The Data Preparation Tools market is experiencing robust growth, projected to reach a market size of $3 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 17.7% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing volume and velocity of data generated across industries necessitates efficient and effective data preparation processes to ensure data quality and usability for analytics and machine learning initiatives. The rising adoption of cloud-based solutions, coupled with the growing demand for self-service data preparation tools, is further fueling market growth. Businesses across various sectors, including IT and Telecom, Retail and E-commerce, BFSI (Banking, Financial Services, and Insurance), and Manufacturing, are actively seeking solutions to streamline their data pipelines and improve data governance. The diverse range of applications, from simple data cleansing to complex data transformation tasks, underscores the versatility and broad appeal of these tools. Leading vendors like Microsoft, Tableau, and Alteryx are continuously innovating and expanding their product offerings to meet the evolving needs of the market, fostering competition and driving further advancements in data preparation technology. This rapid growth is expected to continue, driven by ongoing digital transformation initiatives and the increasing reliance on data-driven decision-making. The segmentation of the market into self-service and data integration tools, alongside the varied applications across different industries, indicates a multifaceted and dynamic landscape. While challenges such as data security concerns and the need for skilled professionals exist, the overall market outlook remains positive, projecting substantial expansion throughout the forecast period. The adoption of advanced technologies like artificial intelligence (AI) and machine learning (ML) within data preparation tools promises to further automate and enhance the process, contributing to increased efficiency and reduced costs for businesses. The competitive landscape is dynamic, with established players alongside emerging innovators vying for market share, leading to continuous improvement and innovation within the industry.

The size and share of the market is categorized based on Application (Data cleansing tools, Data integration software, Data transformation tools, Data enrichment solutions, Data validation tools) and Product (Data preparation, Data integration, Data cleansing, Data transformation, Data enrichment) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform Industry segmented and which is the largest segment?

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

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

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

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

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The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

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

For more insights on the market share of various regions, Request F

El tamaño del mercado de la Tamaña del Mercado del Software de Limpieza de Datos por Producto, Por Aplicacia, Por Geografía, Panorama Competitive Y Pronósico y pronóstico se clasifica en función de la aplicación (migración de datos, inteligencia empresarial, mantenimiento de CRM) y producto (evaluación de calidad de datos, eliminación de datos, estandarización de datos) y regiones geógicas (North America, Europe, ASIA, ASIA, ASIA, South America, Southamera Medio Oriente y África).

Este informe proporciona información sobre el tamaño del mercado y pronostica el valor del mercado, expresado en millones de dólares, en estos segmentos definidos.

La taille du marché du marché des logiciels de nettoyage des données est classée en fonction de l'application (migration des données, intelligence commerciale, maintenance du CRM) et produit (évaluation de la qualité des données, suppression en double, normalisation des données) et Afrique).

segments.

Data Quality Management Software Market size was valued at USD 4.32 Billion in 2023 and is projected to reach USD 10.73 Billion by 2030, growing at a CAGR of 17.75% during the forecast period 2024-2030.

Global Data Quality Management Software Market Drivers

The growth and development of the Data Quality Management Software Market can be credited with a few key market drivers. Several of the major market drivers are listed below:

Growing Data Volumes: Organizations are facing difficulties in managing and guaranteeing the quality of massive volumes of data due to the exponential growth of data generated by consumers and businesses. Organizations can identify, clean up, and preserve high-quality data from a variety of data sources and formats with the use of data quality management software.
Increasing Complexity of Data Ecosystems: Organizations function within ever-more-complex data ecosystems, which are made up of a variety of systems, formats, and data sources. Software for data quality management enables the integration, standardization, and validation of data from various sources, guaranteeing accuracy and consistency throughout the data landscape.
Regulatory Compliance Requirements: Organizations must maintain accurate, complete, and secure data in order to comply with regulations like the GDPR, CCPA, HIPAA, and others. Data quality management software ensures data accuracy, integrity, and privacy, which assists organizations in meeting regulatory requirements.
Growing Adoption of Business Intelligence and Analytics: As BI and analytics tools are used more frequently for data-driven decision-making, there is a greater need for high-quality data. With the help of data quality management software, businesses can extract actionable insights and generate significant business value by cleaning, enriching, and preparing data for analytics.
Focus on Customer Experience: Put the Customer Experience First: Businesses understand that providing excellent customer experiences requires high-quality data. By ensuring data accuracy, consistency, and completeness across customer touchpoints, data quality management software assists businesses in fostering more individualized interactions and higher customer satisfaction.
Initiatives for Data Migration and Integration: Organizations must clean up, transform, and move data across heterogeneous environments as part of data migration and integration projects like cloud migration, system upgrades, and mergers and acquisitions. Software for managing data quality offers procedures and instruments to guarantee the accuracy and consistency of transferred data.
Need for Data Governance and Stewardship: The implementation of efficient data governance and stewardship practises is imperative to guarantee data quality, consistency, and compliance. Data governance initiatives are supported by data quality management software, which offers features like rule-based validation, data profiling, and lineage tracking.
Operational Efficiency and Cost Reduction: Inadequate data quality can lead to errors, higher operating costs, and inefficiencies for organizations. By guaranteeing high-quality data across business processes, data quality management software helps organizations increase operational efficiency, decrease errors, and minimize rework.

Global On Premises Data Integration Software market size 2025 was XX Million. On Premises Data Integration Software Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

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

Global Data Governance Software Market Drivers

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

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

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

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

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

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

Размер рынка рынка программного обеспечения для очистки данных классифицируется на основе применения (миграция данных, бизнес-разведки, обслуживание CRM) и продукт (оценка качества данных, удаление дубликата, стандартизацию данных) и географические регионы (Северная Америка, Европа, Азиатско-Тихоокеанский регион, Южная Америка и Мидл-Восток). сегменты.

The global Cleaning Service Scheduling Software market is experiencing robust growth, driven by the increasing adoption of technology within the cleaning industry and the rising demand for efficient scheduling and management solutions. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 15% during the forecast period (2025-2033). This growth is fueled by several key trends, including the rising popularity of cloud-based solutions offering scalability and accessibility, the increasing need for real-time data and analytics for better operational efficiency, and a growing preference for integrated platforms offering features like customer relationship management (CRM), invoicing, and payment processing. Large enterprises are leading the adoption, but the market is witnessing significant penetration amongst Small and Medium-sized Enterprises (SMEs) due to the cost-effectiveness and ease of use offered by these software solutions. Market restraints include the initial investment costs associated with software implementation, the need for employee training, and concerns regarding data security and privacy. The competitive landscape is highly fragmented, with several established players and emerging startups vying for market share. This competitive environment fosters innovation, pushing developers to continuously enhance their offerings with features such as AI-powered route optimization and customer communication tools. The market segmentation reveals a strong preference for cloud-based solutions due to their inherent flexibility and accessibility, surpassing on-premise deployments. Large enterprises represent a significant portion of the market, primarily due to their greater need for sophisticated scheduling and management capabilities. However, the SME segment is experiencing rapid growth, presenting a significant opportunity for software providers. Regionally, North America and Europe currently dominate the market, but significant growth is projected in Asia-Pacific and other emerging economies driven by increasing urbanization and the rise of professional cleaning services. The forecast period of 2025-2033 anticipates continued market expansion, fueled by technological advancements and the increasing demand for improved operational efficiency across the cleaning service sector. The projected market size in 2033 is estimated to be around $1.8 Billion, reflecting the significant potential for growth in this dynamic market segment.

Janitorial Software Market size was valued at USD 2.43 Billion in 2024 and is projected to reach USD 3.45 Billion by 2031, growing at a CAGR of 7.97% during the forecast period 2024-2031.

Global Janitorial Software Market Drivers

Growing Need for Operational Efficiency: Organisations in a variety of sectors are putting more and more emphasis on streamlining their processes in order to increase output and efficiency. With the use of janitorial software, cleaning companies may increase overall productivity, optimise resource allocation, and streamline operations with features like task management, scheduling, and real-time monitoring.

Growing Adoption of Automation and Internet of Things: The janitorial sector is undergoing a transformation thanks to the combination of automation technologies and Internet of Things (IoT) devices. IoT-enabled sensors and devices can record cleaning activities, keep an eye on the operation of equipment, and gather information on how the facility is used. Utilising this data, janitorial software can automate repetitive processes, plan cleanings according to demand, and offer predictive maintenance features, all of which increase productivity and lower costs.

Growing Attention to Maintenance and Facility Management: Building managers are realising more and more how crucial cleanliness and proactive maintenance are to improving tenant happiness, safety, and health. With the help of janitorial software solutions, businesses can keep their surroundings safe, clean, and well-maintained. These solutions include work order administration, asset tracking, and compliance monitoring.

Strict Regulatory Requirements and Compliance Standards: Businesses, especially those in the healthcare, hotel, and food services sectors, are subject to stringent cleaning and hygiene regulations enforced by regulatory agencies and industry standards groups. By streamlining paperwork, audit trails, and reporting, janitorial software assists businesses in adhering to regulations and lowers their risk of fines, penalties, and reputational harm.

Transition to Green Cleaning Methods: As people become more conscious of how conventional cleaning methods and chemicals affect the environment, they are choosing more environmentally friendly and sustainable cleaning products. With the use of janitorial software, businesses may monitor and oversee green cleaning programmes, which include using eco-friendly materials, energy-saving equipment, and waste reduction techniques, in accordance with legal requirements and corporate sustainability objectives.

A Growing Emphasis on Health and Hygiene: The COVID-19 pandemic has increased consciousness regarding the significance of sanitation, hygiene, and disinfection in halting the transmission of infectious illnesses. By adding capabilities like contactless scheduling, touchless workflows, and hygiene compliance monitoring, janitorial software systems have evolved to meet the changing needs of businesses and assist them in keeping a safe and healthy workplace for workers, clients, and guests.

Emergence of Mobile and Cloud Technologies: Real-time access to cleaning data, remote monitoring, and mobile workforce management have all been made possible by the widespread use of mobile devices and cloud computing, which has completely changed the janitorial software market. Cleaning personnel may get assignments, turn in reports, and connect with supervisors from any location with the use of mobile-enabled janitorial apps, which enhances responsiveness, cooperation, and communication.

Clean outs are a type of asset that allow access for maintenance purposes to smaller sewer lines which includes both main lines and laterals. Operations staff can use this layer to easily determine where cleaning of some sections of gravity based collections systems will not be possible with their primary equipment and to adjust accordingly. Locations are derived from as-builts and coordination with field staff.Attribute Information:Field Name DescriptionOBJECTIDESRI software specific field that serves as an index for the database.FacilityIDA unique identifier for the asset class. Infor required field.LocationDescriptionInformation related to the construction location or project name. Infor required fieldCommentsA catch all for asset information that is irregular and doesn't warrant the creation of a new field.LastUpdateDate when asset was most recently updated.LastEditorName of user whom most recently edited asset information.EnabledESRI software specific field related to the inclusion in a network.AncillaryRoleESRI software specific field related to the role played within a network.GlobalIDESRI software specific field that is automatically assigned by the geodatabase at row creation.ShapeESRI software specific field denoting the geometry type of the asset.created_userName of user whom created the asset.created_dateDate when the asset was created.last_edited_userName of user whom most recently edited asset information.last_edited_dateDate when asset was most recently updated.IsLocatedHas the location of the asset been field verified with a survey grade GPS unit?InstallDateThe date when the asset was installed. Typically pulled from the as-built cover sheet for consistency. Infor required field.LifecycleStatusThe current status of the asset with respect to its location in the asset management lifecycle. Infor required field.

While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.

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 Data Preparation Tools and Software market has emerged as a cornerstone in the data-driven landscape, catering to the ever-increasing need for businesses to efficiently transform raw data into actionable insights. These tools streamline data cleansing, integration, and preparation processes, allowing companies t

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.WHO Statistics Numbers:Clean Care is Safe Care, Registration Update. (2017). Retrieved n.d., from https://www.who.int/gpsc/5may/registration_update/en/.https://www.who.int/gpsc/5may/registration_update/en/Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

Street sweeping zones by Ward and Ward Section Number. For the corresponding schedule, see https://data.cityofchicago.org/d/x2vd-qke7.

For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

Street sweeping zones by Ward and Ward Section Number. For the corresponding schedule, see https://data.cityofchicago.org/id/ggci-kynu. Because the City of Chicago ward map will change on May 18, 2015, this dataset begins on that date. The map for April and the first half of May is https://data.cityofchicago.org/d/4qtf-5nmn.

For more information about the City's Street Sweeping program, go to http://bit.ly/H2PHUP. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).