed/managed by different regulatory structures/requirements.

Scope: A national dataset at resolution relevant for presentation of regional spatial data such as digital maps or regional decision making.

This dataset contains information about NYC Business Solutions service, a service offered by the Department of Small Business Services (SBS) aimed at giving New Yorkers free services to start, operate and grow their businesses. Each row in the dataset represents the number of public housing residents on a Borough-level who receive or utilize this service. For datasets related to other services provided to NYCHA residents, view the data collection “Services available to NYCHA Residents - Local Law 163”.

The open-source big data tools market is experiencing robust growth, driven by the increasing need for scalable, cost-effective data management and analysis solutions across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the rising volume and velocity of data generated across industries, from banking and finance to manufacturing and government, necessitate powerful and adaptable tools. Secondly, the cost-effectiveness and flexibility of open-source solutions compared to proprietary alternatives are major drawcards, especially for smaller organizations and startups. The ease of customization and community support further enhance their appeal. Growth is also being propelled by technological advancements such as the development of more sophisticated data analytics tools, improved cloud integration, and increased adoption of containerization technologies like Docker and Kubernetes for deployment and management. The market's segmentation across application (banking, manufacturing, etc.) and tool type (data collection, storage, analysis) reflects the diverse range of uses and specialized tools available. Key restraints to market growth include the complexity associated with implementing and managing open-source solutions, requiring skilled personnel and ongoing maintenance. Security concerns and the need for robust data governance frameworks also pose challenges. However, the growing maturity of the open-source ecosystem, coupled with the emergence of managed services providers offering support and expertise, is mitigating these limitations. The continued advancements in artificial intelligence (AI) and machine learning (ML) are further integrating with open-source big data tools, creating synergistic opportunities for growth in predictive analytics and advanced data processing. This integration, alongside the ever-increasing volume of data needing analysis, will undoubtedly drive continued market expansion over the forecast period.

Abstract

The survey was conducted in Guinea between July - December 2016 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 150 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

Conakry

Analysis unit

The primary sampling unit of the study is an establishment. The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries (ISIC Rev. 3.1 codes 15-37) and services industries (ISIC codes 45, 50, 51, 52, 55, 60-64, and 72).

For the Guinea ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Guinea ES was done in Conakry.

The sample frame consisted of listings of firms from Banque Centrale de la République de Guinée Conakry.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 8.9% (34 out of 383 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

Questionnaires have common questions (core module) and respectfully additional manufacturing and services specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.39. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.20.

Abstract

The survey was conducted in the Dominican Republic between August 2016 and April 2017 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 359 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15- 37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

Size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Dominican Republic ES was done across three regions: Santo Domingo, Santiago-Puerto Plata-Espaillat, and the rest of the country.

The sample frame consisted of listings of firms from these sources: - a list of 360 firms from the Dominican Republic 2010 ES for panel firms - a list of firms obtained from El Directorio de Empresas y Establecimientos (DEE) 2015 and Oficina Nacional de Estadística (ONE) for firms not covered in 2010.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them. Due to response rate and ineligibility issues, additional sample had to be extracted by the World Bank in order to obtain enough eligible contacts and meet the sample targets.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 23.9% (504 out of 2,112 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions).

Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Each variation of the questionnaire is identified by the index variable, a0.

The last complete fiscal year is January to December 2015. For questions pertaining to monetary amounts, the unit is Dominican Peso.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.46. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.17.

This is a collection of continuous seismic records gathered by temporal and semi-permanent seismic deployments where real-time data transmission was not available. Time spans vary from half an hour to more than a year depending on the purpose of the survey. Description of the employed instrumentation and array constellations can be found in the accompanied material.

Value: Passive seismic data contains records of soil vibration due to the natural earth movements, ocean, weather, and anthropogenic activities. This data is used in ongoing research to infer national lithospheric structure from depth of a few meters to a hundred kilometres. Derived models are an important source of information for assessment of resource potential and natural hazard.

Scope: Over time, surveys have been focused on areas of economic interest, current work of the Australian Passive Seismic Array Project (AusArray) is seeking to create a grid pattern, spaced ~55 km apart, and complemented by semi-permanent higher sensitivity broadband seismic stations.

For more information about AusArray click on the following URL: https://www.ga.gov.au/eftf/minerals/nawa/ausarray

Data from phase 1 are available on request from clientservices@ga.gov.au - Quote eCat# 135284

The global Debt Collection Software and Service market is anticipated to expand at a CAGR of XX% from 2025 to 2033, reaching a valuation of XXX million by 2033. The growing prevalence of unpaid debts, exacerbated by the economic downturn caused by the COVID-19 pandemic, is a primary driver of market expansion. Furthermore, technological advancements, such as the adoption of artificial intelligence (AI) and machine learning (ML), are enhancing the efficiency and accuracy of debt collection processes, further propelling market growth. Factors such as stringent regulations and data privacy concerns related to debt collection practices present some restraints to market growth. However, the increasing adoption of cloud-based debt collection solutions, which offer improved accessibility and scalability, is expected to bolster the industry's growth. The market is segmented based on type (software and services) and application (healthcare, student loans, financial services, government, retail, telecom & utility, mortgage, and others). The software segment accounts for the larger market share due to the comprehensive functionality it provides for managing debt collection processes. North America holds the largest regional market share, followed by Europe and Asia Pacific. The market is characterized by a competitive landscape with established players such as Experian, FIS, CGI, and TransUnion, along with a growing number of startups and niche solution providers. The global debt collection software and service market size was valued at USD 1.9 billion in 2022 and is projected to grow from USD 2.1 billion in 2023 to USD 3.6 billion by 2030, exhibiting a CAGR of 7.4% during the forecast period.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Snapshot img

Drilling Data Management Systems Market Size 2025-2029

The drilling data management systems market size is forecast to increase by USD 17.89 billion, at a CAGR of 10.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of big data analytics in the oil and gas industry. With the vast amounts of data generated during drilling operations, drilling data management systems are becoming essential for improving productivity and transparency. This trend is particularly pronounced in regions with high drilling activity, such as North America and the Middle East. However, the market growth is not without challenges. Fluctuations in crude oil prices continue to impact the market, as drilling activities can be scaled back during price downturns. Additionally, the integration of drilling data management systems with other operational systems and data sources can be complex, requiring substantial investment in technology and personnel.
Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on developing user-friendly solutions that can seamlessly integrate with existing systems and provide real-time data analysis capabilities. By doing so, they can help operators make informed decisions, optimize drilling operations, and reduce costs. Overall, the market presents significant opportunities for growth, particularly as the industry continues to embrace digitalization and data-driven decision-making.

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

Request Free Sample

The market in the oil and gas industry is experiencing significant growth due to the increasing importance of asset optimization and well control in maximizing production rates from conventional reservoirs and economically viable shale formations. Advanced drill planning and engineering systems, geoscience systems, and database administration software are integral components of drilling data management systems, enabling real-time data collection, analysis, and reporting for drilling management teams. Multilateral wells and complex drilling operations necessitate strong data management systems to ensure efficient wellbore location tracking and production rate monitoring.
Cybersecurity concerns are also driving demand for advanced data management solutions to protect sensitive drilling data transmitted wirelessly from oil rigs. The market is further driven by the integration of drilling data management systems with engineering and production asset teams, enabling collaborative decision-making and improved drilling performance. Gas hydrates and other geological challenges pose technical complexities that require sophisticated data management systems to ensure safe and efficient drilling operations. Overall, the market is expected to continue its growth trajectory, driven by the need for data-driven drilling strategies and the increasing complexity of drilling operations.

How is this Drilling Data Management Systems Industry segmented?

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

Component

  Services
  Software
  Hardware


Application

  Oil and gas
  Energy and power


Geography

  North America

    US
    Canada
    Mexico


  Middle East and Africa

    UAE


  Europe

    Norway
    Russia
    UK


  APAC

    Australia


  South America

    Brazil

By Component Insights

The services segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth due to the increasing importance of data-handling efficiency in various industries. With the generation of vast data volumes, there is a heightened demand for flexible, scalable, and effective data management solutions. The services segment within this market encompasses activities such as sensor installation, equipment maintenance, consultation, and data analytics, all applied throughout drilling operations. New technologies like vertical drilling and Enhanced Oil Recovery (EOR) are driving investments in this sector, as they decrease drilling time and facilitate the efficient extraction of oil. Additionally, the integration of advanced technologies like Real-time analysis, Predictive analytics, Artificial Intelligence, IoT Sensors, and Cloud Computing, enhances operational efficiency and data transparency.

The market is further driven by the need for data security, particularly in the oil and gas industry, where cybersecurity concerns are increasingly prevalent. The market caters to both conventional and unconventional resources, including crude oil, shale oil, shale gas, and conven

PurposeTo help search and rescue (SAR) volunteer teams and their partners collect mission data. By collecting information in a consistent manner and with spatially explicit tools, SAR agencies can better answer key questions:How many incidents have we responded to?Where are there high concentrations of incidents by type? How many total hours were volunteered in a given year?Audience Public Safety GIS Specialists who are deploying mission data collection solutions.What Is It? The Mountain Rescue Association has provided their Mission Data Collection Schema as a public resource. This is a zip file that contains the XLSForm, example look up tables, and schema fields in a text document. If you want to use ArcGIS Online and Survey123 Connect to deploy this form, please see documentation provided here https://doc.arcgis.com/en/survey123/desktop/create-surveys/xlsformessentials.htmFor DevelopersSee the GitHub repository https://github.com/cmrRose/sar-mission-data-entry

The Supervisory Control and Data Acquisition (SCADA) software market is estimated to be worth USD 23.0 billion in 2025 and is projected to reach USD 43.6 billion by 2033, exhibiting a CAGR of 8.2% during the forecast period. The growth of this market is primarily driven by the increasing demand for real-time data monitoring and control in various industries, including manufacturing, energy, and transportation. Additionally, the adoption of cloud-based SCADA systems, the proliferation of IoT devices, and the growing emphasis on industrial cybersecurity are further contributing to the market's expansion. Key market trends include the increasing adoption of cloud-based SCADA systems, which offer advantages such as scalability, reduced maintenance costs, and remote access to data. The proliferation of IoT devices is also driving growth as SCADA systems can integrate data from various devices to provide a comprehensive view of operations. Furthermore, the growing emphasis on industrial cybersecurity is leading to the development of SCADA systems with enhanced security features to protect against potential cyber threats. The market is dominated by key players such as Schneider Electric, GE Digital, AVEVA, Softpro, Sartorius, Operation Technology, Atvise, B-Scada, Claroty, AzeoTech, and others. These companies are constantly innovating and developing advanced SCADA solutions to meet the evolving needs of the market. Regional analysis indicates that North America and Europe are the most prominent markets for SCADA software, followed by Asia Pacific and the Middle East and Africa. SCADA software empowers industries to monitor and control complex processes, optimize operations, and ensure safety. This report provides an in-depth examination of the SCADA software landscape, exploring key players, regional trends, and emerging technologies that shape the market.

Comprises a national satellite imagery mosaic and derived information products produced by a collaboration of CSIRO, Geoscience Australia (GA) and State and Territory Surveys, and several additional national and international collaborators.

Mineral products were derived using a validated mosaic of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data.

Value: The data are used to understand distributions of and changes in surface materials and assessment of environmental, agricultural and resource potential.

Scope: This dataset covers the continent with the intent to provide the best quality mosaic from 10+ year archive of scenes across Australia (i.e., lowest cloud/vegetation cover, high sun angle etc)