Sample data for exercises in Further Adventures in Data Cleaning.

TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?

Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.

Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:

Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed

Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:

Digital Marketing and Advertising:

Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking

E-commerce and Retail:

Customer journey mapping Product recommendation enhancements Cart abandonment analysis

Media and Entertainment:

Content consumption trends Audience engagement metrics Cross-platform user behavior analysis

Financial Services:

Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis

Technology and Software:

User experience optimization Feature adoption tracking Competitive intelligence

Market Research and Consulting:

Consumer behavior studies Industry trend analysis Digital transformation strategies

Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:

Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.

By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:

Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.

Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...

The Baby Bottle Cleaning Solution market has emerged as an essential segment within the broader baby care industry, reflecting the growing concerns of parents regarding hygiene and safety for their infants. As the demand for clean and sanitized baby feeding products surges, these specialized cleaning solutions play

The Data Quality Management (DQM) market is experiencing robust growth, driven by the increasing volume and velocity of data generated across various industries. Businesses are increasingly recognizing the critical need for accurate, reliable, and consistent data to support critical decision-making, improve operational efficiency, and comply with stringent data regulations. The market is estimated to be valued at $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of cloud-based DQM solutions, the expanding use of advanced analytics and AI in data quality processes, and the growing demand for data governance and compliance solutions. The market is segmented by deployment (cloud, on-premises), organization size (small, medium, large enterprises), and industry vertical (BFSI, healthcare, retail, etc.), with the cloud segment exhibiting the fastest growth. Major players in the DQM market include Informatica, Talend, IBM, Microsoft, Oracle, SAP, SAS Institute, Pitney Bowes, Syncsort, and Experian, each offering a range of solutions catering to diverse business needs. These companies are constantly innovating to provide more sophisticated and integrated DQM solutions incorporating machine learning, automation, and self-service capabilities. However, the market also faces some challenges, including the complexity of implementing DQM solutions, the lack of skilled professionals, and the high cost associated with some advanced technologies. Despite these restraints, the long-term outlook for the DQM market remains positive, with continued expansion driven by the expanding digital transformation initiatives across industries and the growing awareness of the significant return on investment associated with improved data quality.

The Automatic Airjet Cleaning Machine market is experiencing significant transformation, driven by increasing demand for efficient and effective cleaning solutions across various industries, including manufacturing, healthcare, and hospitality. These machines utilize advanced airjet technology to deliver high-pressu

Data Quality Management Service Market Outlook

The global data quality management service market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach USD 5.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.1% during the forecast period. The primary growth factor driving this market is the increasing volume of data being generated across various industries, necessitating robust data quality management solutions to maintain data accuracy, reliability, and relevance.

One of the key growth drivers for the data quality management service market is the exponential increase in data generation due to the proliferation of digital technologies such as IoT, big data analytics, and AI. Organizations are increasingly recognizing the importance of maintaining high data quality to derive actionable insights and make informed business decisions. Poor data quality can lead to significant financial losses, inefficiencies, and missed opportunities, thereby driving the demand for comprehensive data quality management services.

Another significant growth factor is the rising regulatory and compliance requirements across various industry verticals such as BFSI, healthcare, and government. Regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) necessitate organizations to maintain accurate and high-quality data. Non-compliance with these regulations can result in severe penalties and damage to the organization’s reputation, thus propelling the adoption of data quality management solutions.

Additionally, the increasing adoption of cloud-based solutions is further fueling the growth of the data quality management service market. Cloud-based data quality management solutions offer scalability, flexibility, and cost-effectiveness, making them an attractive option for organizations of all sizes. The availability of advanced data quality management tools that integrate seamlessly with existing IT infrastructure and cloud platforms is encouraging enterprises to invest in these services to enhance their data management capabilities.

From a regional perspective, North America is expected to hold the largest share of the data quality management service market, driven by the early adoption of advanced technologies and the presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, owing to the rapid digital transformation, increasing investments in IT infrastructure, and growing awareness about the importance of data quality management in enhancing business operations and decision-making processes.

Component Analysis

The data quality management service market is segmented by component into software and services. The software segment encompasses various data quality tools and platforms that help organizations assess, improve, and maintain the quality of their data. These tools include data profiling, data cleansing, data enrichment, and data monitoring solutions. The increasing complexity of data environments and the need for real-time data quality monitoring are driving the demand for sophisticated data quality software solutions.

Services, on the other hand, include consulting, implementation, and support services provided by data quality management service vendors. Consulting services assist organizations in identifying data quality issues, developing data governance frameworks, and implementing best practices for data quality management. Implementation services involve the deployment and integration of data quality tools with existing IT systems, while support services provide ongoing maintenance and troubleshooting assistance. The growing need for expert guidance and support in managing data quality is contributing to the growth of the services segment.

The software segment is expected to dominate the market due to the continuous advancements in data quality management tools and the increasing adoption of AI and machine learning technologies for automated data quality processes. Organizations are increasingly investing in advanced data quality software to streamline their data management operations, reduce manual intervention, and ensure data accuracy and consistency across various data sources.

Moreover, the services segment is anticipated to witness significant growth during the forecast period, driven by the increasing demand for professional services that can help organizations address complex dat

The scientific community has entered an era of big data. However, with big data comes big responsibilities, and best practices for how data are contributed to databases have not kept pace with the collection, aggregation, and analysis of big data. Here, we rigorously assess the quantity of data for specific leaf area (SLA) available within the largest and most frequently used global plant trait database, the TRY Plant Trait Database, exploring how much of the data were applicable (i.e., original, representative, logical, and comparable) and traceable (i.e., published, cited, and consistent). Over three-quarters of the SLA data in TRY either lacked applicability or traceability, leaving only 22.9% of the original data usable compared to the 64.9% typically deemed usable by standard data cleaning protocols. The remaining usable data differed markedly from the original for many species, which led to altered interpretation of ecological analyses. Though the data we consider here make up onl..., SLA data was downlaoded from TRY (traits 3115, 3116, and 3117) for all conifer (Araucariaceae, Cupressaceae, Pinaceae, Podocarpaceae, Sciadopityaceae, and Taxaceae), Plantago, Poa, and Quercus species. The data has not been processed in any way, but additional columns have been added to the datset that provide the viewer with information about where each data point came from, how it was cited, how it was measured, whether it was uploaded correctly, whether it had already been uploaded to TRY, and whether it was uploaded by the individual who collected the data., , There are two additional documents associated with this publication. One is a word document that includes a description of each of the 120 datasets that contained SLA data for the four plant groups within the study (conifers, Plantago, Poa, and Quercus). The second is an excel document that contains the SLA data that was downloaded from TRY and all associated metadata.

Missing data codes: NA and N/A

The use of in vitro new approach methodologies (NAMs) to assess respiratory irritation depends on several factors, including the specifics of exposure methods and cell/tissue-based test systems. This topic was examined in the context of human health risk assessment for cleaning products at a 1-day public workshop held on 2 March 2023, organized by the American Cleaning Institute® (ACI). The goals of this workshop were to (1) review in vitro NAMs for evaluation of respiratory irritation, (2) examine different perspectives on current challenges and suggested solutions, and (3) publish a manuscript of the proceedings. Targeted sessions focused on exposure methods, in vitro cell/tissue test systems, and application to human health risk assessment. The importance of characterization of assays and development of reporting standards was noted throughout the workshop. The exposure methods session emphasized that the appropriate exposure system design depends on the purpose of the assessment. This is particularly important given the many dosimetry and technical considerations affecting relevance and translation of results to human exposure scenarios. Discussion in the in vitro cell/tissue test systems session focused on the wide variety of cell systems with varying suitability for evaluating key mechanistic steps, such as molecular initiating events (MIEs) and key events (KEs) likely present in any putative respiratory irritation adverse outcome pathway (AOP). This suggests the opportunity to further develop guidance around in vitro cell/tissue test system endpoint selection, assay design, characterization and validation, and analytics that provide information about a given assay’s utility. The session on applications for human health protection emphasized using mechanistic understanding to inform the choice of test systems and integration of NAMs-derived data with other data sources (e.g., physicochemical properties, exposure information, and existing in vivo data) as the basis for in vitro to in vivo extrapolation. In addition, this group noted a need to develop procedures to align NAMs-based points of departure (PODs) and uncertainty factor selection with current human health risk assessment methods, together with consideration of elements unique to in vitro data. Current approaches are described and priorities for future characterization of in vitro NAMs to assess respiratory irritation are noted.

Abstract

1.1 Preambule

This study was funded by Google.org. The study began in 2008 and will end in 2011. Field work was done between May and July 2009 for the first round and February and March 2010 for the second round. The purpose of this field report is (1) to document how the data was collected; (2) to act as a reference to those who will be writing scientific papers, processing, and analyzing the data; and (30 consolidate the findings for purposes of sharing with key stakeholders including teachers and Ministry of Education. The report has five sections: Section 1 presents the study background. Section two presents data collection issues. Section three outlines the district and individual school reports. Section four captures the challenges experienced. Section five outlines the lessons learnt and recommendations for future classroom-based studies.

1.2 Purpose of the study

The purpose of this study was to examine the teaching process and generate information relevant to objective policy advice on the quality of teaching and learning. The intention is that by sharing the evidence generated by this study with policy makers, it is hoped that it will lead to the improvement of the quality of teaching in primary schools in Kenya. It sought to understand whether classroom interactions, including how aspects such as 'Opportunity to Learn' explain learning achievement.

1.3 Research questions guiding the study

The following are the main research questions guiding the study. However, the data collected is rich on teaching practice information and will make it possible to answer several other research questions.

a). What are the differences and similarities in teaching practice among teachers in high and low performance schools?

b). Does the observed teaching practice explain student achievement?

c). Do teacher attributes explain student's learning achievement?

d). What policy recommendations on teaching practices can improve the quality of teaching in primary education?

Based on the guiding research questions, the following research papers have been conceptualized and are being finalized for publication as publicly available and accessible APHRC Working Papers.

a) Do teachers who have a good understanding of maths demonstrate better teaching practice in the classrooms?

b) Does teaching practice explain differences in learner achievement in low and high performing schools?

c) Social relations as predictors of achievement in maths in Kenya primary schools.

Other questions that the data may help to answer

a) Do opportunities to learn (measured by teacher absenteeism, curriculum completion, and bullying and class size) explain learning gains.

b) To what extent do student characteristics, classroom sitting arrangements and classroom participation explain learning gains?

c) Assess whether female and male teachers differ in mathematics teaching and content knowledge, and whether this is reflected in pupils' mathematics performance.

Geographic coverage

Six districts in Kenya: Embu, Nairobi, Gucha, Garissa, Muranga and Baringo and 12 schools in each district

Analysis unit

Pupils

Schools

Universe

Grade 6 pupils in the selected schools, the headteacher and Math, English and Science Teachers

Sampling procedure

The target was districts that had consistently perfomed at the bottom, middle and top for 5 consective years. The selection of the best and poor performing districts and schools, the Kenya Certificate of Primary Education (KCPE) results of the last five years available were used to rank districts (nationally) and schools (at district level). School performance in national examinations (a proxy indicator for student achievement) in Kenya varies by geographical and ecological regions of the country. Based on the distribution of school mean scores in a district, schools were categorized as low performing and high performing schools in any given year.

Specifically, six districts in Kenya, two that have consistently been ranked in the bottom 10% of the KCPE examinations over the past 4 years, two that have been consistently ranked within the middle 20% and another two that have consistently been ranked in the top 10% over the same period were selected for the study. A total of 72 schools, 12 in each of the six districts were randomly selected for the study. The schools selected for the study included six that had consistently been ranked in the bottom 20%, and six that had consistently been ranked in the top 20%. A further selection criterion for the schools ensured a mix of rural, peri-urban and urban schools in the sample. While taking a national representation in to account, the sample size was influenced by resource availability.

In the selected schools, grade six pupils were included. In case of multi-streams one grade was randomly selected.

Mode of data collection

Face-to-face [f2f]

Research instrument

Survey instruments:

· Head teacher questionnaire: This instrument solicited information on school management, staffing, enrolment and parental participation in school affairs, among others.

· Teacher questionnaire: This solicited for information on biodata, qualification and training, discipline and syllabus coverage. The questionnaire was administered to grade six Maths, English and Science teachers.

· Learner questionnaire: The questionnaire solicited information on social economic background of the grade six learners and the school environment. This questionnaire was administered to grade six pupils in the selected schools.

Assessment tools:

· Mathematics teacher assessment tool, for grade six math teachers.

· Learner mathematics assessment tool, for pupils in the selected grade six streams.

Classroom observation and checklist tools:

· Classroom observation checklist: The checklist solicited information on availability of relevant textbooks, teacher and student made teaching and learning materials, other teaching resources, enrolment, learner absenteeism and lesson preparation.

· Opportunity to Learn (OTL) form: This form collected information from grade six exercise books that a learner used between January and November 2009. The information collected included date when the lesson was taught, and the main topic and subtopic as defined in grade six subject syllabus. In the absence of a main topic or subtopic, some contents of the lesson were recorded. These were later to be matched with main topic and subtopic from the s

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding

b) During data entry

c) Structure checking and completeness

d) Secondary editing

Response rate

Total of 72 schools, all the head teachers interviwed, 2436 pupils, 213 teachers

Snapshot img

Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for business intelligence and AI-powered insights. Companies are recognizing the value of transforming complex data into easily digestible visual representations to inform strategic decision-making. However, this market faces challenges as data complexity and massive data volumes continue to escalate. Organizations must invest in advanced data visualization tools to effectively manage and analyze their data to gain a competitive edge. The ability to automate data visualization processes and integrate AI capabilities will be crucial for companies to overcome the challenges posed by data complexity and volume. By doing so, they can streamline their business operations, enhance data-driven insights, and ultimately drive growth in their respective industries.

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

Request Free SampleIn today's data-driven business landscape, the market continues to evolve, integrating advanced capabilities to support various sectors in making informed decisions. Data storytelling and preparation are crucial elements, enabling organizations to effectively communicate complex data insights. Real-time data visualization ensures agility, while data security safeguards sensitive information. Data dashboards facilitate data exploration and discovery, offering data-driven finance, strategy, and customer experience. Big data visualization tackles complex datasets, enabling data-driven decision making and innovation. Data blending and filtering streamline data integration and analysis. Data visualization software supports data transformation, cleaning, and aggregation, enhancing data-driven operations and healthcare. On-premises and cloud-based solutions cater to diverse business needs. Data governance, ethics, and literacy are integral components, ensuring data-driven product development, government, and education adhere to best practices. Natural language processing, machine learning, and visual analytics further enrich data-driven insights, enabling interactive charts and data reporting. Data connectivity and data-driven sales fuel business intelligence and marketing, while data discovery and data wrangling simplify data exploration and preparation. The market's continuous dynamism underscores the importance of data culture, data-driven innovation, and data-driven HR, as organizations strive to leverage data to gain a competitive edge.

How is this Data Visualization Tools Industry segmented?

The data visualization tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSMexicoEuropeFranceGermanyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced notable expansion as businesses across diverse sectors acknowledge the significance of data analysis and representation to uncover valuable insights and inform strategic decisions. Data visualization plays a pivotal role in this domain. On-premises deployment, which involves implementing data visualization tools within an organization's physical infrastructure or dedicated data centers, is a popular choice. This approach offers organizations greater control over their data, ensuring data security, privacy, and adherence to data governance policies. It caters to industries dealing with sensitive data, subject to regulatory requirements, or having stringent security protocols that prohibit cloud-based solutions. Data storytelling, data preparation, data-driven product development, data-driven government, real-time data visualization, data security, data dashboards, data-driven finance, data-driven strategy, big data visualization, data-driven decision making, data blending, data filtering, data visualization software, data exploration, data-driven insights, data-driven customer experience, data mapping, data culture, data cleaning, data-driven operations, data aggregation, data transformation, data-driven healthcare, on-premises data visualization, data governance, data ethics, data discovery, natural language processing, data reporting, data visualization platforms, data-driven innovation, data wrangling, data-driven s

Abstract

The 2016 Integrated Household Panel Survey (IHPS) was launched in April 2016 as part of the Malawi Fourth Integrated Household Survey fieldwork operation. The IHPS 2016 targeted 1,989 households that were interviewed in the IHPS 2013 and that could be traced back to half of the 204 enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11. The 2019 IHPS was launched in April 2019 as part of the Malawi Fifth Integrated Household Survey fieldwork operations targeting the 2,508 households that were interviewed in 2016. The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. Available as part of this project is the IHPS 2019 data, the IHPS 2016 data as well as the rereleased IHPS 2010 & 2013 data including only the subsample of 102 EAs with updated panel weights. Additionally, the IHPS 2016 was the first survey that received complementary financial and technical support from the Living Standards Measurement Study – Plus (LSMS+) initiative, which has been established with grants from the Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust Fund for Statistical Capacity Building, and the International Fund for Agricultural Development, and is implemented by the World Bank Living Standards Measurement Study (LSMS) team, in collaboration with the World Bank Gender Group and partner national statistical offices. The LSMS+ aims to improve the availability and quality of individual-disaggregated household survey data, and is, at start, a direct response to the World Bank IDA18 commitment to support 6 IDA countries in collecting intra-household, sex-disaggregated household survey data on 1) ownership of and rights to selected physical and financial assets, 2) work and employment, and 3) entrepreneurship – following international best practices in questionnaire design and minimizing the use of proxy respondents while collecting personal information. This dataset is included here.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Children under 5 years
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop

Universe

The IHPS 2016 and 2019 attempted to track all IHPS 2013 households stemming from 102 of the original 204 baseline panel enumeration areas as well as individuals that moved away from the 2013 dwellings between 2013 and 2016 as long as they were neither servants nor guests at the time of the IHPS 2013; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sub-sample of IHS3 2010 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS 2013) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban. The IHPS 2013 main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

Given budget and resource constraints, for the IHPS 2016 the number of sample EAs in the panel was reduced to 102 out of the 204 EAs. As a result, the domains of analysis are limited to the national, urban and rural areas. Although the results of the IHPS 2016 cannot be tabulated by region, the stratification of the IHPS by region, urban and rural strata was maintained. The IHPS 2019 tracked all individuals 12 years or older from the 2016 households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data Entry Platform To ensure data quality and timely availability of data, the IHPS 2019 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHPS 2019, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer that the NSO provided. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

Data Management The IHPS 2019 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHPS 2019 Interviews were mainly collected in “sample” mode (assignments generated from headquarters) and a few in “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample. This hybrid approach was necessary to aid the tracking operations whereby an enumerator could quickly create a tracking assignment considering that they were mostly working in areas with poor network connection and hence could not quickly receive tracking cases from Headquarters.

The range and consistency checks built into the application was informed by the LSMS-ISA experience with the IHS3 2010/11, IHPS 2013 and IHPS 2016. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (the NSO management) assigned work to the supervisors based on their regions of coverage. The supervisors then made assignments to the enumerators linked to their supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHPS 2019 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to Stata for other consistency checks, data cleaning, and analysis.

Data Cleaning The data cleaning process was done in several stages over the course of fieldwork and through preliminary analysis. The first stage of data cleaning was conducted in the field by the field-based field teams utilizing error messages generated by the Survey Solutions application when a response did not fit the rules for a particular question. For questions that flagged an error, the enumerators were expected to record a comment within the questionnaire to explain to their supervisor the reason for the error and confirming that they double checked the response with the respondent. The supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some supervisors preferred to review completed interviews on the tablets so they would review prior to syncing but still record the notes in the supervisor account and reject questionnaires accordingly. The second stage of data cleaning was also done in the field, and this resulted from the additional error reports generated in Stata, which were in turn sent to the field teams via email or DropBox. The field supervisors collected reports for their assignments and in coordination with the enumerators reviewed, investigated, and collected errors. Due to the quick turn-around in error reporting, it was possible to conduct call-backs while the team was still operating in the EA when required. Corrections to the data were entered in the rejected questionnaires and sent back to headquarters.

The data cleaning process was done in several stages over the course of the fieldwork and through preliminary analyses. The first stage was during the interview itself. Because CAPI software was used, as enumerators asked the questions and recorded information, error messages were provided immediately when the information recorded did not match previously defined rules for that variable. For example, if the education level for a 12 year old respondent was given as post graduate. The second stage occurred during the review of the questionnaire by the Field Supervisor. The Survey Solutions software allows errors to remain in the data if the enumerator does not make a correction. The enumerator can write a comment to explain why the data appears to be incorrect. For example, if the previously mentioned 12 year old was, in fact, a genius who had completed graduate studies. The next stage occurred when the data were transferred to headquarters where the NSO staff would again review the data for errors and verify the comments from the

The Cleaning-in-Place (CIP) System market has witnessed significant growth, driven by the rising demand for sanitation and hygiene across various industries, particularly in food and beverage, pharmaceuticals, and biotechnology. CIP systems are automated, effective cleaning solutions that enhance operational efficie

Data Quality Software and Solutions Market Outlook

The global data quality software and solutions market size was valued at $2.5 billion in 2023, and it is projected to reach $7.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.5% over the forecast period. This significant growth is driven by factors such as the increasing amount of data generated across various industries, the rising need for data accuracy and consistency, and advancements in artificial intelligence and machine learning technologies.

One of the primary growth drivers for the data quality software and solutions market is the exponential increase in data generation across different industry verticals. With the advent of digital transformation, businesses are experiencing unprecedented volumes of data. This surge necessitates robust data quality solutions to ensure that data is accurate, consistent, and reliable. As organizations increasingly rely on data-driven decision-making, the demand for data quality software is expected to escalate, thereby propelling market growth.

Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) into data quality solutions has significantly enhanced their capabilities. AI and ML algorithms can automate data cleansing processes, identify patterns, and predict anomalies, which improves data accuracy and reduces manual intervention. The continuous advancements in these technologies are expected to further bolster the adoption of data quality software, as businesses seek to leverage AI and ML for optimized data management.

The growing regulatory landscape concerning data privacy and security is another crucial factor contributing to market growth. Governments and regulatory bodies across the world are implementing stringent data protection laws, compelling organizations to maintain high standards of data quality. Compliance with these regulations not only helps in avoiding hefty penalties but also enhances the trust and credibility of businesses. Consequently, companies are increasingly investing in data quality solutions to ensure adherence to regulatory requirements, thereby driving market expansion.

Regionally, North America is expected to dominate the data quality software and solutions market, followed by Europe and Asia Pacific. North America's leadership position can be attributed to the early adoption of advanced technologies, a high concentration of data-driven enterprises, and robust infrastructure. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest CAGR over the forecast period, spurred by the rapid digitization of economies, increasing internet penetration, and the growing focus on data analytics and management.

Component Analysis

In the data quality software and solutions market, the component segment is bifurcated into software and services. The software segment encompasses various solutions designed to improve data accuracy, consistency, and reliability. These software solutions include data profiling, data cleansing, data matching, and data enrichment tools. The increasing complexity of data management and the need for real-time data quality monitoring are driving the demand for comprehensive software solutions. Businesses are investing in advanced data quality software that integrates seamlessly with their existing data infrastructure, providing actionable insights and enhancing operational efficiency.

The services segment includes professional and managed services aimed at helping organizations implement, maintain, and optimize their data quality initiatives. Professional services comprise consulting, implementation, and training services, wherein experts assist businesses in deploying data quality solutions tailored to their specific needs. Managed services, on the other hand, involve outsourcing data quality management to third-party providers, allowing organizations to focus on their core competencies while ensuring high data quality standards. The growing reliance on data quality services is attributed to the increasing complexity of data ecosystems and the need for specialized expertise.

Companies are increasingly seeking professional services to navigate the complexities associated with data quality management. These services provide valuable insights into best practices, enabling organizations to establish effective data governance frameworks. Moreover, the demand for managed services is rising as businesses look to offload the burden of continuous data quality monitoring and maintenance. By outsourcing these functions, organ

Removal of leaf-litter may help municipalities reduce phosphorus loads. Catch-basin cleaning and street cleaning are two commonly used Best Management Practices that could be modified to remove leaves and qualify for additional load-reduction credits. This Data Release contains four tab-delimited .txt files containing additional information about the study area, characteristics of municipal street solids, and load-reduction estimates from increased catch basin and street cleaning practices that are not available in the associated report. This Data Release also contains a compressed file, "EngBrk_ModelArchive.7z", which archives the model developed and used for the project. The four .txt table files are: (1) "VT_LU_data.txt", which includes the area, in acres and percent, for each land-use type within the nine participating municipalities and within the Englesby Brook basin (based on the National Oceanic and Atmospheric Administration's (NOAA) 2006 C-CAP Regional Land Cover); (2) "CB_sample_characteristics.txt", which describes the physical characteristics of samples collected from piles of catch-basin solids for nine municipalities in this study (September to November 2017 and April to November 2018); (3) "SC_sample_characteristics.txt", which describes the physical characteristics of samples collected from piles of street-cleaning solids for nine municipalities in this study (September to November 2017 and April to November 2018); and (4) "Estimated P-load reductions.txt", which contains estimated phosphorus load-reduction credits by using individual Soil Water Assessment Tool (SWAT) drainage areas for street cleaning and street cleaning with leaf management practices for the seven participating Municipal Separate Storm Sewer Systems (MS4s) municipalities in northwestern Vermont. The cities of Barre and Montpelier currently do not have to meet MS4 permit requirements. Information from the NOAA's 2006 C-CAP Regional Land Cover (https://data.noaa.gov/dataset/dataset/noaas-coastal-change-analysis-program-c-cap-2006-regional-land-cover-data-coastal-united-state1) and the Vermont Center for Geographic Information (https://vcgi.vermont.gov/) were used to characterize land use within each of the nine municipalities in the central and northwestern Vermont, study area, and the partially urbanized Englesby Brook basin located in Burlington and South Burlington, Vermont, that drains into Lake Champlain. The compressed file "EngBrk_ModelArchive.7z" represents the model archive that contains 31 files associated with the Englesby Brook model using the Source Loading and Management Model for Windows (WinSLAMM) version 10.4.0.

In order to effectively fight against aquatic invasive species, the Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks has formulated a series of best practices. Among these good practices, cleaning watercraft greatly reduces the risks of dispersal of aquatic invasive species, whether animals or plants. In recent years, several municipalities in Quebec have installed cleaning stations (permanent or mobile), near water bodies, in order to protect them from the arrival of new invasive species or to reduce the risks of dispersion. The MELCCFP participated in this effort by funding several cleaning stations through its funding program. In order to facilitate the planning of nautical activities for citizens, it is important to make the location of these cleaning stations available. Thus, this dataset lists the location, address and name of known cleaning stations in Quebec. WARNINGS: * The identification of these stations was carried out in collaboration with the Reunification of organizations of watersheds of Quebec and the Laurentides Regional Environment Council, as part of projects funded by Fisheries and Oceans Canada, as well as the organizations managing the stations. There may be a time lag between the position listed in the data set and the actual location of the station. If you notice such a discrepancy, please inform the data set managers so that the necessary corrections can be made. * The “mobile” cleaning stations were positioned at their most frequent locations during the year. Depending on the season and current events in a locality, mobile stations may not be parked at the location listed. Update * If you believe that information is incorrect for one of the stations, please send DEFA@mffp.gouv.qc.ca the information that needs to be changed to the information that needs to be changed, including the unique identifier of the station (Station_Identifier field) in question.This third party metadata element was translated using an automated translation tool (Amazon Translate).

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Procurement Analytics Market Size 2025-2029

The procurement analytics market size is forecast to increase by USD 5.81 billion at a CAGR of 19.8% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for cost reduction and efficiency in business operations. Companies are recognizing the value of leveraging data-driven insights to optimize their procurement processes, leading to substantial savings and improved performance. A key trend fueling market expansion is the integration of Artificial Intelligence (AI) and predictive analytics, enabling more accurate forecasting and automated decision-making. However, market growth is tempered by challenges such as data security and privacy concerns, which require robust data protection measures to ensure the confidentiality and integrity of sensitive information. According to data analytics, consumers are increasingly seeking seamless shopping experiences across multiple channels, leading retailers to invest in omnichannel strategies.
To capitalize on market opportunities and navigate challenges effectively, companies must prioritize data security, invest in advanced analytics technologies, and stay informed of regulatory developments. However, the market faces challenges related to procurement, particularly due to increasing environmental regulations and digital paper, and the shift towards digital transformation. By doing so, they can harness the power of procurement analytics to streamline operations, reduce costs, and gain a competitive edge.

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

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In the dynamic market, user experience (UX) plays a pivotal role in driving adoption of advanced data mining tools. Sustainable procurement practices are increasingly integrated into analytics solutions, ensuring data cleansing aligns with social responsibility. Agile methodologies and blockchain technology enable continuous improvement through real-time analytics and predictive modeling. Data visualization tools, custom reports, and interactive dashboards facilitate collaborative analytics, allowing stakeholders to make informed decisions. Procurement maturity models, mobile analytics, and procurement governance ensure data quality management and adherence to best practices. Deep learning and automated decision making streamline processes, while data validation and predictive modeling enhance accuracy.
Role-based access control and change management ensure data security and efficiency. Cloud-based analytics and data enrichment provide scalability and flexibility. Procurement ethics and user interface (UI) design further enhance the value of analytics solutions, ensuring a seamless user experience and ethical decision-making. Overall, the market is evolving to meet the needs of modern businesses, offering innovative solutions for data management and strategic sourcing. Machine learning algorithms optimize supply chain management, enabling retailers to anticipate demand and maintain efficient operations. Digital marketing strategies, including influencer marketing and content marketing, engage customers and drive sales. E-commerce platforms and online retail offer convenience, while virtual reality shopping and augmented reality applications enhance the shopping experience.

How is this Procurement Analytics Industry segmented?

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

Deployment

  On-premises
  Cloud


Business Segment

  Large enterprises
  SMEs


Component

  Solutions
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period. On-premises procurement analytics solutions refer to software systems installed and operated within an organization's data centers or IT infrastructure. These solutions offer businesses complete ownership and control over procurement data, making them suitable for industries with stringent data security, compliance, or customization requirements, such as finance, defense, and government. The primary advantage of on-premises deployment is the ability to deeply customize the system to align with specific organizational processes. Since the solution resides on internal servers, IT teams can tailor features, workflows, and integrations more freely than with most cloud-based options. Data warehousing plays a crucial role in procurement analytics by collecting, storing, and managing large volumes

The hospital cleaning services market is experiencing robust growth, driven by increasing healthcare-associated infections (HAIs) and stringent hygiene regulations. The market's value is estimated at $15 billion in 2025, projected to grow at a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This growth is fueled by several factors, including the rising number of hospital beds globally, an aging population requiring more healthcare services, and increased awareness of infection control best practices. Technological advancements, such as the adoption of automated cleaning systems and the use of environmentally friendly disinfectants, are further contributing to market expansion. However, challenges remain, including the high cost of specialized cleaning equipment and trained personnel, and the need for continuous training to keep pace with evolving infection control protocols. The market is segmented by service type (disinfection, sterilization, waste management), cleaning technology (manual, automated), and hospital type (general, specialized). Leading players such as ServiceMaster Clean, Jani-King, and Clean Team are consolidating their market share through acquisitions and expansion into new geographical regions. This competitive landscape is driving innovation and improved service offerings. The forecast period of 2025-2033 anticipates continued growth, with a projected market value exceeding $25 billion by 2033. This expansion will be primarily driven by emerging economies where healthcare infrastructure is rapidly developing, and increasing demand for specialized cleaning services in critical care units and operating theaters. Key regional variations exist, with North America and Europe currently dominating the market, but significant growth potential is expected in Asia-Pacific and Latin America, fueled by rising healthcare spending and a focus on enhancing hygiene standards. To maintain a competitive edge, companies are investing in research and development to deliver advanced cleaning solutions and improve the efficiency and effectiveness of their services. A focus on sustainability and reducing environmental impact is also becoming increasingly important, influencing the adoption of eco-friendly cleaning products and practices.

The datasets presented here were partially used in “Formulation and MIP-heuristics for the lot sizing and scheduling problem with temporal cleanings” (Toscano, A., Ferreira, D. , Morabito, R. , Computers & Chemical Engineering) [1], in “A decomposition heuristic to solve the two-stage lot sizing and scheduling problem with temporal cleaning” (Toscano, A., Ferreira, D. , Morabito, R. , Flexible Services and Manufacturing Journal) [2], and in “A heuristic approach to optimize the production scheduling of fruit-based beverages” (Toscano et al., Gestão & Produção, 2020) [3]. In fruit-based production processes, there are two production stages: preparation tanks and production lines. This production process has some process-specific characteristics, such as temporal cleanings and synchrony between the two production stages, which make optimized production planning and scheduling even more difficult. In this sense, some papers in the literature have proposed different methods to solve this problem. To the best of our knowledge, there are no standard datasets used by researchers in the literature in order to verify the accuracy and performance of proposed methods or to be a benchmark for other researchers considering this problem. The authors have been using small data sets that do not satisfactorily represent different scenarios of production. Since the demand in the beverage sector is seasonal, a wide range of scenarios enables us to evaluate the effectiveness of the proposed methods in the scientific literature in solving real scenarios of the problem. The datasets presented here include data based on real data collected from five beverage companies. We presented four datasets that are specifically constructed assuming a scenario of restricted capacity and balanced costs. These dataset is supplementary data for the submitted paper to Data in Brief [4]. [1] Toscano, A., Ferreira, D., Morabito, R., Formulation and MIP-heuristics for the lot sizing and scheduling problem with temporal cleanings, Computers & Chemical Engineering. 142 (2020) 107038. Doi: 10.1016/j.compchemeng.2020.107038. [2] Toscano, A., Ferreira, D., Morabito, R., A decomposition heuristic to solve the two-stage lot sizing and scheduling problem with temporal cleaning, Flexible Services and Manufacturing Journal. 31 (2019) 142-173. Doi: 10.1007/s10696-017-9303-9. [3] Toscano, A., Ferreira, D., Morabito, R., Trassi, M. V. C., A heuristic approach to optimize the production scheduling of fruit-based beverages. Gestão & Produção, 27(4), e4869, 2020. https://doi.org/10.1590/0104-530X4869-20. [4] Piñeros, J., Toscano, A., Ferreira, D., Morabito, R., Datasets for lot sizing and scheduling problems in the fruit-based beverage production process. Data in Brief (2021).

The US carpet cleaning industry has faced significant volatility due to turbulent changes in disposable income and consumer confidence. Additional issues have arisen from substantial changes in consumer preferences in the industry. Despite this, the industry has maintained a steady performance in recent years, establishing itself as an essential service for households and businesses. Generating steady revenue over the past five years and home to a remarkably fragmented operator landscape, the industry thrives on routine demand from homeowners seeking healthier living environments and businesses needing spotless spaces. However, competition is fierce, barriers to entry are low and operators, even large franchises like Stanley Steemer, struggle to command a 5 to 6% market share, making local reputation and word-of-mouth recommendations crucial for success. Industry revenue has been expanding at a CAGR of 2.7% over the past five years and is expected to reach $6.9 billion in 2025, when revenue will rise by an estimated 1.1%. Despite revenue growth over the past five years, analysis reveals a slowdown and decline in profit margin. Profit has dropped from 10.1% in 2020 to 9.6% in 2025, impacted by rising costs and intensifying competition, especially from DIY cleaning solutions and substitute flooring options like hardwood. Demand for eco-friendly and non-toxic cleaning methods has shifted the playing field, as more companies adopt green practices to meet consumer preference shifts. However, this squeezes the margin even more, leading to higher supply and certification expenses. To stay competitive, many operators have added specialized services such as tile, upholstery and air duct cleaning, but the proliferation of DIY cleaning gadgets and online gig platforms has kept traditional carpet cleaning jobs under pressure. In the next five years, forecasts suggest moderate growth, with the profit margin stabilizing near 9.7%. Companies will continue streamlining operations by embracing energy-efficient equipment and mobile business models. Through 2030, operators will invest in digital marketing, automation and training to meet stricter regulations and evolving health standards, especially in urban and eco-conscious markets. As competition and substitutes continue to challenge the traditional market, businesses that diversify services, scale up green offerings and strengthen local relationships will be best positioned to overcome a maturing, increasingly competitive market. Revenue is forecast to rise at a CAGR of 2.0% over the next five years, reaching $7.6 billion in 2030.

Food Beverage Disinfection And Cleaning Market size was valued at USD 3,270.8 Million in 2023 and is projected to reach USD 4,879.5 Million by 2031, growing at a CAGR of 5.5% during the forecast period 2024-2031.

Global Food Beverage Disinfection And Cleaning Market Drivers

The market drivers for the Food Beverage Disinfection And Cleaning Market can be influenced by various factors. These may include:

Growing Awareness of Food Safety: With the rising incidence of foodborne illnesses, the awareness around food safety has significantly increased among consumers and businesses. This consciousness drives the demand for effective disinfection and cleaning solutions in food and beverage sectors. Regulatory bodies are imposing stringent standards for sanitation, necessitating advanced cleaning protocols. Consumers are becoming more knowledgeable about hygiene practices, leading to higher expectations from food service providers. Consequently, investing in efficient cleaning and disinfection products becomes a top priority for manufacturers and retailers. This trend fuels market growth as companies seek reliable solutions to maintain safety and quality in food handling. Regulatory Compliance and Standards: Governments and health organizations worldwide have implemented strict regulations concerning hygiene and sanitation in the food and beverage industry. Compliance with these regulations is crucial for brands seeking to maintain market access and uphold consumer trust. Regular inspections and audits emphasize the need for adequate cleaning and disinfection practices throughout the supply chain. The introduction of new standards, such as HACCP (Hazard Analysis Critical Control Point) and GMP (Good Manufacturing Practices), compels businesses to invest in innovative cleaning solutions. Non-compliance can lead to severe penalties, product recalls, and reputational damage, driving demand for disinfection and cleaning products. Technological Advancements: The food and beverage disinfection and cleaning market are witnessing significant technological advancements. Innovations such as automated cleaning systems, advanced surface disinfectants, and eco-friendly cleaning agents are improving the efficacy of cleaning processes. Technologies like ultraviolet (UV) light disinfection and electrostatic sprayers are gaining traction due to their efficiency and ability to cover large areas quickly. The development of smart cleaning systems, which utilize IoT connectivity for monitoring and data collection, is also enhancing operational efficiency. These advancements not only streamline cleaning but also reduce operational costs, making them attractive options for businesses in the industry. Rising Demand for Eco-Friendly Products: Consumer preference is increasingly shifting towards eco-friendly and sustainable practices in the food and beverage sector. As environmental concerns grow, businesses are seeking cleaning and disinfection products that minimize ecological impact. This trend drives research and development to create biodegradable, plant-based, or non-toxic cleaning agents without sacrificing effectiveness. Companies embracing green practices not only comply with consumer preferences but also benefit from potential cost savings and enhanced brand loyalty. The push for sustainability is thus becoming a vital market driver, compelling manufacturers to innovate and adapt their offerings to cater to environmentally conscious consumers. Increasing Global Food Production: As the global population continues to grow, the demand for food production has surged, leading to an increased need for effective cleaning and disinfection solutions. Higher food production levels create extensive supply chains where hygiene plays a critical role in preventing contamination. Both manufacturers and retailers are compelled to adopt stringent cleaning protocols to ensure product safety and quality. The rise in global food trade further amplifies this need, as consistent hygiene practices across borders become essential. This broad demand across various sectors ultimately drives the growth of the food and beverage disinfection and cleaning market, ensuring safety from farm to table.