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.

The global data integration integrity software market size was valued at USD 19,740 million in 2025 and is projected to grow at a compound annual growth rate (CAGR) of 5.1% from 2025 to 2033. The increasing adoption of cloud-based data integration solutions, growing need for data quality and governance, and rising demand for data integration solutions from small and medium-sized enterprises (SMEs) are key drivers of market growth. The cloud-based segment held the largest market share in 2025 and is expected to continue its dominance during the forecast period. The growing adoption of cloud-based solutions due to their scalability, flexibility, and cost-effectiveness is driving the growth of this segment. The large enterprise segment accounted for a significant share of the market in 2025 and is expected to maintain its dominance during the forecast period. Large enterprises have complex data integration requirements and are willing to invest in robust data integration solutions. North America was the largest regional market in 2025, accounting for a significant share of the global market.

Ethical Data Management

McGRAW’s US Business Executives database offers one of the most accurate and comprehensive B2B datasets available today, covering over 91 million business executives with a 95%+ coverage guarantee. Designed for businesses that require the highest quality data, our database provides detailed, validated, and regularly updated information on decision-makers and influencers across the globe.

Our data purchase options are flexible to fit the needs of various campaigns, whether for direct marketing, lead generation, or advanced market analysis.. With McGRAW, clients gain access to an exhaustive array of fields for each executive profile:

• Core Contact Details: First name, last name, job title, business name, business address, phone number, and business email.
• Company Insights: URL, company sales volume, employee size, SIC/NAICS codes, and industry descriptions.
• Additional Data: Fax numbers, industry-specific descriptors, and company domains.

Why McGRAW’s Data Stands Out

Many B2B data providers rely solely on vendor-contributed files, often bypassing the rigorous validation and verification essential in today’s fast-paced, data-driven environment. This leaves companies dependent on data that may lack accuracy and relevancy. At McGRAW, we’ve taken a different approach. We own and operate dedicated call centers both nearshore and offshore, allowing us to directly engage with our data prior to each delivery. This proactive touch ensures each record meets our high standards of accuracy and reliability.

In addition, our team of social media validators, market researchers, and digital marketing specialists continuously refines our data. They actively verify and update records, staying on top of changes to maintain a fresh and relevant dataset. Each delivery undergoes additional sampling and verification checks through both internal and third-party tools such as Fresh Address, BriteVerify, and Impressionwise to guarantee optimal data quality.

Why Choose McGRAW?

McGRAW guarantees 95%+ accuracy across key fields, including: - Contact Information: First and last names, business name, title, address, and business email. - Business Data: Company domain, phone number, fax number, SIC code, industry description, sales volume, and employee size.

Each record is double-checked through a combination of call center verification, social media validation, and advanced data hygiene processes. This robust approach ensures that you receive accurate, actionable insights every time.

At McGRAW, we don’t just supply data; we ensure every record is reliable, relevant, and ready to power your business strategy. Our investment in direct data handling, coupled with continuous validation processes, makes our Global Business Executives database a leading source for businesses serious about data quality and integrity.

The ETL (Extract, Transform, Load) automation testing market is experiencing robust growth, driven by the increasing complexity of data integration processes and the rising demand for faster, more reliable data pipelines. Businesses across all sectors are adopting cloud-based solutions and big data analytics, fueling the need for automated testing to ensure data quality and integrity. The market's expansion is further propelled by the need to reduce manual testing efforts, accelerate deployment cycles, and minimize the risk of data errors. Considering the current market dynamics and a conservative estimate based on similar technology adoption curves, let's assume a 2025 market size of $2.5 billion USD and a compound annual growth rate (CAGR) of 15% through 2033. This suggests a significant expansion in the coming years, reaching approximately $7 billion USD by 2033. The software segment currently dominates, but the services segment is expected to show strong growth due to the increasing demand for specialized expertise in ETL testing methodologies and tool implementation. Large enterprises are leading the adoption, but SMEs are increasingly adopting automation to streamline their data processes and improve operational efficiency. The key players mentioned demonstrate the competitive landscape, highlighting the presence of both established software vendors and specialized service providers. Geographic distribution shows a concentration of market share in North America and Europe initially, but significant growth is anticipated in Asia-Pacific regions, particularly in India and China, driven by their expanding digital economies and increasing data volumes. Challenges remain in terms of the initial investment required for implementing ETL automation testing solutions and the need for skilled personnel. However, the long-term benefits of improved data quality, reduced costs, and accelerated delivery outweigh these initial hurdles, ensuring continued market expansion.

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...

In 2021–23, the U.S. Geological Survey (USGS), in cooperation with the Ohio Division of Natural Resources, led a study to characterize baseline water quality (2021–23) in eastern Ohio, as they relate to hydraulic fracturing and (or) other oil and gas extraction-related activities. Water-quality data were collected eight times at each of eight sampling sites during a variety of flow conditions to assess baseline water quality. Quality-control (QC) samples collected before and during sampling consisted of blanks and replicates. Blank samples were used to check for contamination potentially introduced during sample collection, processing, equipment cleaning, or analysis. Replicate samples were used to determine the reproducibility or variability in the collection and analysis of environmental samples. All QC samples were collected and processed according to protocols described in the “National Field Manual for the Collection of Water-Quality Data” (USGS, variously dated). To ensure sample integrity and final quality of data, QC samples (one equipment blank, three field blanks, and five replicate samples) were collected for major ions, nutrients, and organics. This data set includes one table of blank samples and one table of field replicate samples. U.S. Geological Survey, variously dated, National field manual for the collection of water-quality data: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chaps. A1-A10, available online at http://pubs.water.usgs.gov/twri9A.

Clinical Trials Support Services Market Valuation – 2024-2031

Clinical Trials Support Services Market was valued at USD 676.02 Million in 2024 and is projected to reach USD 979.93 Million by 2031, growing at a CAGR of 4.75% from 2024 to 2031.

Clinical Trials Support Services Market Drivers

Increasing clinical trials activities: The growing number of clinical trials being conducted globally is driving the demand for support services to ensure efficient and compliant execution.

Advancements in technology: The development of new technologies, such as electronic data capture (EDC) and cloud-based platforms, is improving the efficiency and effectiveness of clinical trials.

Focus on data quality and integrity: The increasing emphasis on data quality and integrity in clinical trials is driving the demand for support services that can help ensure data accuracy and compliance.

Clinical Trial Support Services Market Restraints

High costs: Clinical trials can be expensive, and the demand for support services can add to these costs.

Regulatory challenges: The conduct of clinical trials is subject to strict regulations, which can increase the complexity and cost of conducting trials.

The global market for Patient Record Quality Control (PRQC) solutions is experiencing robust growth, driven by increasing regulatory pressures for data accuracy and patient safety, coupled with the rising adoption of electronic health records (EHRs). The market, estimated at $2.5 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $8 billion by 2033. This expansion is fueled by several key factors. Firstly, the increasing prevalence of chronic diseases necessitates more comprehensive and accurate patient records, leading to greater demand for quality control solutions. Secondly, the transition from paper-based to digital record-keeping creates a need for robust systems to ensure data integrity and compliance with standards such as HIPAA and GDPR. Furthermore, the rise in telehealth and remote patient monitoring further necessitates advanced PRQC systems to guarantee the accuracy and accessibility of patient data across different platforms. The market is segmented by type (outpatient, inpatient, and homepage intelligent control) and application (hospitals and clinics), with hospitals currently accounting for the largest share. Technological advancements such as AI-powered anomaly detection and automated data validation are also driving market growth. The major players in the PRQC market are actively investing in research and development to enhance their offerings and gain a competitive edge. This includes integrating advanced analytics capabilities, developing cloud-based solutions for scalability and accessibility, and expanding into new geographic markets. Despite the strong growth potential, the market faces certain restraints, including the high initial investment costs associated with implementing new PRQC systems and the need for extensive staff training and integration with existing EHR infrastructure. However, the long-term benefits of improved patient care, reduced medical errors, and enhanced regulatory compliance outweigh these challenges. Regional variations in adoption rates are expected, with North America and Europe exhibiting faster growth initially, followed by a surge in demand from Asia-Pacific regions as healthcare infrastructure modernizes.

File Analysis Software Market size was valued at USD 12.04 Billion in 2023 and is projected to reach USD 20.49 Billion by 2030, growing at a CAGR of 11% during the forecast period 2024-2030.

Global File Analysis Software Market Drivers

The market drivers for the File Analysis Software Market can be influenced by various factors. These may include:

Data Growth: Organisations are having difficulty efficiently managing, organising, and analysing their files due to the exponential growth of digital data. File analysis software offers insights into file usage, content, and permissions, which aids in managing this enormous volume of data.

Regulatory Compliance: Organisations must securely and efficiently manage their data in order to comply with regulations like the GDPR, CCPA, HIPAA, etc. Software for file analysis assists in locating sensitive material, guaranteeing compliance, and reducing the risks connected to non-compliance and data breaches.

Data security concerns are a top priority for organisations due to the rise in cyber threats and data breaches. Software for file analysis is essential for locating security holes, unapproved access, and other possible threats in the file system.

Data Governance Initiatives: In order to guarantee the availability, quality, and integrity of their data, organisations are progressively implementing data governance techniques. Software for file analysis offers insights into data ownership, consumption trends, and lifecycle management, which aids in the implementation of data governance policies.

Cloud Adoption: The increasing use of hybrid environments and cloud services calls for efficient file management and analysis across several platforms. Software for file analysis gives users access to and control over files kept on private servers, cloud computing platforms, and third-party services.

Cost Optimisation: By identifying redundant, outdated, and trivial (ROT) material, organisations hope to minimise their storage expenses. Software for file analysis aids in the identification of such material, makes data cleanup easier, and maximises storage capacity.

Digital Transformation: Tools that can extract actionable insights from data are necessary when organisations embark on digital transformation programmes. Advanced analytics and machine learning techniques are employed by file analysis software to offer significant insights into user behaviour, file usage patterns, and data classification.

Collaboration and Remote Work: As more people work remotely and use collaboration technologies, more digital files are created and shared within the company. In remote work situations, file analysis software ensures efficiency and data security by managing and protecting these files.

ESG DATA PRODUCT DESCRIPTION

This ESG dataset offers comprehensive coverage of corporate energy management across thousands of global companies. Our data captures detailed patterns of energy consumption, production, and distribution, providing granular insights into various energy types—including electricity and heat—and the technologies (e.g. solar PV, hydropower...) and sources (e.g. biofuels, coal, natural gas...) utilized. With thorough information on renewability and rigorous standardization of every energy metrics, this dataset enables precise benchmarking, cross-sector comparisons, and strategic decision-making for sustainable energy practices.

Built on precision and transparency, the energy dataset adheres to the highest standards of ESG data quality. Every data point is fully traceable to its original source, ensuring unmatched reliability and accuracy. The dataset is continuously updated to capture the most current and complete information, including revisions, new disclosures, and regulatory updates.

ESG DATA PRODUCT CHARACTERISTICS

• Company Coverage:              5,000+ companies • Geographical Coverage:       Global • Sectorial Coverage:                All sectors • Data Historical Range:           2014 - 2024 • Median Data History:             5 years • Data Traceability Rate:           100% • Data Frequency:                     Annual • Average Reporting Lag:         3 months • Data Format:                            Most Recent/Point-in-Time

UNIQUE DATA VALUE PROPOSITION

Uncompromised Standardization

When company energy data do not align with standard energy reporting frameworks, our team of environmental engineers meticulously maps the reported figures to the correct energy types and flow categories. This guarantees uniformity and comparability across our dataset, bridging the gap created by diverse reporting formats.

Precision in Every Figure

Our advanced cross-source data precision matching algorithm ensures that the most accurate energy metrics are always delivered. For instance, an exact figure like 12,510,545 Joules is prioritized over a rounded figure like 12mio, reflecting our dedication to precision and detail.

Unbiased Data Integrity

Our approach is grounded in delivering energy data exactly as reported by companies, without making inferences or estimates for undisclosed data. This strict adherence to factual reporting ensures the integrity of the data you receive, providing an unaltered and accurate view of corporate emissions.

End-to-End Data Traceability

Every energy data point is directly traceable to its original source, complete with page references and calculation methodologies. This level of detail ensures the reliability and verifiability of our data, giving you complete confidence in our energy dataset.

Full-Scope Boundary Verification

We tag energy figures that do not cover a company's entire operational boundaries with an 'Incomplete Boundaries' attribute. This transparency ensures that any potential limitations are clearly communicated, enhancing the comparability of our energy data.

USE CASES

Asset Management

Asset Management firms use energy data to benchmark portfolio companies against industry standards, ensuring alignment with net-zero goals and regulatory frameworks like SFDR and TCFD. They assess energy transition risks, track renewable energy adoption, and develop sustainable investment products focused on energy efficiency and climate-conscious innovation.

Financial Institutions & Banking

Financial Institutions & Banking integrate energy data into credit risk assessments and sustainability-linked loans, ensuring borrowers meet renewable energy targets. They also enhance due diligence processes, comply with climate disclosure regulations, and validate green bond frameworks with precise renewable energy metrics.

FinTech

FinTech companies leverage energy data to automate regulatory reporting, power energy management analytics, and develop APIs that assess corporate climate risk. They also build sustainable investment tools that enable investors to prioritize companies excelling in energy efficiency and renewability.

GreenTech & ClimateTech

GreenTech & ClimateTech firms use predictive energy analytics to model energy transition risks and renewable adoption trends. They optimize supply chains, facilitate renewable energy procurement, and assess the environmental and financial impacts of energy investments, supporting PPAs and carbon credit markets.

Corporates

Corporates rely on energy data for performance benchmarking, renewable energy procurement, and transition planning. By analyzing detailed energy consumption and sourcing metrics, they optimize sustainability strategies and improve energy efficiency.

Professional Services & Consulting

Professional Services & Consulting firms use energy data to advise on energy transitions, regulatory complia...

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Electronic Clinical Outcome Assessment Solution Market Size 2024-2028

The electronic clinical outcome assessment (eCOA) solution market size is forecast to increase by USD 1.49 billion at a CAGR of 14.5% between 2023 and 2028. The market is experiencing significant growth due to the digitization of the healthcare industry and the increasing use of connected devices and technologies. Cloud professional platforms, hardware, supporting infrastructure providers, software, and services are key components of this market. The adoption of eCOA solutions is driven by the need for accurate and timely data collection and analysis to improve patient outcomes and reduce healthcare costs. Moreover, the use of wearables and other remote monitoring devices is increasing, enabling real-time data collection and analysis. However, data security and privacy concerns remain a challenge for the market, as sensitive patient information must be protected.

Furthermore, to address this, advanced security measures and regulations are being implemented to ensure the confidentiality, integrity, and availability of data. In summary, the eCOA solution market is witnessing growth due to the digitization of healthcare and the increasing use of connected devices. However, data security and privacy concerns must be addressed to ensure the widespread adoption of these solutions. Cloud platforms, hardware, software, and services are essential components of the market, and the use of wearables and other remote monitoring devices is increasing.

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The market is witnessing significant growth in the pharmaceutical and biopharmaceutical sectors. eCOA solutions enable healthcare professionals and patients to record and report clinical trial data in real-time, using cloud-based, decentralized platforms. These systems offer several advantages, including improved data accuracy, reduced operational costs, and increased patient engagement. Pharmaceutical and biopharmaceutical companies are increasingly adopting eCOA solutions to streamline their clinical trials. Decentralized clinical trials, which utilize eCOA solutions, allow patients to participate from their homes or local clinics, reducing the need for frequent hospital visits.

Furthermore, this approach is particularly beneficial for hospitals, medical device companies, and connected devices manufacturers, as it enables them to collect data more efficiently and effectively. eCOA solutions are also gaining popularity in emerging economies due to their ability to cater to the genetic diversity of patients and diseases. Biotechnology companies are leveraging these systems to meet regulatory requirements and ensure data integrity in their clinical trials. Major players in the eCOA market include Kayentis, Medidata, Signant Health, ArisGlobal, Evidentiq, Assistek, Thread, Modus Outcomes, and Dacima. These companies offer Alexa-style tools and services that facilitate data collection, management, and analysis, making it easier for healthcare professionals to monitor patient progress and make informed decisions.

In addition, the regulatory environment plays a crucial role in the adoption of eCOA solutions. Regulatory bodies are increasingly recognizing the benefits of these systems and are implementing guidelines to ensure their effective use in clinical trials. This trend is expected to continue, as eCOA solutions offer significant advantages over traditional paper-based methods, including improved data quality, reduced errors, and increased patient engagement. In conclusion, the eCOA solution market is experiencing steady growth in the pharmaceutical and biopharmaceutical sectors. These systems offer numerous benefits, including improved data accuracy, reduced operational costs, and increased patient engagement. As regulatory requirements continue to evolve, and the need for decentralized clinical trials increases, the adoption of eCOA solutions is expected to continue.

Market Segmentation

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

Deployment

  Cloud-based
  On-premises


End-user

  CROs
  Hospitals
  Pharmaceuticals and biological industry
  Others


Geography

  North America

    Canada
    US


  Asia

    China
    Japan


  Europe

    Germany


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. In the realm of electronic Clinical Outcome Assessment (eCOA) solutions, cloud-based offerings have emerged as a leading choice for pharmaceutical organizations. This trend is driven by the advantages associated with cloud technology. Primarily, cloud-based eCOA solutions enable remote access to patient health data and

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

Data Governance Market: Definition/ Overview

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

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

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

On August 25th, 2022, Metro Council Passed Open Data Ordinance; previously open data reports were published on Mayor Fischer's Executive Order, You can find here both the Open Data Ordinance, 2022 (PDF) and the Mayor's Open Data Executive Order, 2013 Open Data Annual ReportsPage 6 of the Open Data Ordinance, Within one year of the effective date of this Ordinance, and thereafter no later than September1 of each year, the Open Data Management Team shall submit to the Mayor and Metro Council an annual Open Data Report.The Open Data Management team (also known as the Data Governance Team is currently led by the city's Data Officer Andrew McKinney in the Office of Civic Innovation and Technology. Previously, it was led by the former Data Officer, Michael Schnuerle and prior to that by Director of IT.Open Data Ordinance O-243-22 TextLouisville Metro GovernmentLegislation TextFile #: O-243-22, Version: 3ORDINANCE NO._, SERIES 2022AN ORDINANCE CREATING A NEW CHAPTER OF THE LOUISVILLE/JEFFERSONCOUNTY METRO CODE OF ORDINANCES CREATING AN OPEN DATA POLICYAND REVIEW. (AMENDMENT BY SUBSTITUTION)(AS AMENDED).SPONSORED BY: COUNCIL MEMBERS ARTHUR, WINKLER, CHAMBERS ARMSTRONG,PIAGENTINI, DORSEY, AND PRESIDENT JAMESWHEREAS, Metro Government is the catalyst for creating a world-class city that provides itscitizens with safe and vibrant neighborhoods, great jobs, a strong system of education and innovationand a high quality of life;WHEREAS, it should be easy to do business with Metro Government. Online governmentinteractions mean more convenient services for citizens and businesses and online governmentinteractions improve the cost effectiveness and accuracy of government operations;WHEREAS, an open government also makes certain that every aspect of the builtenvironment also has reliable digital descriptions available to citizens and entrepreneurs for deepengagement mediated by smart devices;WHEREAS, every citizen has the right to prompt, efficient service from Metro Government;WHEREAS, the adoption of open standards improves transparency, access to publicinformation and improved coordination and efficiencies among Departments and partnerorganizations across the public, non-profit and private sectors;WHEREAS, by publishing structured standardized data in machine readable formats, MetroGovernment seeks to encourage the local technology community to develop software applicationsand tools to display, organize, analyze, and share public record data in new and innovative ways;WHEREAS, Metro Government’s ability to review data and datasets will facilitate a betterUnderstanding of the obstacles the city faces with regard to equity;WHEREAS, Metro Government’s understanding of inequities, through data and datasets, willassist in creating better policies to tackle inequities in the city;WHEREAS, through this Ordinance, Metro Government desires to maintain its continuousimprovement in open data and transparency that it initiated via Mayoral Executive Order No. 1,Series 2013;WHEREAS, Metro Government’s open data work has repeatedly been recognized asevidenced by its achieving What Works Cities Silver (2018), Gold (2019), and Platinum (2020)certifications. What Works Cities recognizes and celebrates local governments for their exceptionaluse of data to inform policy and funding decisions, improve services, create operational efficiencies,and engage residents. The Certification program assesses cities on their data-driven decisionmakingpractices, such as whether they are using data to set goals and track progress, allocatefunding, evaluate the effectiveness of programs, and achieve desired outcomes. These datainformedstrategies enable Certified Cities to be more resilient, respond in crisis situations, increaseeconomic mobility, protect public health, and increase resident satisfaction; andWHEREAS, in commitment to the spirit of Open Government, Metro Government will considerpublic information to be open by default and will proactively publish data and data containinginformation, consistent with the Kentucky Open Meetings and Open Records Act.NOW, THEREFORE, BE IT ORDAINED BY THE COUNCIL OF THELOUISVILLE/JEFFERSON COUNTY METRO GOVERNMENT AS FOLLOWS:SECTION I: A new chapter of the Louisville Metro Code of Ordinances (“LMCO”) mandatingan Open Data Policy and review process is hereby created as follows:§ XXX.01 DEFINITIONS. For the purpose of this Chapter, the following definitions shall apply unlessthe context clearly indicates or requires a different meaning.OPEN DATA. Any public record as defined by the Kentucky Open Records Act, which could bemade available online using Open Format data, as well as best practice Open Data structures andformats when possible, that is not Protected Information or Sensitive Information, with no legalrestrictions on use or reuse. Open Data is not information that is treated as exempt under KRS61.878 by Metro Government.OPEN DATA REPORT. The annual report of the Open Data Management Team, which shall (i)summarize and comment on the state of Open Data availability in Metro Government Departmentsfrom the previous year, including, but not limited to, the progress toward achieving the goals of MetroGovernment’s Open Data portal, an assessment of the current scope of compliance, a list of datasetscurrently available on the Open Data portal and a description and publication timeline for datasetsenvisioned to be published on the portal in the following year; and (ii) provide a plan for the next yearto improve online public access to Open Data and maintain data quality.OPEN DATA MANAGEMENT TEAM. A group consisting of representatives from each Departmentwithin Metro Government and chaired by the Data Officer who is responsible for coordinatingimplementation of an Open Data Policy and creating the Open Data Report.DATA COORDINATORS. The members of an Open Data Management Team facilitated by theData Officer and the Office of Civic Innovation and Technology.DEPARTMENT. Any Metro Government department, office, administrative unit, commission, board,advisory committee, or other division of Metro Government.DATA OFFICER. The staff person designated by the city to coordinate and implement the city’sopen data program and policy.DATA. The statistical, factual, quantitative or qualitative information that is maintained or created byor on behalf of Metro Government.DATASET. A named collection of related records, with the collection containing data organized orformatted in a specific or prescribed way.METADATA. Contextual information that makes the Open Data easier to understand and use.OPEN DATA PORTAL. The internet site established and maintained by or on behalf of MetroGovernment located at https://data.louisvilleky.gov/ or its successor website.OPEN FORMAT. Any widely accepted, nonproprietary, searchable, platform-independent, machinereadablemethod for formatting data which permits automated processes.PROTECTED INFORMATION. Any Dataset or portion thereof to which the Department may denyaccess pursuant to any law, rule or regulation.SENSITIVE INFORMATION. Any Data which, if published on the Open Data Portal, could raiseprivacy, confidentiality or security concerns or have the potential to jeopardize public health, safety orwelfare to an extent that is greater than the potential public benefit of publishing that data.§ XXX.02 OPEN DATA PORTAL(A) The Open Data Portal shall serve as the authoritative source for Open Data provided by MetroGovernment.(B) Any Open Data made accessible on Metro Government’s Open Data Portal shall use an OpenFormat.(C) In the event a successor website is used, the Data Officer shall notify the Metro Council andshall provide notice to the public on the main city website.§ XXX.03 OPEN DATA MANAGEMENT TEAM(A) The Data Officer of Metro Government will work with the head of each Department to identify aData Coordinator in each Department. The Open Data Management Team will work to establish arobust, nationally recognized, platform that addresses digital infrastructure and Open Data.(B) The Open Data Management Team will develop an Open Data Policy that will adopt prevailingOpen Format standards for Open Data and develop agreements with regional partners to publish andmaintain Open Data that is open and freely available while respecting exemptions allowed by theKentucky Open Records Act or other federal or state law.§ XXX.04 DEPARTMENT OPEN DATA CATALOGUE(A) Each Department shall retain ownership over the Datasets they submit to the Open DataPortal. The Departments shall also be responsible for all aspects of the quality, integrity and securityPortal. The Departments shall also be responsible for all aspects of the quality, integrity and securityof the Dataset contents, including updating its Data and associated Metadata.(B) Each Department shall be responsible for creating an Open Data catalogue which shall includecomprehensive inventories of information possessed and/or managed by the Department.(C) Each Department’s Open Data catalogue will classify information holdings as currently “public”or “not yet public;” Departments will work with the Office of Civic Innovation and Technology todevelop strategies and timelines for publishing Open Data containing information in a way that iscomplete, reliable and has a high level of detail.§ XXX.05 OPEN DATA REPORT AND POLICY REVIEW(A) Within one year of the effective date of this Ordinance, and thereafter no later than September1 of each year, the Open Data Management Team shall submit to the Mayor and Metro Council anannual Open Data Report.(B) Metro Council may request a specific Department to report on any data or dataset that may bebeneficial or pertinent in implementing policy and legislation.(C) In acknowledgment that technology changes rapidly, in the future, the Open Data Policy shouldshall be reviewed annually and considered for revisions or additions that will continue to positionMetro Government as a leader on issues of

PurposeAround 5% of United States (U.S.) population identifies as Sexual and Gender Diverse (SGD), yet there is limited research around cancer prevention among these populations. We present multi-pronged, low-cost, and systematic recruitment strategies used to reach SGD communities in New Mexico (NM), a state that is both largely rural and racially/ethnically classified as a “majority-minority” state.MethodsOur recruitment focused on using: (1) Every Door Direct Mail (EDDM) program, by the United States Postal Services (USPS); (2) Google and Facebook advertisements; (3) Organizational outreach via emails to publicly available SGD-friendly business contacts; (4) Personal outreach via flyers at clinical and community settings across NM. Guided by previous research, we provide detailed descriptions on using strategies to check for fraudulent and suspicious online responses, that ensure data integrity.ResultsA total of 27,369 flyers were distributed through the EDDM program and 436,177 impressions were made through the Google and Facebook ads. We received a total of 6,920 responses on the eligibility survey. For the 5,037 eligible respondents, we received 3,120 (61.9%) complete responses. Of these, 13% (406/3120) were fraudulent/suspicious based on research-informed criteria and were removed. Final analysis included 2,534 respondents, of which the majority (59.9%) reported hearing about the study from social media. Of the respondents, 49.5% were between 31-40 years, 39.5% were Black, Hispanic, or American Indian/Alaskan Native, and 45.9% had an annual household income below $50,000. Over half (55.3%) were assigned male, 40.4% were assigned female, and 4.3% were assigned intersex at birth. Transgender respondents made up 10.6% (n=267) of the respondents. In terms of sexual orientation, 54.1% (n=1371) reported being gay or lesbian, 30% (n=749) bisexual, and 15.8% (n=401) queer. A total of 756 (29.8%) respondents reported receiving a cancer diagnosis and among screen-eligible respondents, 66.2% reported ever having a Pap, 78.6% reported ever having a mammogram, and 84.1% reported ever having a colonoscopy. Over half of eligible respondents (58.7%) reported receiving Human Papillomavirus vaccinations.ConclusionStudy findings showcase effective strategies to reach communities, maximize data quality, and prevent the misrepresentation of data critical to improve health in SGD communities.

Overview Water companies in the UK are responsible for testing the quality of drinking water. This dataset contains the results of samples taken from the taps in domestic households to make sure they meet the standards set out by UK and European legislation. This data shows the location, date, and measured levels of determinands set out by the Drinking Water Inspectorate (DWI).Key Definitions Aggregation Process involving summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes  Anonymisation Anonymised data is a type of information sanitisation in which data anonymisation tools encrypt or remove personally identifiable information from datasets for the purpose of preserving a data subject's privacy Dataset Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fieldsDeterminand A constituent or property of drinking water which can be determined or estimatedDWI Drinking Water Inspectorate, an organisation “providing independent reassurance that water supplies in England and Wales are safe and drinking water quality is acceptable to consumers.”  DWI Determinands Constituents or properties that are tested for when evaluating a sample for its quality as per the guidance of the DWI. For this dataset, only determinands with “point of compliance” as “customer taps” are included.   Granularity Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours ID Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenanceLSOA Lower-Level Super Output Area is made up of small geographic areas used for statistical and administrative purposes by the Office for National Statistics. It is designed to have homogeneous populations in terms of population size, making them suitable for statistical analysis and reporting. Each LSOA is built from groups of contiguous Output Areas with an average of about 1,500 residents or 650 households allowing for granular data collection useful for analysis, planning and policy-making while ensuring privacy.  ONS Office for National Statistics  Open Data Triage The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open DataSample A sample is a representative segment or portion of water taken from a larger whole for the purpose of analysing or testing to ensure compliance with safety and quality standards.  Schema Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.  Units Standard measurements used to quantify and compare different physical quantitiesWater Quality The chemical, physical, biological, and radiological characteristics of water, typically in relation to its suitability for a specific purpose, such as drinking, swimming, or ecological health. It is determined by assessing a variety of parameters, including but not limited to pH, turbidity, microbial content, dissolved oxygen, presence of substances and temperature.  Data History  Data Origin  These samples were taken from customer taps. They were then analysed for water quality, and the results were uploaded to a database. This dataset is an extract from this database. Data Triage Considerations Granularity Is it useful to share results as averages or individual? We decided to share as individual results as the lowest level of granularity. AnonymisationIt is a requirement that this data cannot be used to identify a singular person or household. We discussed many options for aggregating the data to a specific geography to ensure this requirement is met. The following geographical aggregations were discussed: • Water Supply Zone (WSZ) - Limits interoperability with other datasets • Postcode – Some postcodes contain very few households and may not offer necessary anonymisation • Postal Sector – Deemed not granular enough in highly populated areas • Rounded Co-ordinates – Not a recognised standard and may cause overlapping areas • MSOA – Deemed not granular enough • LSOA – Agreed as a recognised standard appropriate for England and Wales • Data Zones – Agreed as a recognised standard appropriate for Scotland Data Triage Review Frequency  Annually unless otherwise requested Publish FrequencyAnnuallyData SpecificationsEach dataset will cover a calendar year of samples This dataset will be published annually Historical datasets will be published as far back as 2016 from the introduction of The Water Supply (Water Quality) Regulations 2016 The determinands included in the dataset are as per the list that is required to be reported to the Drinking Water Inspectorate. Context Many UK water companies provide a search tool on their websites where you can search for water quality in your area by postcode. The results of the search may identify the water supply zone that supplies the postcode searched. Water supply zones are not linked to LSOAs which means the results may differ to this dataset Some sample results are influenced by internal plumbing and may not be representative of drinking water quality in the wider area. Some samples are tested on site and others are sent to scientific laboratories. Supplementary informationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.   1. Drinking Water Inspectorate Standards and Regulations: https://www.dwi.gov.uk/drinking-water-standards-and-regulations/ 2. LSOA (England and Wales) and Data Zone (Scotland): https://www.nrscotland.gov.uk/files/geography/2011-census/geography-bckground-info-comparison-of-thresholds.pdf 3. Description for LSOA boundaries by the ONS: https://www.ons.gov.uk/methodology/geography/ukgeographies/censusgeographies/census2021geographies4. Postcode to LSOA lookup tables: https://geoportal.statistics.gov.uk/datasets/postcode-to-2021-census-output-area-to-lower-layer-super-output-area-to-middle-layer-super-output-area-to-local-authority-district-august-2023-lookup-in-the-uk/about5. Legislation history: https://www.dwi.gov.uk/water-companies/legislation/

Success.ai’s Licensed Professionals Data API equips organizations with the data intelligence they need to confidently engage with professionals across regulated industries. Whether you’re verifying the credentials of healthcare practitioners, confirming licensing status for legal advisers, or identifying certified specialists in construction, this API provides real-time, AI-validated details on certifications, licenses, and qualifications.

By tapping into over 700 million verified profiles, you can ensure compliance, build trust, and streamline your due diligence processes. Backed by our Best Price Guarantee, Success.ai’s solution helps you operate efficiently, mitigate risk, and maintain credibility in highly regulated markets.

Why Choose Success.ai’s Licensed Professionals Data API?

1. Verified Licenses & Certifications

   • Access detailed information about professional credentials, educational backgrounds, and accreditations.
   • Rely on 99% data accuracy through AI-driven validation, ensuring each verification is robust and reliable.

2. Comprehensive Global Coverage

   • Includes professionals across healthcare, law, construction, finance, engineering, and more.
   • Confidently scale verification processes as you enter new regions or explore diverse regulated markets.

3. Continuously Updated Data

   • Receive real-time updates to maintain up-to-date compliance records and accurate credential checks.
   • Respond swiftly to changes in licensing standards, new certifications, or shifting industry requirements.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.
   • Safeguard brand reputation and reduce risk of non-compliance with strict regulatory environments.

Data Highlights:

• Over 700M Verified Profiles: Engage with a vast pool of licensed professionals worldwide.
• Licensing & Qualification Details: Confirm professional credentials, specializations, and areas of practice.
• Continuous Data Refresh: Always access current, reliable data for timely verifications.
• Best Price Guarantee: Optimize ROI by leveraging premium-quality data at industry-leading rates.

Key Features of the Licensed Professionals Data API:

1. On-Demand Credential Verification

   • Seamlessly enrich CRM systems, HR platforms, or compliance tools with verified professional licensure data.
   • Minimize manual research and accelerate due diligence cycles for faster decision-making.

2. Advanced Filtering & Query Options

   • Query the API by industry (healthcare, legal, construction), geographic location, or specific certifications.
   • Target precisely the professionals required for your projects, compliance checks, or service offerings.

3. Real-Time Validation & Reliability

   • Depend on AI-driven verification processes to ensure data integrity and relevance.
   • Make confident, informed decisions backed by accurate credentials and licensing details.

4. Scalable & Flexible Integration

   • Easily integrate the API into existing workflows, analytics platforms, or recruitment systems.
   • Adjust parameters as project scopes or regulatory conditions evolve, maintaining long-term adaptability.

Strategic Use Cases:

1. Compliance & Regulatory Assurance

   • Verify credentials in healthcare (e.g., physician licenses), legal (bar admissions), or construction (professional certifications) to ensure compliance.
   • Avoid reputational damage and legal liabilities by confirming qualifications before engagement.

2. Recruitment & Talent Acquisition

   • Identify and confirm the qualifications of candidates in regulated industries.
   • Streamline hiring processes for specialized roles, improving time-to-fill and talent quality.

3. Partner & Supplier Validation

   • Confirm that partners, vendors, or contractors meet industry standards and licensing requirements.
   • Strengthen supply chain integrity and safeguard organizational interests.

4. Market Research & Industry Analysis

   • Assess the concentration of licensed professionals in specific regions or specialties.
   • Inform product development, service offerings, or strategic expansions based on verified professional talent pools.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-tier licensed professional data at leading market rates, ensuring exceptional ROI on compliance and verification efforts.

2. Seamless Integration

   • Incorporate the API effortlessly into existing tools, reducing data silos and manual handling, thus improving productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide decisions, minimize risk, and maintain compliance in highly regulated sectors.

4. Customizable & Scalable Solutions

   • Tailor datasets to meet evolving standards, regulation changes, or business ...

This workflow focuses on analyzing diverse soil datasets using PCA to understand their physicochemical properties. It connects to a MongoDB database to retrieve soil samples based on user-defined filters. Key objectives include variable selection, data quality improvement, standardization, and conducting PCA for data variance and pattern analysis. The workflow generates graphical representations, such as covariance and correlation matrices, scree plots, and scatter plots, to enhance data interpretability. This facilitates the identification of significant variables, data structure exploration, and optimal component determination for effective soil analysis. Background - Understanding the intricate relationships and patterns within soil samples is crucial for various environmental and agricultural applications. Principal Component Analysis (PCA) serves as a powerful tool in unraveling the complexity of multivariate soil datasets. Soil datasets often consist of numerous variables representing diverse physicochemical properties, making PCA an invaluable method for: ∙Dimensionality Reduction: Simplifying the analysis without compromising data integrity by reducing the dimensionality of large soil datasets. ∙Identification of Dominant Patterns: Revealing dominant patterns or trends within the data, providing insights into key factors contributing to overall variability. ∙Exploration of Variable Interactions: Enabling the exploration of complex interactions between different soil attributes, enhancing understanding of their relationships. ∙Interpretability of Data Variance: Clarifying how much variance is explained by each principal component, aiding in discerning the significance of different components and variables. ∙Visualization of Data Structure: Facilitating intuitive comprehension of data structure through plots such as scatter plots of principal components, helping identify clusters, trends, and outliers. ∙Decision Support for Subsequent Analyses: Providing a foundation for subsequent analyses by guiding decision-making, whether in identifying influential variables, understanding data patterns, or selecting components for further modeling. Introduction The motivation behind this workflow is rooted in the imperative need to conduct a thorough analysis of a diverse soil dataset, characterized by an array of physicochemical variables. Comprising multiple rows, each representing distinct soil samples, the dataset encompasses variables such as percentage of coarse sands, percentage of organic matter, hydrophobicity, and others. The intricacies of this dataset demand a strategic approach to preprocessing, analysis, and visualization. This workflow introduces a novel approach by connecting to a MongoDB, an agile and scalable NoSQL database, to retrieve soil samples based on user-defined filters. These filters can range from the natural site where the samples were collected to the specific date of collection. Furthermore, the workflow is designed to empower users in the selection of relevant variables, a task facilitated by user-defined parameters. This flexibility allows for a focused and tailored dataset, essential for meaningful analysis. Acknowledging the inherent challenges of missing data, the workflow offers options for data quality improvement, including optional interpolation of missing values or the removal of rows containing such values. Standardizing the dataset and specifying the target variable are crucial, establishing a robust foundation for subsequent statistical analyses. Incorporating PCA offers a sophisticated approach, enabling users to explore inherent patterns and structures within the data. The adaptability of PCA allows users to customize the analysis by specifying the number of components or desired variance. The workflow concludes with practical graphical representations, including covariance and correlation matrices, a scree plot, and a scatter plot, offering users valuable visual insights into the complexities of the soil dataset. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of diverse soil samples: ∙Connect to MongoDB and retrieve data: Dynamically connect to a MongoDB database, allowing users to download soil samples based on user-defined filters. ∙Variable selection: Empower users to extract relevant variables based on user-defined parameters, facilitating a focused and tailored dataset. ∙Data quality improvement: Provide options for interpolation or removal of missing values to ensure dataset integrity for downstream analyses. ∙Standardization and target specification: Standardize the dataset values and designate the target variable, laying the groundwork for subsequent statistical analyses. ∙PCA: Conduct PCA with flexibility, allowing users to specify the number of components or desired variance for a comprehensive understanding of data variance and patterns. ∙Graphical representations: Generate visual outputs, including covariance and correlation matrices, a scree plot, and a scatter plot, enhancing the interpretability of the soil dataset. Scientific questions - This workflow addresses critical scientific questions related to soil analysis: ∙Facilitate Data Access: To streamline the retrieval of systematically stored soil sample data from the MongoDB database, aiding researchers in accessing organized data previously stored. ∙Variable importance: Identify variables contributing significantly to principal components through the covariance matrix and PCA. ∙Data structure: Explore correlations between variables and gain insights from the correlation matrix. ∙Optimal component number: Determine the optimal number of principal components using the scree plot for effective representation of data variance. ∙Target-related patterns: Analyze how selected principal components correlate with the target variable in the scatter plot, revealing patterns based on target variable values.

Abstract

Because of the importance of the household sector and due to it's large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special household energy survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey.

This survey aimed to provide data on energy consumption in the household and to provide data on energy consumption behavior in the society by type of energy.

The survey presents data on various energy households indicators in the Palestinian Territory, and presents statistical data on electricity and other fuel consumption for the household, using type of fuel by different activities (cooking, baking, conditioning, lighting, and water Heating).

Analysis unit

Households

Universe

The target population was all Palestinian households living in West Bank and Gaza.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The sampling frame consists of all the enumeration areas enumerated in 2007: each enumeration area consists of buildings and housing units with an average of around 124 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size The estimated sample size is 3,184 households.

Sampling Design: The sample of this survey is a part of the main sample of the Labor Force Survey (LFS), which is implemented quarterly (distributed over 13 weeks) by PCBS since 1995. This survey was attached to the LFS in the third quarter of 2013 and the sample comprised six weeks, from the eighth week to the thirteen week of the third round of the Labor Force Survey of 2013. The sample is two-stage stratified cluster sample:

First stage: selection of a stratified systematic random sample of 206 enumeration areas for the semi-round.

Second stage: selection of a random area sample of an average of 16 households from each enumeration area selected in the first stage.

Sample strata The population was divided by: 1. Governorate (16 governorates) 2. Type of locality (urban, rural, refugee camps)

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

The data processing stage consisted of the following operations: - Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field. - Data entry: The household energy survey questionnaire was programmed onto handheld devices and data were entered directly using these devices in the West Bank. With regard to Jerusalem J1 and the Gaza Strip, data were entered into the computer in the offices in Ramallah and Gaza. At this stage, data were entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements: · To prevent the duplication of questionnaires during data entry. · To apply checks on the integrity and consistency of entered data. · To handle errors in a user friendly manner. · The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

During fieldwork 3,184 families were visited in the Palestinian Territory. There are 2,692 complete questionnaires, which in percentage was about 85%.

Sampling error estimates

Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are anticipated in comparison with the real values obtained through censuses. The variance was calculated for the most important indicators: the variance table is attached with the final report. There is no problem in the dissemination of results at national and regional level (North, Middle, South of West Bank, Gaza Strip) and by locality. However, the indicator of averages of household consumption for certain fuels by region show a high variance.

Non Sampling Errors The implementation of the survey encountered non-response where the household was not present at home during the field work visit and where the housing unit was vacant: these made up a high percentage of the non-response cases. The total non-response rate was 10.8%, which is very low when compared to the household surveys conducted by PCBS. The refusal rate was 3.3%, which is very low compared to the household surveys conducted by PCBS and may be attributed to the short and clear questionnaire.

The survey sample consisted of around 3,184 households, of which 2,692 households completed the interview: 1,757 households from the West Bank and 935 households in the Gaza Strip. Weights were modified to account for the non-response rate. The response rate in the West Bank was 86.8 % while in the Gaza Strip it was 94.3%.

Non-Response Cases

No. of cases non-response cases
2,692 Household completed 35 Household traveling 17 Unit does not exist 111 No one at home
102 Refused to cooperate
152 Vacant housing unit 5 No available information
70 Other
3,184 Total sample size

Response and non-response formulas:

Percentage of over coverage errors = Total cases of over coverage x 100% Number of cases in original sample = 5.3%

Non response rate = Total cases of non response x 100% Net Sample size = 10.8%

Net sample = Original sample - cases of over coverage Response rate = 100% - non-response rate = 89.2%

Treatment of non-response cases using weight adjustment

Where

the primary weight before adjustment for the household i g: adjustment group by ( governorate, locality type ). fg: weight adjustment factor for the group g. : Total weights in group g
cases : Total weights of over coverage : Total weights of response cases

We calculate fg for each group ,and final we obtain the final household weight () by using the following formula:

Comparability The data of the survey are comparable geographically and over time by comparing data from different geographical areas to data of previous surveys and the 2007 census.

Data quality assurance procedures Several procedures were undertaken to ensure appropriate quality control in the survey. Field workers were trained on the main skills prior to data collection, field visits were conducted to field workers to ensure the integrity of data collection, editing of questionnaires took place prior to data entry and a data entry application was used that prevents errors during the data entry process, then the data were reviewed. This was done to ensure that data were error free, while cleaning and inspection of anomalous values were carried out to ensure harmony between the different questions on the questionnaire.

Technical notes The following are important technical notes on the indicators presented in the results of the survey: · Some households were not present in their houses and could not be seen by interviewers. · Some households were not accurate in answering the questions in the questionnaire.
· Some errors occurred due to the way the questions were asked by interviewers. · Misunderstanding of the questions by the respondents. · Answering questions related to consumption based on estimations. · In all calculations related to gasoline, the average of all available types of gasoline was used. · In this survey, data were collected about the consumption of olive cake and coal in households, but due to lack of relevant data and fairly high variance, the data were grouped with others in the statistical tables. · The increase in consumption of electricity and the decrease in the consumption of the other types of fuel in the Gaza Strip reflected the Israeli siege imposed on the territory.

Data appraisal

The data of the survey is comparable geographically and over time by comparing the data between different geographical areas to data of previous surveys.

Success.ai’s Healthcare Marketing Data API empowers pharmaceutical firms, medical equipment suppliers, healthcare recruiters, and service providers to effectively connect with a broad network of health professionals. Covering roles such as physicians, nurses, hospital administrators, and specialized practitioners, this API delivers verified contact details, professional insights, and continuously updated data to ensure your outreach and engagement strategies remain compliant, accurate, and impactful.

With our AI-validated data and Best Price Guarantee, Success.ai’s Healthcare Marketing Data API is the vital tool you need to navigate complex healthcare markets, foster stronger professional relationships, and drive measurable growth.

Why Choose Success.ai’s Healthcare Marketing Data API?

1. Verified and Compliant Healthcare Professional Contacts

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare professionals worldwide.
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3. Continuously Updated and Accurate

   • Receive real-time data updates that keep pace with changing roles, institutional affiliations, and healthcare trends.
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4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible, lawful data usage and protecting brand integrity.

Data Highlights:

• Verified Healthcare Professional Profiles: Engage with a broad range of medical practitioners, administrators, and clinical experts.
• Professional Histories and Specializations: Understand each professional’s background, affiliations, and areas of expertise for more relevant outreach.
• Real-Time Updates: Continually refreshed data ensures you’re always aligned with current organizational structures and regulatory shifts.
• Best Price Guarantee: Achieve maximum ROI and cost-efficiency in your healthcare outreach efforts.

Key Features of the Healthcare Marketing Data API:

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   • Easily integrate the API into existing systems, analytics platforms, or recruitment tools.
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Strategic Use Cases:

1. Pharmaceutical Sales and Detailing

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Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-quality healthcare marketing data at industry-leading prices, ensuring maximum ROI on your campaigns.

2. Seamless Integration

   • Incorporate the API into existing workflows, eliminating data silos and manual data management.

3. Data Accuracy with AI Validatio...