Data Governance Software Market size was valued at USD 4.18 Billion in 2024 and is projected to reach USD 20.97 Billion by 2031, growing at a CAGR of 22.35% from 2024 to 2031.

Global Data Governance Software Market Drivers

Data Privacy Regulations: The increasing stringency of data privacy regulations such as GDPR, CCPA, and HIPAA mandates organizations to implement robust data governance practices. Data governance software helps companies ensure compliance with these regulations by managing data access, usage, and security.

Data Security Concerns: With the growing frequency and sophistication of cyber threats, organizations prioritize data security. Data governance software provides tools for defining and enforcing data security policies, monitoring data access and usage, and detecting and mitigating security breaches.

Data Quality Improvement: Poor data quality can lead to errors, inefficiencies, and inaccurate decision-making. Data governance software helps organizations establish data quality standards, define data quality metrics, and implement processes for data cleansing, validation, and enrichment to improve overall data quality.

Increasing Data Volumes and Complexity: Organizations are dealing with ever-increasing volumes of data from various sources, including structured and unstructured data, IoT devices, social media, and cloud applications. Data governance software helps manage this complexity by providing tools for data discovery, classification, and lineage tracking.

Digital Transformation Initiatives: Organizations undergoing digital transformation initiatives recognize the importance of data governance in ensuring the success of these initiatives. Data governance software facilitates data integration, collaboration, and governance across disparate systems and data sources, supporting digital transformation efforts.

Risk Management and Compliance: Effective data governance is essential for managing risks associated with data breaches, regulatory non-compliance, and reputational damage. Data governance software enables organizations to identify, assess, and mitigate risks related to data management and usage.

Global Manufacturing Analytics Market size was valued at USD 10.44 Billion in 2024 and is projected to reach USD 44.76 Billion by 2031, growing at a CAGR of 22.01% from 2024 to 2031.

Global Manufacturing Analytics Market Drivers

Growing Adoption of Industrial Internet of Things (IIoT): As more sensors and connected devices are used in manufacturing processes, massive volumes of data are generated. This increases the demand for analytics solutions in order to extract useful insights from the data.

Demand for Operational Efficiency: In order to increase output, cut expenses, and minimize downtime, manufacturers strive to improve their operations. Real-time operational data analysis is made possible by analytics systems, which promote proactive decision-making and process enhancements.

Growing Complexity in production Processes: With numerous steps, variables, and dependencies, modern production processes are getting more and more complicated. These intricate processes can be analyzed and optimized with the help of analytics technologies to increase productivity and quality.

Emphasis on Predictive Maintenance: To reduce downtime and prevent equipment breakdowns, manufacturers are implementing predictive maintenance procedures. By using machine learning algorithms to evaluate equipment data and forecast maintenance requirements, manufacturing analytics systems can optimize maintenance schedules and minimize unscheduled downtime.

Quality Control and Compliance Requirements: The use of analytics solutions in manufacturing is influenced by strict quality control guidelines and legal compliance obligations. Manufacturers may ensure compliance with quality standards and laws by using these technologies to monitor and evaluate product quality metrics in real-time.

Demand for Supply Chain Optimization: In an effort to increase productivity, save expenses, and boost customer happiness, manufacturers are putting more and more emphasis on supply chain optimization. Analytics tools give manufacturers insight into the workings of their supply chains, allowing them to spot bottlenecks, maximize inventory, and enhance logistical procedures.

Technological Developments in Big Data and Analytics: The production of analytics solutions is becoming more innovative due to advances in machine learning, artificial intelligence, and big data analytics. Thanks to these developments, manufacturers can now analyze massive amounts of data in real time, derive insights that can be put into practice, and improve their operations continuously.

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics.

The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Quality Assurance Services Market was valued at USD 5.3 Billion in 2024 and is projected to reach USD 12.9 Billion by 2031, growing at a CAGR of 11.2% during the forecast period 2024-2031.

Global Quality Assurance Services Market Drivers

The market drivers for the Quality Assurance Services Market can be influenced by various factors. These may include:

Increasing Complexity of Products and Services: The growing complexity of products and services across various industries necessitates robust quality assurance (QA) services to ensure compliance with standards and regulations.
Emphasis on Regulatory Compliance: Stringent regulatory requirements in industries such as healthcare, pharmaceuticals, aerospace, and automotive drive the demand for quality assurance services to meet regulatory standards and certifications.
Focus on Customer Experience: Organizations prioritize quality assurance to enhance customer satisfaction, improve product reliability, and maintain brand reputation through consistent delivery of high-quality products and services.
Globalization and Supply Chain Management: Globalization of supply chains requires rigorous quality control and assurance processes to manage product quality across international markets and ensure consistency.
Adoption of Industry 4.0 Technologies: Integration of advanced technologies such as IoT, AI, big data analytics, and automation in manufacturing and service sectors increases the need for quality assurance services to optimize processes and ensure product reliability.
Risk Management and Mitigation: Quality assurance services help mitigate risks associated with product defects, recalls, non-compliance, and potential legal liabilities, thereby protecting organizational assets and reputation.
Continuous Improvement Initiatives: Organizations adopt quality assurance as part of continuous improvement initiatives to achieve operational excellence, reduce waste, and enhance overall efficiency and productivity.
Demand for Software Testing Services: With the rise of digital transformation and software-driven solutions, there is an increasing demand for quality assurance services in software testing and validation to ensure application reliability and security.
Outsourcing Trends: Outsourcing of quality assurance services by organizations to specialized QA providers helps reduce costs, access expertise, and focus on core competencies, driving market growth.
Focus on Sustainable Practices: Increasing focus on sustainable practices and corporate social responsibility (CSR) encourages organizations to implement rigorous quality assurance measures to ensure environmental and ethical standards are met.

This research focused on defining improved water quality guideline trigger values for the GBR Water Quality Guidelines, by identifying measures of changes in coral reefs that are specifically related to recent and past exposure to changing water quality from altered catchments. An analysis of spatial and seasonal water quality conditions in six NRM regions on the GBR assessed the relationships between water quality and reef ecosystem health. Trigger values for water quality were determined to protect ecosystem health and model based predictions for ecosystem benefits for improvements should the trigger values by implemented.

Analysis was conducted for the six NRM regions: Burnett Mary, Fitzroy, Mackay Whitsundays, Burdekin Dry Tropics, Wet Tropics, and Cape York NRM. This included nine water quality parameters were analysed: Secchi depth, chlorophyll, suspended solids, particulate, dissolved and total nitrogen, and particulate, dissolved and total phosphorus. Four groups of biota were used as proxies for reef ecosystem status and biodiversity: these were macroalgal cover, species richness of hard corals, and species richness of phototrophic and heterotrophic octocorals.

Two separate approaches were used to define water quality guideline trigger values:

(i) The modelled relationships between the condition of reef biota

(ii) The analyses of the spatial distribution of water quality.

The chlorophyll and nutrient data were collected since 1976, and between 1992 and 2006 as part of the Long-Term Chlorophyll Monitoring program, and of the Reef Plan Marine Monitoring program since 2005.

The Secchi data were collected by a consortium of people from AIMS, DPIF, and members of the Reef Plan Marine Monitoring Program since 1976. These data are available through the data links on this page (eAtlas).

The hard coral biodiversity data were collected between 1994 and 2001.

The study was funded by the Great Barrier Reef Marine Park Authority, the Australian Institute of Marine Science and the Australian Government’s Marine and Tropical Sciences Research Facility

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Big Data as a Service Market Size 2024-2028

The big data as a service market size is forecast to increase by USD 41.20 billion at a CAGR of 28.45% between 2023 and 2028.

The market is experiencing significant growth due to the increasing volume of data and the rising demand for advanced data insights. Machine learning algorithms and artificial intelligence are driving product quality and innovation in this sector. Hybrid cloud solutions are gaining popularity, offering the benefits of both private and public cloud platforms for optimal data storage and scalability. Industry standards for data privacy and security are increasingly important, as large amounts of data pose unique risks. The BDaaS market is expected to continue its expansion, providing valuable data insights to businesses across various industries.

What will be the Big Data as a Service Market Size During the Forecast Period?

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Big Data as a Service (BDaaS) has emerged as a game-changer in the business world, enabling organizations to harness the power of big data without the need for extensive infrastructure and expertise. This service model offers various components such as data management, analytics, and visualization tools, enabling businesses to derive valuable insights from their data. BDaaS encompasses several key components that drive market growth. These include Business Intelligence (BI), Data Science, Data Quality, and Data Security. BI provides organizations with the ability to analyze data and gain insights to make informed decisions.



Data Science, on the other hand, focuses on extracting meaningful patterns and trends from large datasets using advanced algorithms. Data Quality is a critical component of BDaaS, ensuring that the data being analyzed is accurate, complete, and consistent. Data Security is another essential aspect, safeguarding sensitive data from cybersecurity threats and data breaches. Moreover, BDaaS offers various data pipelines, enabling seamless data integration and data lifecycle management. Network Analysis, Real-time Analytics, and Predictive Analytics are other essential components, providing businesses with actionable insights in real-time and enabling them to anticipate future trends. Data Mining, Machine Learning Algorithms, and Data Visualization Tools are other essential components of BDaaS.

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

The market 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.

Type

  Data analytics-as-a-Service
  Hadoop-as-a-service
  Data-as-a-service


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    Canada
    US


  APAC

    China


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Type Insights

The data analytics-as-a-service segment is estimated to witness significant growth during the forecast period.

Big Data as a Service (BDaaS) is a significant market segment, highlighted by the availability of Hadoop-as-a-Service solutions. These offerings enable businesses to access essential datasets on-demand without the burden of expensive infrastructure. DAaaS solutions facilitate real-time data analysis, empowering organizations to make informed decisions. The DAaaS landscape is expanding rapidly as companies acknowledge its value in enhancing internal data. Integrating DAaaS with big data systems amplifies analytics capabilities, creating a vibrant market landscape. Organizations can leverage diverse datasets to gain a competitive edge, driving the growth of the global BDaaS market. In the context of digital transformation, cloud computing, IoT, and 5G technologies, BDaaS solutions offer optimal resource utilization.

However, regulatory scrutiny poses challenges, necessitating stringent data security measures. Retail and other industries stand to benefit significantly from BDaaS, particularly with distributed computing solutions. DAaaS adoption is a strategic investment for businesses seeking to capitalize on the power of external data for valuable insights.

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The Data analytics-as-a-Service segment was valued at USD 2.59 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 35% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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Big Data as a Service Market analysis, North America is experiencing signif

The air quality monitoring network records and examines the concentrations of various pollutants in the air (immissions). If specified limit values ​​/ alarm thresholds are exceeded, measures are taken to reduce pollution. Reference measurement methods are defined in the EU directives. These are partly automated measurement methods, but partly also methods with laboratory analysis. In the case of procedures involving laboratory analysis, the measurement results are generally available approx. 6 weeks after the end of a measurement month. The distribution of the measuring points are nationwide, comprehensive, according to 39. BImSchV or EU air quality guidelines at pollution points (traffic, industry) and in the urban and rural background. Components: aromatic hydrocarbons (benzene, toluene, ethylbenzene, xylene), particulate matter (PM10,PM2.5), metals in PM10, PAH in PM10, NO2 (passive collector), soot (EC/OC)

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Brazil were from Cerrado, Goias, Minas and Gerais and were selected based on the following criterion: - Small and medium growers: less or equal to 2000ha of soybean

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Viet Nam were selected based on the following criterion:

(a) smallholder maize growers Corn growers in Dong Nai & Son La province Second season
Low investment
Use of little or no seed treatment or crop protection (--> all use CPP but BF should use generics)
Average cultivation skills: mid-tier (sub-optimal CP/SE use) (mid-tier growers use generic CP, cheaper CP, non-Syngenta hybrid seeds)
Not progressive: simple knowledge on agronomy and pests; less accessible to technology
influenced by fellow farmers and retailers
not strong financial status, may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing: Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group)

o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

AQMS (Atmosphere Quality Monitoring Systems) Market size is growing at a good pace over the last few years & is expected to grow at a CAGR of 6.89% from 2024-2031

Global AQMS (Atmosphere Quality Monitoring Systems) Market Drivers

Tight Environmental laws: The need for AQMS is driven by tightening environmental standards and government laws that are meant to monitor and reduce air pollution. The installation of air quality monitoring equipment is required by laws like the US Clean Air Act, the EU Ambient Air Quality Directive, and programmes like India’s National Clean Air Programme (NCAP), which propel market expansion.

Growing Concerns about Air Pollution: Concerns over air pollution are growing. The need for AQMS is fueled by growing understanding of the harmful health effects of air pollution, such as respiratory illnesses, cardiovascular problems, and premature deaths. The government is using AQMS for real-time monitoring and management of air pollution through public-private partnerships and initiatives, driven by public concern over the quality of life and air quality.

Urbanisation and Industrialization: As a result of rising industrialization and urbanisation in emerging nations, pollution from industry, transportation, and daily urban activities is rising. The need for comprehensive AQMS solutions to monitor and control air pollution grows as cities and industrial zones spread, propelling market growth.

Technological Developments: The creation of sophisticated AQMS solutions with improved accuracy, dependability, and real-time monitoring capabilities is made possible by developments in sensor technology, data analytics, and the Internet of Things. New developments include inexpensive sensors, satellite-based monitoring, and remote sensing technologies improve the efficiency of AQMS in identifying contaminants and evaluating changes in air quality.

Safety and Health Issues: The demand for AQMS solutions is driven by rising public knowledge and concern about the negative health effects of poor air quality. The market is growing as a result of the growing demand from people, communities, and organisations for access to real-time data on air quality so they can decide on outdoor activities, safety precautions, and pollution mitigation strategies.

Governmental Projects and Funding: The AQMS industry is expanding as a result of government financing, grants, and investments targeted at tackling environmental issues and enhancing air quality. Government contracts, public-private partnerships, and incentive programmes for AQMS implementation promote uptake and propel market growth.

Corporate Social Responsibility (CSR): Organisations dedicated to lowering their environmental impact and reducing air pollution use AQMS as a means of achieving their sustainability objectives and corporate initiatives. Businesses spend money on AQMS solutions to track emissions, adhere to rules, and show environmental stewardship—all of which support market expansion.

Emergence of Smart Cities: AQMS implementation is made possible by the notion of smart cities, which is defined by the integration of IoT, AI (Artificial Intelligence), and digital technologies to enhance urban infrastructure and services. The need for AQMS solutions is being driven by smart city projects, which place a high priority on air quality management and monitoring as a component of urban sustainability initiatives.

Public Awareness and Environmental Activism: Environmental activism, public campaigns, and media coverage are effective ways to increase public awareness of air quality issues and rally support for pollution control measures. Demand for AQMS is driven by citizen science programmes, advocacy groups, and grassroots movements that demand greater accountability, transparency, and action on air pollution.

Initiatives to Combat Global Climate Change: Air quality monitoring and pollution reduction are critical components of international accords and measures to combat climate change, such as the Sustainable Development Goals (SDGs) and the Paris Agreement. As part of their climate action plans, nations agree to monitor and report air quality data, which drives demand for AQMS solutions.

Snapshot img

Financial Analytics Market Size 2024-2028

The financial analytics market size is forecast to increase by USD 8.08 billion at a CAGR of 12.7% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for advanced risk management tools in today's complex financial landscape. With the exponential rise in data generation across various industries, there is a pressing need for sophisticated analytics solutions to make informed decisions and gain a competitive edge. However, this trend comes with its challenges, particularly around data privacy and security concerns. As companies continue to invest in data collection and analysis, ensuring the protection of sensitive information will remain a top priority. Additionally, regulatory compliance and the integration of emerging technologies, such as artificial intelligence and machine learning, will further shape the market's strategic landscape. Companies seeking to capitalize on these opportunities must stay informed of the latest trends and regulations while navigating the evolving data privacy and security landscape. By doing so, they can effectively leverage financial analytics to drive operational efficiency, mitigate risk, and ultimately, achieve long-term success.

What will be the Size of the Financial Analytics Market during the forecast period?

Request Free SampleThe market is experiencing growth, driven by the increasing adoption of data-driven decision-making and digital transformation initiatives in various industries. This market encompasses a range of solutions, including financial analytics solutions, predictive analytics, advanced analytics solutions, and ESG-based databases. Key trends include the integration of data from multiple sources to inform revenue management, customer satisfaction, resource utilization, and financial efficiency. Data security and data quality management are critical considerations, as financial analytics solutions process sensitive financial information. Regulatory compliance, including audit & compliance regulations and trade regulations, also play a significant role in market dynamics. Import-export analysis and healthcare expenditure are among the numerous applications of financial analytics, contributing to the market's overall size and direction.

How is this Financial Analytics Industry segmented?

The financial analytics 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. ComponentSolutionServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSEuropeGermanyUKAPACChinaIndiaMiddle East and AfricaSouth America

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.Financial analytics solutions play a pivotal role in managing various financial risks for organizations, including credit, market, and operational risks. These tools enable businesses to identify potential risks and implement preventive measures. Compliance with stringent financial regulations, such as Basel III, Dodd-Frank, and GDPR, necessitates advanced data analytics and reporting capabilities. Financial analytics solutions facilitate regulatory adherence and help organizations avoid penalties. These solutions offer insights into financial performance, measure key performance indicators (KPIs), and track progress towards financial objectives. Additionally, they support data-driven decision-making, predictive analytics, and data security. In sectors like healthcare, energy & power, IT & telecom, automotive and manufacturing, and retail, digital transformation initiatives are driving the adoption of financial analytics solutions. These advanced analytics tools employ artificial intelligence, machine learning, and other technologies to enhance investment decision-making processes, resource utilization, financial efficiency, product profitability, customer profitability, and risk mitigation. Companies can leverage these solutions to ensure regulatory compliance, improve financial management, and enhance overall business performance.

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The Solution segment was valued at USD 3.57 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 34% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American financial sector is subject to rigorous reporting and compliance regulations, necessitating the utilization of f

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Sudan were selected based on the following criterion:

(a) smallholder sorghum growers located in Gezira
part of a cooperative
med-high technology adoption
also cultivate other crops (cotton, ground nut, vegetables, water melon

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Content

This data contains provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. Counts for the most recent final annual data are provided for comparison. National provisional counts include deaths occurring within the 50 states and the District of Columbia as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation (see Technical notes) resulting in an underestimate relative to final counts. To address this, methods were developed to adjust provisional counts for reporting delays by generating a set of predicted provisional counts (see Technical notes). Starting in June 2018, this monthly data release will include both reported and predicted provisional counts.

The provisional data include: (a) the reported and predicted provisional counts of deaths due to drug overdose occurring nationally and in each jurisdiction; (b) the percentage changes in provisional drug overdose deaths for the current 12 month-ending period compared with the 12-month period ending in the same month of the previous year, by jurisdiction; and (c) the reported and predicted provisional counts of drug overdose deaths involving specific drugs or drug classes occurring nationally and in selected jurisdictions. The reported and predicted provisional counts represent the numbers of deaths due to drug overdose occurring in the 12-month periods ending in the month indicated. These counts include all seasons of the year and are insensitive to variations by seasonality. Deaths are reported by the jurisdiction in which the death occurred.

Several data quality metrics, including the percent completeness in overall death reporting, percentage of deaths with cause of death pending further investigation, and the percentage of drug overdose deaths with specific drugs or drug classes reported are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical notes). Reporting of the specific drugs and drug classes involved in drug overdose deaths varies by jurisdiction, and comparisons of death rates involving specific drugs across selected jurisdictions should not be made (see Technical notes). Provisional data will be updated on a monthly basis as additional records are received.

Technical notes

Nature and sources of data

Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from state vital registration offices through the Vital Statistics Cooperative Program (VSCP).

The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death.

Provisional death counts presented in this data visualization are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2017 would include deaths occurring from July 1, 2016, through June 30, 2017. The 12-month ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. Counts for the 12-month period ending in the same month of the previous year are shown for comparison. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12-month ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation. Methods to adjust provisional counts have been developed to provide predicted provisional counts of drug overdose deaths, accounting for delayed reporting (see Percentage of records pending investigation and Adjustments for delayed reporting).

Provisional data are based on available records that meet certain data quality criteria at the time of analysis and may not include all deaths that occurred during a given time period. Therefore, they should not be considered comparable with final data and are subject to change.

Cause-of-death classification and definition of drug deaths Mortality statistics are compiled in accordance with World Health Organization (WHO) regulations specifying that WHO member nations classify and code causes of death with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regulations on use of the classification. Causes of death for data presented in this report were coded according to ICD guidelines described in annual issues of Part 2a of the NCHS Instruction Manual (2).

Drug overdose deaths are identified using underlying cause-of-death codes from the Tenth Revision of ICD (ICD–10): X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), and Y10–Y14 (undetermined). Drug overdose deaths involving selected drug categories are identified by specific multiple cause-of-death codes. Drug categories presented include: heroin (T40.1); natural opioid analgesics, including morphine and codeine, and semisynthetic opioids, including drugs such as oxycodone, hydrocodone, hydromorphone, and oxymorphone (T40.2); methadone, a synthetic opioid (T40.3); synthetic opioid analgesics other than methadone, including drugs such as fentanyl and tramadol (T40.4); cocaine (T40.5); and psychostimulants with abuse potential, which includes methamphetamine (T43.6). Opioid overdose deaths are identified by the presence of any of the following MCOD codes: opium (T40.0); heroin (T40.1); natural opioid analgesics (T40.2); methadone (T40.3); synthetic opioid analgesics other than methadone (T40.4); or other and unspecified narcotics (T40.6). This latter category includes drug overdose deaths where ‘opioid’ is reported without more specific information to assign a more specific ICD–10 code (T40.0–T40.4) (3,4). Among deaths with an underlying cause of drug overdose, the percentage with at least one drug or drug class specified is defined as that with at least one ICD–10 multiple cause-of-death code in the range T36–T50.8.

Drug overdose deaths may involve multiple drugs; therefore, a single death might be included in more than one category when describing the number of drug overdose deaths involving specific drugs. For example, a death that involved both heroin and fentanyl would be included in both the number of drug overdose deaths involving heroin and the number of drug overdose deaths involving synthetic opioids other than methadone.

Selection of specific states and other jurisdictions to report Provisional counts are presented by the jurisdiction in which the death occurred (i.e., the reporting jurisdiction). Data quality and timeliness for drug overdose deaths vary by reporting jurisdiction. Provisional counts are presented for reporting jurisdictions based on measures of data quality: the percentage of records where the manner of death is listed as “pending investigation,” the overall completeness of the data, and the percentage of drug overdose death records with specific drugs or drug classes recorded. These criteria are defined below.

Percentage of records pending investigation

Drug overdose deaths often require lengthy investigations, and death certificates may be initially filed with a manner of death “pending investigation” and/or with a preliminary or unknown cause of death. When the percentage of records reported as “pending investigation” is high for a given jurisdiction, the number of drug overdose deaths is likely to be underestimated. For jurisdictions reporting fewer than 1% of records as “pending investigation”, the provisional number of drug overdose deaths occurring in the fourth quarter of 2015 was approximately 5% lower than the final count of drug overdose deaths occurring in that same time period. For jurisdictions reporting greater than 1% of records as “pending investigation” the provisional counts of drug overdose deaths may underestimate the final count of drug overdose deaths by as much as 30%. Thus, jurisdictions are included in Table 2 if 1% or fewer of their records in NVSS are reported as “pending investigation,” following a 6-month lag for the 12-month ending periods included in the dashboard. Values for records pending investigation are updated with each monthly release and reflect the most current data available.

Percent completeness

NCHS receives monthly counts of the estimated number of deaths from each jurisdictional vital registration offices (referred to as “control counts”). This number represents the best estimate of how many deaths occurred in a given jurisdiction in each month. Death records in the NVSS database must have

Abstract

PCBS conducted the Environmental Economic Survey during the period 22/03/2011 to 20/08/2011; with the primary objective of providing reliable data on the main environmental indicators in economic establishments in the Palestinian Territory, including the methods used to handle solid waste and wastewater. The survey also includes the role of the local authority in providing a suitable environment that minimizes the negative effect of different types of pollution from economic activities. This report is divided into three chapters: the first chapter defines the main findings of the report. The second chapter explains the methodology of data collection and tabulation, in addition to details regarding data quality and estimations of the data sources of this report. The third chapter contains the concepts and definitions used in this report.

Geographic coverage

Palestinian Territory.

Analysis unit

Target Population All of the Palestinian economic establishments were included in the Economic Series Survey sample in the Palestinian Territory.

Universe

All of the economic establishments in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample was a single-stage stratified cluster random sample using 3,922 Palestinian economic establishments distributed according to economic activity and governorate. Target Population: All Palestinian economic establishments were included in the Economic Series Survey sample in the Palestinian Territory. Sample Frame: The sampling frame was based on the Establishments' Census-2007 conducted by PCBS. Stratification: Three levels of stratification were followed in designing the sample of the economic survey, including: 1.Stratification by region: West Bank and Gaza Strip and classification according to governorate. 2.Stratification by economic activity according to ISIC4. 3.Stratification by employer group.

Sampling deviation

There is not any deviations

Mode of data collection

Face-to-face [f2f]

Research instrument

The environmental questionnaire was designed according to international standards and recommendations for the most important indicators, taking into account the special situation of Palestine. Many visits were made to economic establishments to improve the survey tools and test the questionnaire prior to the implementation of the survey: subsequently some modifications were made to the questionnaire and to the instructions following the visits.

Cleaning operations

The data processing stage comprised the following operations: 1.Editing before data entry: all questionnaires were edited again in the office using the same instructions adopted for editing in the field. 2.Data entry: At this stage, data were entered into the computer using Access database. 3.The data entry program was set up to satisfy a number of requirements, such as: Duplication of the questionnaire on the computer screen. Checks for logic and consistency of data entered. Possibility of internal editing of answers to questions. Maintaining a minimum of digital data entry and field work errors. User-friendly handling. Possibility of transferring data into another format to be used and analyzed using other statistical analytical systems, such as SAS and SPSS.

Response rate

None response rate = (Sum of none response cases/ Net sample) x 100% = (386/ 3,491) x 100% = 11.1% Response rate = 100% - none response rate = 100% - 11.1% = 88.9%

Sampling error estimates

These types of error are due to studying only a part of a social base. Since this survey is sample based, the data will be affected by sampling errors due to not using the whole frame of society and differences may appear compared with the actual values that could be obtained through a census. For this survey, variance calculations were made for the amounts of water consumed and the main source of supply in economic establishments by region and activity.

Data appraisal

Comparability The data of the environmental economic survey are comparable geographically and against time; a comparison of the data between different geographical areas and with data from previous rounds showed no significant differences. Concerning economical activities, published data in this report are in ISIC-4 of economic activities, so it must attention when comparable with previous reports because published at ISIC-3 of economic activities.
Data Quality Assurance Procedures Several measures were applied to ensure quality control in the survey, such as the training of field workers on the main skills before data collection, and conducting field visits to field researchers to ensure the integrity of data collection, in addition to conducting a re-interview of five percent of the economic establishments. An audit of questionnaires was carried out before data entry and a program was used that does not allow any mistakes during the data entry process. Data were examined to ensure that they were free from errors not previously discovered. After receipt of the raw data file, cleaning and inspection of the anomalous values was conducted and the consistency of different questions on the questionnaire was examined.

Abstract

The Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey 2002-2003 is one of the biggest national surveys carried out in accordance with an international methodology with technical and financial support from the World Bank and United Nations Development Programme.

Background This survey was developed in response to provide the picture of the current situation of poverty in Mongolia in relation to social and economic indicators and contribute toward implementation and progress on National Millennium Development Goals articulated in the National Millennium Development Report and monitoring of the Economic Growth Support and Poverty Reduction Strategy, as well as toward developing and designing future policies and actions. Also, the survey enriched the national database on poverty and contributed in improving the professional capacity of experts and professionals of the National Statistical Office of Mongolia.

Purpose Since the onset of the transition to a market economy of Mongolia our country the need to study changes in people's living standards in relation to household members' demographic situation, their education, health, employment and household engagement in private enterprises has become extremely important. With that purpose and with the support of the World Bank and the United Nations Development Programme, the National Statistical Office of Mongolia conducted the Integrated Household Income and Expenditure Survey with Living Standards Measurement Survey-like features between 2002 and 2003. In conjunction with LSMS household interviews the NSO also collected a price and a community questionnaire in each selected soum. The latter collected information on the quality of infrastructure, and basic education and health services.

Main importance of the survey is to provide policy makers and decision makers with realistic information about poverty and will become a resource for experts and researchers who are interested in studying poverty as well as social and economic issues of Mongolia.

In July 2003 the Government of Mongolia completed the Economic Growth and Poverty Reduction Strategy Paper in which the Government gave high priority to the fight against poverty. As part of that commitment this paper is a study that intends to monitor poverty and understand its main causes in order to provide policy-makers with useful information to improve pro-poor policies.

Content The Integrated HIES with LSMS design has the peculiarity of being a sub-sample of a larger survey, namely the Household Income and Expenditure Survey 2002. Instead of administering an independent consumption module, the Integrated HIES with LSMS 2002-2003 depends on the HIES 2002 information on household consumption expenditure. This is why the survey is referred as Integrated HIES with LSMS 2002-2003. This survey is the only source of information of income-poverty, and the questionnaire is designed to provide poverty estimates and a set of useful social indicators that can monitor more in general human development, as well as more specific issues on key sectors, such as health, education, and energy. And, the price and social survey, in conjunction with LSMS household interviews, collected information on the quality of infrastructure, and basic education and health services of each selected soum.

HIES - food expenditure and consumption, non-food expenditure, other expense, income LSMS - general information, household roster, housing, education, employment, health, fertility, migration, agriculture, livestock, non-farm enterprises, other souces of income, savings and loans, remittances, durable goods, energy PRICE SURVEY - prices of household consumer goods and services SOCIAL SURVEY - population and households, economy and infrastructure, education, health, agriculture and livestock, and non-agricultural business

Survey results The final report of this survey has main results on key poverty indicators, used internationally, as they relate to various social sectors. Its annexes contain information regarding the consumption structure, poverty lines along with the methodology used, as well as some statistical indicators.

The main contributions of this survey report are: - new poverty estimates based on the latest available household survey, the Integrated HIES with LSMS 2002-2003 - the implementation of appropriate, and internationally accepted, methodologies in the calculation of poverty and its analysis (these methodologies may constitute a reference for the analysis of future surveys) - a 'poverty profile' that describes the main characteristics of poverty

The first section of the report provides information on the Mongolian economic background, and presents the basic poverty measures that are linked to the economic performance to offer an indication of what happened to poverty and inequality in recent years. A second section goes in much more detail in generating and describing the poverty profile, in particular looking at the geographical distribution of poverty, poverty and its correlation with household demographic characteristics, characteristics of the household head, employment, and assets. A final section looks at poverty and social sectors and investigates various aspects of education, health and safety nets. The report contains also a number of useful, but more technical appendixes with information about the HIES-LSMS 2002-2003 (sample design and data quality), on the methodology used to construct the basic welfare indicator, and set the poverty line, some sensitivity analysis, and additional statistical information.

Geographic coverage

The survey is nationally representative and covers the whole of Mongolia.

Analysis unit

• Household (defined as a group of persons who usually live and eat together)
• Household member (defined as members of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)
• Selected soums (for collecting prices of household consumer goods and services and information on quality of infrastructure, basic education, health services and so on)

Universe

The survey covered selected households and all members of the households (usual residents). And the price and social surveys covered all selected soums.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Integrated HIES with LSMS 2002-2003 households are a subset of the household interviewed for the HIES 2002. One third of the HIES 2002 households were contacted again and interviewed on the LSMS topics. The subset was equally distributed among the four quarters.

The HIES 2002, and consequently the Integrated HIES with LSMS 2002-2003, used the 2000 Census as sample frame. 1,248 enumerations areas were part of the sample, which is a two-stage stratified random sample. The strata, or domains of estimation, are four: Ulaanbaatar, Aimag capitals and small towns, Soum centres, and Countryside. At a first stage a number of Primary Sampling Units (PSUs) were selected from each stratum. In the selected PSUs enumerators listed all the households residing in the area, and in a second stage households were randomly selected from the list of households identified in that PSU (10 households were selected in urban areas and 8 households in rural areas).

It should be noted that non-response case of households once selected for the survey exerts unfavorable influence on the representativeness of the survey. Therefore an enumerator should take every step to avoid that. To obtain true and timely survey results a proper agreement should be reached with a selected household before a survey starts. One of the main reasons of non-response is that an enumerator doesn't meet with the household members who are able to give the required information. An enumerator should visit a household at least 3 times within the given period to take the questionnaire.

Another common reason is that a household refuses to participate in the survey. In this case an enumerator should explain the purpose of the survey again, explain that the private data will be kept strictly confidential according to the corresponding law. If necessary an enumerator can ask local statistical division or local administration for the help. However this practice is very seldom.

If there is no possibility to take the questionnaires from the selected households due to weather conditions or disasters, reserved households with numbers 11, 12, 13 respectively from the list provided by the NSO should replace the omitted ones. However the reasons of replacements are to be declared in detail on the form.

Sampling deviation

At the planning stage the time lag between the HIES and LSMS interviews was expected to be relatively short. However, for various reasons it is on average of about 9 months, and for some households more than one year. Households interviewed in the first and second quarter of 2002 were generally re-interviewed in March and April 2003, while households of the third and fourth quarter of 2002 were re-interviewed in May, June and July of 2003. The considerable time lag between HIES and LSMS interviews was the main responsible for a considerable loss of households in the LSMS sample, households that could not be easily relocated and therefore re-interviewed. Due also to some incomplete questionnaires, the number of households that were used for the final poverty analysis is 3,308.

Mode of data collection

Face-to-face [f2f]

Research instrument

A

BackgroundTwo World Health Organization comparative assessments rated the quality of South Africa’s 1996 mortality data as low. Since then, focussed initiatives were introduced to improve civil registration and vital statistics. Furthermore, South African cause-of-death data are widely used by research and international development agencies as the basis for making estimates of cause-specific mortality in many African countries. It is hence important to assess the quality of more recent South African data.MethodsWe employed nine criteria to evaluate the quality of civil registration mortality data. Four criteria were assessed by analysing 5.38 million deaths that occurred nationally from 1997–2007. For the remaining five criteria, we reviewed relevant legislation, data repositories, and reports to highlight developments which shaped the current status of these criteria.FindingsNational mortality statistics from civil registration were rated satisfactory for coverage and completeness of death registration, temporal consistency, age/sex classification, timeliness, and sub-national availability. Epidemiological consistency could not be assessed conclusively as the model lacks the discriminatory power to enable an assessment for South Africa. Selected studies and the extent of ill-defined/non-specific codes suggest substantial shortcomings with single-cause data. The latter criterion and content validity were rated unsatisfactory.ConclusionIn a region marred by mortality data absences and deficiencies, this analysis signifies optimism by revealing considerable progress from a dysfunctional mortality data system to one that offers all-cause mortality data that can be adjusted for demographic and health analysis. Additionally, timely and disaggregated single-cause data are available, certified and coded according to international standards. However, without skillfully estimating adjustments for biases, a considerable confidence gap remains for single-cause data to inform local health planning, or to fill gaps in sparse-data countries on the continent. Improving the accuracy of single-cause data will be a critical contribution to the epidemiologic and population health evidence base in Africa.

Data Governance Market was valued at USD 3676.25 Million in 2023 and is projected to reach USD 17912.24 Million By 2031, growing at a CAGR of 21.89% during the forecast period 2024 to 2031.

Data Governance Market: Definition/ Overview

Data governance is the comprehensive control of data availability, usefulness, integrity, and security in an organization. It consists of a system of processes, responsibilities, regulations, standards, and metrics designed to ensure the effective and efficient use of information. Data governance refers to the processes that businesses use to manage data over its entire lifecycle, from creation and storage to sharing and archiving.

The fundamental purpose is to ensuring that data is accurate, consistent, and available to authorized users while adhering to legal regulations. Data governance is used in a variety of industries, including healthcare, banking, retail, and manufacturing, to improve decision-making, operational efficiency, and risk management by offering a standardized approach to data processing and quality control.

The volume, diversity, and velocity of data generated in the digital age have driven the expansion and evolution of data governance. As enterprises implement new technologies like artificial intelligence, machine learning, and big data analytics, the demand for strong data governance frameworks will grow. Emerging trends, such as data democratization, which makes data available to a wider audience within an organization, and the integration of data governance with data privacy and security measures, will affect the future.

Highlights:

• High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.
• The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.
• Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.
• In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.
• Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.
• The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).
• Additional information is provided in a "Data in Brief" journal article: https://doi.org/10.1016/j.dib.2023.109224

Collector array description: The data is from a flat plate collector array with a total gross collector area of 516 m² (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.

Data files:

• FHW_ArcS_main_2017.csv – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix _calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix _std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).
• FHW_ArcS_main_2017.parquet – Same as FHW_ArcS_main_2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.
• FHW_ArcS_parameters.json – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.
• FHW_ArcS_raw_2017.csv – Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS_main_2017.csv, which includes all calculated data channels, is a superset of this file.

Scripts:

• make_data.py – This Python script exposes the calculation process of the calculated data channels (suffix _calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.
• make_plots.py – This Python script, together with utils_plots.py, generates several figures based on the main dataset.

Data collection and preparation: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria

Data owner: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria

Additional information is provided in a journal article in "Data in Brief", titled "One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria".

Note: A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Cote d'ivoire were from Bonoua (town), Oumé (department), Tiassalé (department), Afféry (town), Aboisso (department) and were selected based on the following criterion: - Low level of technology adoption
- Diversification with other crops (80-90% cocoa)
- From GGP 2017 onwards: Question included about full-year yield results, apart from focus season yields for KPI

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Snapshot img

HVAC Test Instruments Market Size 2025-2029

The HVAC test instruments market size is forecast to increase by USD 237.9 million at a CAGR of 7% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for HVAC systems in both residential and commercial sectors. This trend is driven by the need for energy efficiency and indoor air quality, leading to the adoption of advanced HVAC technologies and energy-efficient HVAC systems. Another key trend is the growing popularity of wireless HVAC test instruments, which offer convenience and ease of use, along with the rising integration of HVAC control systems for improved performance and automation. However, the market also faces challenges such as the heavy reliance on China for low-cost HVAC test instruments, which may impact the quality and reliability of the products. Additionally, the increasing complexity of HVAC systems and the need for regular maintenance and testing pose challenges for market players.

What will be the Size of the Market During the Forecast Period?

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The market is witnessing significant growth due to the increasing demand for energy-efficient HVAC systems in both residential and commercial buildings. Regulatory measures aimed at reducing energy consumption and improving indoor air quality are driving the market's expansion. In the residential sector, homeowners are prioritizing comfort and energy savings, leading to the adoption of smart HVAC systems. These systems allow for remote monitoring and control of temperature, humidity conditions, and airflow parameters. In commercial buildings, the focus is on optimizing energy usage and ensuring occupant comfort, making wireless HVAC systems an attractive option.



Regulations play a crucial role in the market. Compliance with energy efficiency standards and indoor air quality regulations is mandatory for HVAC system installations. Test instruments are essential in ensuring these regulations are met, with clamp meters used to measure electrical connections and anemometers used to test airflow. Smart HVAC systems are gaining popularity in both residential HVAC and commercial HVAC sectors due to their energy efficiency and ability to adapt to changing temperature and humidity conditions, providing enhanced comfort and reduced energy consumption. Test instruments are necessary to ensure the proper functioning of these systems, with low GWP (Global Warming Potential) refrigerants becoming increasingly common. 

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

The market 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.

Application

  Airflow and quality
  Temperature and humidity
  Electrical
  Others


Product Type

  Dye injector kit
  Refrigerant measuring and monitoring
  Gauges


Geography

  APAC

    China
    India
    Japan
    South Korea


  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  Middle East and Africa



  South America

By Application Insights

The airflow and quality segment is estimated to witness significant growth during the forecast period.

The market encompasses various tools, including anemometers, clamp meters, balometers, filter testers, and carbon monoxide meters. Among these, airflow and quality test instruments hold the largest market share due to their essential role in HVAC system testing. The increasing demand for these instruments stems from regulations such as the Indoor Air Quality (IAQ) standard and the Association of Home Appliance Manufacturers (AHAM) AC-1-2013 standard. These regulations necessitate the use of accurate and reliable test instruments for maintaining IAQ. Prominent market players cater to this demand by providing a comprehensive range of HVAC test instruments.

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The Airflow and quality segment was valued at USD 208.90 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 55% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The Asia Pacific (APAC) region, specifically China, Japan, India, and Indonesia, leads the global market for HVAC systems due to high adoption rates. APAC's dominance is attributed to the region's strong construction sector, particularly in residential and commercial buildings. This growth results in increased demand for HVAC services and, consequently