A global self-hosted Market Research dataset containing all administrative divisions, cities, addresses, and zip codes for 247 countries. All geospatial data is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

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Our map data packages are offered in variable formats, including .csv. All geographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

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The global database management system (DBMS) market was valued at USD 89.00 Billion in 2024. The market is expected to grow at a CAGR of 10.80% during the forecast period of 2025-2034 to reach a value of USD 248.19 Billion by 2034. This expansion is driven by the increasing need for scalable, cloud-based, and AI-integrated database solutions capable of managing both structured and unstructured data.

Another key growth driving factor is the rising demand for real-time data processing across industries such as finance, healthcare, and e-commerce, where instant data access and analytics are critical for decision-making and customer engagement. Innovations like MongoDB’s integration with Microsoft Azure for real-time analytics and generative AI highlight how DBMS providers are adapting to support next-generation capabilities, positioning the market for sustained growth.

The global database management system (DBMS) market growth is further driven by the surge in digital data, the proliferation of IoT devices, and the growing reliance on big data analytics across industries. Organizations are working efficiently to manage, integrate, and secure vast volumes of structured and unstructured data generated from diverse sources. This demand has made robust DBMS solutions essential for supporting real-time analytics, ensuring regulatory compliance, and enabling agile business operations.

A key trend shaping the market is the integration of artificial intelligence (AI) and advanced analytics into DBMS platforms. In October 2024, NNIT joined the Veeva AI Partner Program and launched FRED, an AI-powered solution for Veeva Vault R&D applications. By leveraging large language models, FRED enables organizations to convert business queries into executable code, automating data processes and enhancing compliance across relational databases and cloud repositories.

Vector Database Market Outlook

According to our latest research, the global vector database market size reached USD 1.12 billion in 2024, demonstrating robust momentum driven by the surging adoption of artificial intelligence and machine learning applications. The market is experiencing a remarkable expansion, registering a CAGR of 22.4% from 2025 to 2033. By 2033, the market is forecasted to reach USD 8.43 billion, underscoring the transformative role of vector databases in powering next-generation data-driven solutions. This extraordinary growth trajectory is fueled by the increasing need for high-performance search and analytics capabilities across industries, as organizations pivot towards leveraging unstructured and semi-structured data for strategic advantage.

A primary growth factor for the vector database market is the exponential increase in the volume and complexity of unstructured data generated by enterprises. As organizations accumulate vast amounts of images, videos, text, and other rich media, traditional relational databases struggle to provide the speed and scalability required for real-time analysis and retrieval. Vector databases, designed specifically to handle high-dimensional vector representations, have become essential for enabling advanced search and recommendation systems. The proliferation of AI-powered applications, such as semantic search, natural language processing, and image recognition, is amplifying the demand for vector databases, as these systems rely on vector embeddings to deliver accurate and contextually relevant results. Furthermore, the integration of vector databases with popular machine learning frameworks is streamlining the development and deployment of intelligent solutions, accelerating market adoption.

Another significant driver is the rapid digital transformation across key verticals, including BFSI, healthcare, retail and e-commerce, and IT and telecommunications. Enterprises in these sectors are increasingly leveraging vector databases to enhance customer experiences, improve operational efficiency, and unlock new revenue streams. For instance, in retail and e-commerce, vector databases power personalized recommendation engines and visual search capabilities, driving higher conversion rates and customer satisfaction. In healthcare, they enable advanced medical image analysis and patient data retrieval, supporting better diagnostics and treatment outcomes. The growing emphasis on data-driven decision-making and the need to derive actionable insights from complex datasets are compelling organizations to invest in vector database technologies, further propelling market growth.

The evolution of deployment models and the rise of cloud-native architectures have also contributed to the expansion of the vector database market. Organizations are increasingly opting for cloud-based vector database solutions to benefit from scalability, flexibility, and cost efficiency. Cloud deployment enables seamless integration with existing IT infrastructure and allows enterprises to scale resources dynamically based on workload demands. This shift is particularly pronounced among small and medium enterprises (SMEs), which often lack the capital and expertise to maintain on-premises infrastructure. The availability of managed vector database services from major cloud providers is lowering the barrier to entry, democratizing access to advanced data management capabilities, and fueling widespread adoption across diverse industry segments.

The financial services sector is increasingly recognizing the transformative potential of vector search technology. Vector Search for Financial Services is revolutionizing how institutions manage and analyze vast datasets, enabling more accurate risk assessments and personalized customer interactions. By leveraging high-dimensional vector representations, financial organizations can enhance fraud detection, streamline compliance processes, and deliver tailored financial products. This technology is particularly beneficial in real-time trading environments, where rapid data retrieval and analysis are crucial. As the financial industry continues to evolve, the adoption of vector search solutions is set to accelerate, driving innovation and competitive advantage in a data-driven landscape.

From a regional perspective, North America continues to dominate the vector database market, driven by the p

Global Vector Database Market size & share estimated to surpass USD 10,409.89 million by 2032, to grow at a CAGR of 21.7% during the forecast period.

Open-Source Database Software Market size was valued at USD 10.00 Billion in 2024 and is projected to reach USD 35.83 Billion by 2032, growing at a CAGR of 20% during the forecast period 2026-2032.

Global Open-Source Database Software Market Drivers

The market drivers for the Open-Source Database Software Market can be influenced by various factors. These may include:

Cost-Effectiveness: Compared to proprietary systems, open-source databases frequently have lower initial expenses, which attracts organizations—especially startups and small to medium-sized enterprises (SMEs) with tight budgets. Flexibility and Customisation: Open-source databases provide more possibilities for customization and flexibility, enabling businesses to modify the database to suit their unique needs and grow as necessary. Collaboration and Community Support: Active developer communities that share best practices, support, and contribute to the continued development of open-source databases are beneficial. This cooperative setting can promote quicker problem solving and innovation. Performance and Scalability: A lot of open-source databases are made to scale horizontally across several nodes, which helps businesses manage expanding data volumes and keep up performance levels as their requirements change. Data Security and Sovereignty: Open-source databases provide businesses more control over their data and allow them to decide where to store and use it, which helps to allay worries about compliance and data sovereignty. Furthermore, open-source code openness can improve security by making it simpler to find and fix problems. Compatibility with Contemporary Technologies: Open-source databases are well-suited for contemporary application development and deployment techniques like microservices, containers, and cloud-native architectures since they frequently support a broad range of programming languages, frameworks, and platforms. Growing Cloud Computing Adoption: Open-source databases offer a flexible and affordable solution for managing data in cloud environments, whether through self-managed deployments or via managed database services provided by cloud providers. This is because more and more organizations are moving their workloads to the cloud. Escalating Need for Real-Time Insights and Analytics: Organizations are increasingly adopting open-source databases with integrated analytics capabilities, like NoSQL and NewSQL databases, as a means of instantly obtaining actionable insights from their data.

The Graph Database Market size was valued at USD 1.9 USD billion in 2023 and is projected to reach USD 7.91 USD billion by 2032, exhibiting a CAGR of 22.6 % during the forecast period. A graph database is one form of NoSQL database that contains and represents relationships as graphs. Graph databases do not presuppose the data as relations as most contemporary relational databases do, applying nodes, edges, and properties instead. The primary types include property graphs that permit attributes on the nodes and edges and RDF triplestores that center on subject-predicate-object triplets. Some of the features include; the method's ability to traverse relationships at high rates, the schema change is easy and the method is scalable. Some of the familiar use cases are social media, recommendations, anomalies or fraud detection, and knowledge graphs where the relationships are complex and require higher comprehension. These databases are considered valuable where the future connection between the items of data is as significant as the data themselves. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Adverse Health Effect May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Data Fabric Market size was valued at around USD 2.69 billion in 2024 and is expected to reach USD 8.22 billion by 2030, growing at a CAGR of around 20.46% from 2025 to 30.

Market Research Intellect presents the Database Servers Market Report-estimated at USD 12.5 billion in 2024 and predicted to grow to USD 24.7 billion by 2033, with a CAGR of 8.5% over the forecast period. Gain clarity on regional performance, future innovations, and major players worldwide.

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Business Information Market Size 2025-2029

The business information market size is forecast to increase by USD 79.6 billion, at a CAGR of 7.3% between 2024 and 2029.

The market is characterized by the increasing demand for customer-centric solutions as enterprises adapt to evolving customer preferences. This shift necessitates the provision of real-time, accurate, and actionable insights to facilitate informed decision-making. However, this market landscape is not without challenges. The threat of data misappropriation and theft looms large, necessitating robust security measures to safeguard sensitive business information. As businesses continue to digitize their operations and rely on external data sources, ensuring data security becomes a critical success factor. Companies must invest in advanced security technologies and implement stringent data protection policies to mitigate these risks. Navigating this complex market requires a strategic approach that balances the need for customer-centric solutions with the imperative to secure valuable business data.

What will be the Size of the Business Information Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In today's data-driven business landscape, the continuous and evolving nature of market dynamics plays a pivotal role in shaping various sectors. Data integration solutions enable seamless data flow between different systems, enhancing cloud-based business applications' functionality. Data quality management ensures data accuracy and consistency, crucial for strategic planning and customer segmentation. Data infrastructure, data warehousing, and data pipelines form the backbone of business intelligence, facilitating data storytelling and digital transformation. Data lineage and data mining reveal valuable insights, fueling data analytics platforms and business intelligence infrastructure. Data privacy regulations necessitate robust data management tools, ensuring compliance and protecting sensitive information.

Sales forecasting and business intelligence consulting offer valuable industry analysis and data-driven decision making. Data governance frameworks and data cataloging maintain order and ethics in the vast expanse of big data analytics. Machine learning algorithms, predictive analytics, and real-time analytics drive business intelligence reporting and process modeling, leading to business process optimization and financial reporting software. Sentiment analysis and marketing automation cater to customer needs, while lead generation and data ethics ensure ethical business practices. The ongoing unfolding of market activities and evolving patterns necessitate the integration of various tools and frameworks, creating a dynamic interplay that fuels business growth and innovation.

How is this Business Information Industry segmented?

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

End-user

  BFSI
  Healthcare and life sciences
  Manufacturing
  Retail
  Others


Application

  B2B
  B2C


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW). 

By End-user Insights

The bfsi segment is estimated to witness significant growth during the forecast period.

In the dynamic business landscape, data-driven insights have become essential for strategic planning and decision-making across various industries. The market caters to this demand by offering solutions that integrate and manage data from multiple sources. These include cloud-based business applications, data quality management tools, data warehousing, data pipelines, and data analytics platforms. Data storytelling and digital transformation are key trends driving the market's growth, enabling businesses to derive meaningful insights from their data. Data governance frameworks and policies are crucial components of the business intelligence infrastructure. Data privacy regulations, such as GDPR and HIPAA, are shaping the market's development.

Data mining, predictive analytics, and machine learning algorithms are increasingly being used for sales forecasting, customer segmentation, and churn prediction. Business intelligence consulting and industry analysis provide valuable insights for organizations seeking competitive advantage. Data visualization dashboards, market research databases, and data discovery tools facilitate data-driven decision making. Sentiment analysis and predictive analytics are essential for marketing automation and business

Financial Data Services Market size was valued at USD 23.3 Billion in 2023 and is projected to reach USD 42.6 Billion by 2031, growing at a CAGR of 8.1% during the forecast period 2024-2031.Global Financial Data Services Market DriversThe market drivers for the Financial Data Services Market can be influenced by various factors. These may include:The need for real-time analytics is growing: Real-time analytics are becoming more and more necessary in the financial sector due to the acceleration of data consumption. To reduce risks, make wise decisions, and enhance customer service, organizations need quick insights. Stakeholders are giving priority to solutions that enable quick data processing and analysis due to the increase in market volatility and complexity. The need for sophisticated analytical skills is driving providers of financial data services to modernize their products. As companies come to realize that using real-time data is crucial for keeping a competitive edge in a fast-paced financial climate, the competition among them to provide timely insights also boosts market growth.Growing Machine Learning and AI Adoption: Data analysis has been profoundly changed by the incorporation of AI and machine learning technology into financial data services. By enabling predictive analytics, these technologies help financial organizations make better decisions and reduce risk. Businesses can find trends that were previously invisible by automating data processing operations. This leads to more precise forecasts and improved investment plans. Furthermore, sophisticated algorithms are flexible enough to adjust to shifting circumstances, keeping organizations flexible. The increasing intricacy of financial markets necessitates the use of AI and machine learning, which in turn drives demand for sophisticated financial data services and promotes innovation in the sector.Global Financial Data Services Market RestraintsSeveral factors can act as restraints or challenges for the Financial Data Services Market. These may include:Difficulties in Regulatory Compliance: Regulations controlling data management, privacy, and financial transactions place heavy restrictions on the financial data services market. Regulations like the GDPR, CCPA, and banking industry standards like Basel III and SOX must all be complied with by organizations. Complying with these requirements frequently necessitates a significant investment in staff and compliance systems, which can be taxing, especially for smaller businesses. Regulations are dynamic, and different locations have different needs, which adds to the complexity and expense. Noncompliance not only results in monetary fines but also has the potential to harm an entity's image, so impeding market expansion.Dangers to Data Security: Threats to data security are a major impediment to the financial data services market. Because they manage sensitive data, financial institutions are often the targets of cyberattacks. Breach can lead to significant monetary losses, legal repercussions, and long-term harm to one's image. Although they can greatly increase operating expenses, investments in strong security measures like encryption, safe access protocols, and continual monitoring are crucial. Moreover, the dynamic strategies employed by cybercriminals need continuous adjustment, placing a burden on resources and detracting from the main operations of businesses. The evolution of security threats poses a challenge to preserving consumer trust, hence impeding industry expansion.

The Data Analytics Market size was valued at USD 41.05 USD billion in 2023 and is projected to reach USD 222.39 USD billion by 2032, exhibiting a CAGR of 27.3 % during the forecast period. Data Analytics can be defined as the rigorous process of using tools and techniques within a computational framework to analyze various forms of data for the purpose of decision-making by the concerned organization. This is used in almost all fields such as health, money matters, product promotion, and transportation in order to manage businesses, foresee upcoming events, and improve customers’ satisfaction. Some of the principal forms of data analytics include descriptive, diagnostic, prognostic, as well as prescriptive analytics. Data gathering, data manipulation, analysis, and data representation are the major subtopics under this area. There are a lot of advantages of data analytics, and some of the most prominent include better decision making, productivity, and saving costs, as well as the identification of relationships and trends that people could be unaware of. The recent trends identified in the market include the use of AI and ML technologies and their applications, the use of big data, increased focus on real-time data processing, and concerns for data privacy. These developments are shaping and propelling the advancement and proliferation of data analysis functions and uses. Key drivers for this market are: Rising Demand for Edge Computing Likely to Boost Market Growth. Potential restraints include: Data Security Concerns to Impede the Market Progress . Notable trends are: Metadata-Driven Data Fabric Solutions to Expand Market Growth.

Streaming Data Quality Market Outlook

According to our latest research, the global streaming data quality market size reached USD 1.84 billion in 2024, and is projected to grow at a robust CAGR of 20.7% from 2025 to 2033, reaching approximately USD 11.78 billion by 2033. This impressive growth trajectory is primarily driven by the increasing adoption of real-time analytics, the explosion of IoT devices, and the rising importance of high-quality data for business intelligence and decision-making processes.

A key growth factor for the streaming data quality market is the exponential surge in data generated by connected devices and digital platforms. Organizations across industries are shifting towards real-time data processing to gain immediate insights and maintain a competitive edge. As a result, ensuring the quality, accuracy, and reliability of streaming data has become a critical requirement. The proliferation of IoT devices, social media activity, and digital transactions contributes to the complexity and volume of data streams, compelling businesses to invest in advanced streaming data quality solutions that can handle large-scale, high-velocity information with minimal latency. The demand for such solutions is further amplified by the growing reliance on artificial intelligence and machine learning models, which require clean and trustworthy data to deliver accurate predictions and outcomes.

Another significant driver for market expansion is the tightening regulatory landscape and the need for robust data governance. Industries such as BFSI, healthcare, and government are subject to stringent compliance mandates regarding data privacy, security, and traceability. Regulatory frameworks like GDPR, HIPAA, and CCPA have made it imperative for organizations to implement real-time data quality monitoring and validation mechanisms. This has led to a surge in demand for streaming data quality platforms equipped with automated data cleansing, anomaly detection, and auditing capabilities. As organizations strive to minimize compliance risks and avoid costly penalties, the integration of streaming data quality tools into their IT infrastructure has become a strategic priority.

Furthermore, the rise of cloud computing and the shift towards hybrid and multi-cloud environments are catalyzing the adoption of streaming data quality solutions. Cloud-native architectures enable organizations to scale their data processing capabilities dynamically, supporting the ingestion, transformation, and analysis of massive data streams from various sources. The flexibility and cost-effectiveness of cloud-based deployments are particularly attractive for small and medium enterprises, enabling them to leverage enterprise-grade data quality tools without significant upfront investments. As cloud adoption continues to accelerate, vendors are innovating with AI-powered, cloud-native data quality solutions that offer seamless integration, real-time monitoring, and high scalability, further fueling market growth.

From a regional perspective, North America currently dominates the streaming data quality market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology providers, early adoption of advanced analytics, and robust digital infrastructure have positioned North America at the forefront of market growth. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding e-commerce, and increasing investments in smart city initiatives. Europe is also witnessing significant growth, particularly in sectors such as BFSI, healthcare, and manufacturing, where data quality is critical for regulatory compliance and operational excellence.

Component Analysis

The streaming data quality market is segmented by component into Software and Services. The software segment currently holds the lionÂ’s share of the market, driven by the increasing demand for sophisticated data q

Uncover actionable insights with Cloud Database Security Market forecasts, key developments, and strategic trends to drive business growth.

Comprehensive dataset containing 12 verified Marketing businesses in Missouri, United States with complete contact information, ratings, reviews, and location data.

t CompanyData.com (BoldData), we provide trusted, verified company data sourced directly from official trade registers around the world. Our global list of 22 million manufacturing companies offers unmatched access to the industrial backbone of the global economy—spanning small-scale producers to large multinational manufacturers. This comprehensive dataset is built to support everything from outreach to automation.

Each record contains rich, up-to-date firmographics, company hierarchies, and contact information, including names of key decision-makers, direct emails, mobile phone numbers, turnover estimates, and employee ranges. Our data is collected and maintained with precision to ensure the highest standards of accuracy and compliance. Whether you’re navigating the automotive, food processing, electronics, or machinery sectors, we help you connect to the right manufacturing companies across global markets.

Our manufacturing dataset powers a broad range of use cases: from KYC verification and due diligence to sales prospecting, marketing campaigns, CRM data enrichment, and AI model training. Whether you need to verify business legitimacy, expand your market reach, or automate intelligence pipelines, our data gives you the edge.

We deliver our data in the format that fits your business best: tailored bulk files, access through our self-service platform, real-time API integration, or data enrichment services that complete and refine your existing databases. Backed by a global database of 380 million verified companies and deep domain expertise, CompanyData.com helps you reach manufacturers worldwide with confidence, compliance, and strategic precision.

Flea markets, with their vibrant colors, eclectic merchandise, and unique charm, are a staple of local communities around the globe, serving as a dynamic marketplace for producers and consumers alike. Originally emerging as informal trading venues, flea markets have evolved into organized events that provide a space

The Real-time Database market has emerged as a pivotal component in the modern digital landscape, facilitating instant data retrieval and updates across a multitude of applications. This dynamic sector serves diverse industries, including e-commerce, healthcare, finance, and gaming, where the demand for immediate ac

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 Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- 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.

The Data Center Management Software market has emerged as a crucial component in the ever-evolving landscape of IT infrastructure management. With the increasing demand for efficient and reliable data centers, businesses across various sectors are leveraging this software to optimize operations, improve resource uti