Individuals have the right to access their personal data held by private companies. This operation can be started by different types of data subjects. A 2020 poll conducted among UK managers showed that ** percent of the requests came from employees or ex-employees. Another ** percent of Data Subject Access Requests (DSAR) were submitted by customers.

Data Subject Access Request (DSAR) Software Market Outlook

The global Data Subject Access Request (DSAR) Software market size was valued at approximately USD 1.2 billion in 2023 and is projected to grow to USD 3.5 billion by 2032, boasting a compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth is largely driven by increasing regulatory demands for data privacy and protection across various sectors. With the exponential rise in data generation and the necessity for organizations to comply with regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), the demand for efficient DSAR software solutions is expected to see substantial growth.

One of the primary factors propelling the growth of the DSAR software market is the escalating need for data privacy and protection in an increasingly digital world. As more businesses transition to digital platforms and rely on data analytics to drive decision-making, the volume of personal data being collected has surged. This has heightened the risk of data breaches and unauthorized access to sensitive information, prompting governments worldwide to enact stringent data privacy laws. Consequently, organizations are compelled to adopt DSAR software to ensure compliance with these regulations and to manage consumer requests for data access effectively, driving market growth.

Another significant growth factor is the increasing consumer awareness and demand for transparency regarding how their personal data is used and stored. Consumers are becoming savvy about their data privacy rights and are more likely to request access to their personal data. This demand for transparency is encouraging businesses to implement DSAR software solutions that facilitate seamless and efficient data access requests. By providing consumers with confidence that their data is managed responsibly, organizations can enhance their reputation and build trust, which is critical in maintaining customer relationships and loyalty.

The rapid advancements in technology and the integration of artificial intelligence (AI) and machine learning (ML) in DSAR software are also contributing to market growth. These technological innovations are enabling more sophisticated data processing and automation of DSAR processes, resulting in improved accuracy and efficiency. By leveraging AI and ML capabilities, DSAR software can offer predictive analytics, automated workflows, and enhanced data management, which are particularly beneficial for large enterprises dealing with vast volumes of data. This technological evolution is expected to further augment the adoption of DSAR software across various industries.

Regionally, North America holds a dominant position in the DSAR software market, largely due to the early adoption of advanced technologies and stringent data privacy regulations in the region. Europe's market is also experiencing substantial growth, driven by the rigorous enforcement of GDPR, which has encouraged enterprises to invest in robust DSAR solutions. Meanwhile, the Asia Pacific region is anticipated to exhibit the fastest growth rate over the forecast period, attributed to the burgeoning digital economy and increasing regulatory pressures in countries like India and China. These regional dynamics underscore the global demand for DSAR software and the varied factors influencing its adoption across different geographies.

Component Analysis

The DSAR software market can be divided into two primary components: software and services. The software segment encompasses a variety of solutions designed to facilitate the management of data access requests, ensuring compliance with privacy laws and regulations. These software solutions often include features such as data mapping, request tracking, automated workflow management, and reporting capabilities. The growing complexity of data privacy regulations is driving demand for comprehensive software solutions that can efficiently handle these requirements, thereby boosting the growth of this segment.

Within the software component, there is an increasing focus on developing user-friendly interfaces and customizable features that cater to the specific needs of different industries. Software vendors are investing significantly in research and development to enhance the functionality of their offerings, integrating advanced technologies such as AI and ML to provide predictive analytics and automate repetitive tasks. This not only improves operational efficiency but also reduces the risk of human error, making software solutions more appeal

This dataset is an abridged version of the Information Management and Security Team Subject Access Requests Log. The log is used to record, track and report on the Subject Access Requests made to the FSA. A subject access request is the name given to a request made by an individual for personal data an organisation holds about them under the Data Protection Act.

Dataset containing information related to non-NYPD Subjects involved in Force Incidents. The Threat, Resistance, or Injury (TRI) Report is the primary means by which the NYPD records use of force incidents. All reportable instances of force – whether used by a member of the Department, or against the member – are recorded on a TRI Report. Data provided here are a result of the information captured on TRI Reports. Each record corresponds to a non-NYPD subject involved in a force incident. The data can be used to explore the various categories of force incidents and when and in which precinct they occurred. For any given incident, there may be one or more members of service involved. Since NYPD policy requires two-person patrols, most incidents will have at least two members. The data is used to populate the public facing Force Dashboard. (https://app.powerbigov.us/view?r=eyJrIjoiOGNhMjVhYTctMjk3Ny00MTZjLTliNDAtY2M2ZTQ5YWI3N2ViIiwidCI6IjJiOWY1N2ViLTc4ZDEtNDZmYi1iZTgzLWEyYWZkZDdjNjA0MyJ9).

In this project, we aim to analyze and gain insights into the performance of students based on various factors that influence their academic achievements. We have collected data related to students' demographic information, family background, and their exam scores in different subjects.

**********Key Objectives:*********

1. Performance Evaluation: Evaluate and understand the academic performance of students by analyzing their scores in various subjects.

2. Identifying Underlying Factors: Investigate factors that might contribute to variations in student performance, such as parental education, family size, and student attendance.

3. Visualizing Insights: Create data visualizations to present the findings effectively and intuitively.

Dataset Details:

• The dataset used in this analysis contains information about students, including their age, gender, parental education, lunch type, and test scores in subjects like mathematics, reading, and writing.

Analysis Highlights:

• We will perform a comprehensive analysis of the dataset, including data cleaning, exploration, and visualization to gain insights into various aspects of student performance.

• By employing statistical methods and machine learning techniques, we will determine the significant factors that affect student performance.

Why This Matters:

Understanding the factors that influence student performance is crucial for educators, policymakers, and parents. This analysis can help in making informed decisions to improve educational outcomes and provide support where it is most needed.

Acknowledgments:

We would like to express our gratitude to [mention any data sources or collaborators] for making this dataset available.

Please Note:

This project is meant for educational and analytical purposes. The dataset used is fictitious and does not represent any specific educational institution or individuals.

The Data Privacy Management Platform (DPMP) market is experiencing robust growth, projected to reach $1521.8 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 21.1%. This expansion is fueled by several key drivers. Increasingly stringent data privacy regulations globally, such as GDPR, CCPA, and others, are forcing organizations to invest heavily in solutions ensuring compliance. The rising volume and sensitivity of data being collected and processed necessitate sophisticated tools for managing consent, data subject requests, and breach notifications. Furthermore, the growing awareness of data breaches and their potential financial and reputational consequences drives demand for robust DPMP solutions. The market's segmentation reveals a strong preference for SaaS-based platforms due to their scalability, ease of deployment, and cost-effectiveness. Compliance Management, Risk Management, and Report & Analysis applications are the dominant segments, reflecting the core needs of organizations in this space. North America currently holds a significant market share, driven by robust regulatory frameworks and a high concentration of large enterprises. However, growth in regions like Asia-Pacific is expected to accelerate in the coming years due to increasing digitalization and the adoption of data privacy regulations. Competitive landscape analysis reveals a mix of established players and emerging innovative companies, indicating a dynamic and evolving market. The forecast period (2025-2033) anticipates sustained market expansion, driven by continued regulatory pressure and technological advancements. The integration of artificial intelligence (AI) and machine learning (ML) within DPMPs will further enhance automation, efficiency, and accuracy in data privacy management. Challenges remain, however, including the complexity of navigating diverse regulatory landscapes, the need for robust data governance frameworks, and concerns about data security within the platforms themselves. Despite these hurdles, the long-term outlook for the DPMP market is extremely positive, with continued high growth projected throughout the forecast period, significantly expanding the overall market size. Expansion into new sectors, such as healthcare and finance, which handle highly sensitive data, will also boost the market.

GDPR Compliance Tool Market Outlook

The GDPR Compliance Tool market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 3.8 billion by 2032, exhibiting a Compound Annual Growth Rate (CAGR) of approximately 11%. This robust growth is driven by the increasing emphasis on data protection and privacy regulations across the globe.

One of the primary growth factors of the GDPR Compliance Tool market is the growing awareness about data privacy and the stringent regulations being enforced by governments, particularly the General Data Protection Regulation (GDPR) in Europe. Businesses are increasingly recognizing the importance of compliance to avoid hefty fines and reputation damage, thereby driving the demand for advanced compliance tools. Moreover, the digitization of businesses and the exponential growth in data generation have necessitated sophisticated tools to manage and protect sensitive information, further boosting market growth.

Another significant factor contributing to the market's expansion is the rising number of cyber threats and data breaches. As data breaches become more frequent and sophisticated, organizations are compelled to invest in robust compliance tools to safeguard their data and ensure regulatory compliance. This has led to an increased adoption of GDPR compliance solutions across various industries, including BFSI, healthcare, and IT & telecommunications, which handle vast amounts of personal data.

Additionally, the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in compliance tools is enhancing their efficiency and effectiveness. These technologies enable real-time monitoring and analysis of data, helping organizations to quickly identify and address potential compliance issues. The continuous advancements in technology and the growing need for automated compliance solutions are expected to further propel the market growth in the coming years.

From a regional perspective, Europe holds a significant share of the GDPR Compliance Tool market due to the early adoption of GDPR regulations. However, other regions such as North America and Asia Pacific are also witnessing substantial growth due to increasing regulatory pressures and the need for data protection. North America, in particular, is expected to exhibit a high growth rate, driven by stringent regulations and the proactive approach of enterprises towards data privacy and security.

Component Analysis

The GDPR Compliance Tool market is segmented by component into software and services. The software segment dominates the market as organizations increasingly rely on advanced software solutions to automate and streamline their compliance processes. These software solutions offer various functionalities, including data mapping, data subject access requests (DSAR), consent management, and breach management, which are critical for GDPR compliance. The integration of AI and ML in these solutions further enhances their capabilities, making them indispensable for modern businesses.

Compliance software is designed to provide a comprehensive solution for managing all aspects of GDPR compliance. It helps organizations to maintain accurate records of processing activities, automate workflows, and ensure that all data handling practices comply with GDPR requirements. The growing complexity of data management and the need for real-time compliance monitoring are driving the adoption of these software solutions across various industries.

On the other hand, the services segment includes consulting, implementation, and support services. These services are essential for organizations that require expert guidance and assistance in implementing and maintaining GDPR compliance. Consulting services help organizations to assess their current compliance status, identify gaps, and develop a roadmap for achieving full compliance. Implementation services ensure that the chosen compliance tools are effectively integrated into the organization's existing systems and processes.

Support services play a crucial role in providing ongoing assistance to organizations, helping them to stay updated with the latest regulatory changes and ensuring continuous compliance. The demand for these services is particularly high among small and medium-sized enterprises (SMEs) that may lack the internal resources and expertise to manage compliance independently. As the number of SMEs adopting GDPR compliance tools increases, the services segment is expe

DP (Data Protection Act) / SAR (Subject Access Request) - Total Received

This dataset is about book subjects. It has 3 rows and is filtered where the books is Data collection : key debates and methods in social research. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Values are mean ± SD. CSA; cross-sectional area. There were no significant differences between patient and control groups in either dominant or non-dominant arms.*Significant differences: dominant vs. non-dominant arm, paired data Student-t test, p

Summary of discarded datapoints per approach (from dataset 3, with total n = 34.340) and threshold, providing the relation to the assessment using the complete parameter set ( = 100%) as well as the corresponding absolute number of datapoints exceeding the threshold (n, values in parentheses).

Dataset

This dataset was created by Mayank Tripathi

Contents

Wholesale Trade: Subject Series - Misc Subjects: End-of-Year Inventories for the U.S.: 2012 and 2011.

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

Accommodation and Food Services: Subject Series - Misc Subjects: Primary Type of Food Service for the U.S. and States: 2012.

Dataset

This dataset was created by Mira Küçük

Contents

To inform efforts to improve the discoverability of and access to biomedical datasets by providing a preliminary estimate of the number and type of datasets generated annually by National Institutes of Health (NIH)-funded researchers. Of particular interest is characterizing those datasets that are not deposited in a known data repository or registry, e.g., those for which a related journal article does not indicate that underlying data have been deposited in a known repository. Such “invisible” datasets comprise the “long tail” of biomedical data and pose significant practical challenges to ongoing efforts to improve discoverability of and access to biomedical research data. This study identified datasets used to support the NIH-funded research reported in articles published in 2011 and cited in PubMed® and deposited in PubMed Central® (PMC). After searching for all articles that acknowledged NIH support, we first identified articles that contained explicit mention of datasets being deposited in recognized repositories. Thirty members of the NIH staff then analyzed a random sample of the remaining articles to estimate how many and what types of datasets were used per article. Two reviewers independently examined each paper. Each dataset is titled Bigdata_randomsample_xxxx_xx. The xxxx refers to the set of articles the annotator looked at, while the xxidentifies the annotator that did the analysis. Within each dataset, the author has listed the number of datasets they identified within the articles that they looked at. For every dataset that was found, the annotators were asked to insert a new row into the spreadsheet, and then describe the dataset they found (e.g., type of data, subject of study, etc.). Each row in the spreadsheet was always prepended by the PubMed Identifier (PMID) where the dataset was found. Finally, the files 2013-08-07_Bigdatastudy_dataanalysis, Dataanalysis_ack_si_datasets, and Datasets additional random sample mention vs deposit 20150313 refer to the analysis that was performed based on each annotator's analysis of the publications they were assigned, and the data deposits identified from the analysis.

Thank you for your request for information about the following: Request ‘I wish to obtain the details if the medical assessor that reviewed my mother's claim..’ The NHS Business Services Authority (NHSBSA) received your request on 7 May 2025. We have handled your request under the Freedom of Information Act 2000 (FOIA). Our response Name(s) The following response does not relate to a specific claim or claimant. The request is being answered more generally given requests under FOIA are requester-blind, that is to say the identity of the requester is not taken into account when considering a request for information under FOIA. I can confirm that we do hold the names of the medical assessors however, we consider the names of the medical assessor to be personal data under the Data Protection Act 2018. Please be aware that I have decided not to release the names of the medical assessors as this information falls under the exemption in section 40 subsections 2 and 3(A)(a) of the FOIA. This is because disclosure of their names would result in their identification. As the requested information would allow a medical assessor to be identified, I consider this information is exempt. This is because it would breach the first data protection principle as: a) it is not fair to disclose their personal details to the world and is likely to cause damage or distress. b) these details are not of sufficient interest to the public to warrant an intrusion into their privacy. The requested information is exempt if disclosure would contravene any of the data protection principles. For disclosure to comply with the lawfulness, fairness, and transparency principle, we either need the consent of the data subject(s) or there must be a legitimate interest in disclosure. In addition, the disclosure must be necessary to meet the legitimate interest and finally, the disclosure must not cause unwarranted harm. This means that the NHSBSA is therefore required to conduct a balancing exercise between the legitimate interest of the applicant in disclosure against the rights and freedoms of the medical assessor. While I acknowledge that you have a legitimate interest in disclosure of the information, the disclosure of the requested information would cause unwarranted harm. Disclosure under FOIA is to the world and therefore the NHSBSA has to consider the overall impact of the disclosure and its duty of care. The expectation of the medical assessors is that they will remain anonymous and will therefore not be subject to contact or pressure from claimants or campaigning groups. Given the certainty that the name and will identify them, there is a reasonable expectation that this information will not be disclosed under the FOIA. Disclosing this information would be unfair and as such this would breach the UK General Data Protection Regulation first data protection principle. Please see the following link to view the section 40 exemption in full: https://www.legislation.gov.uk/ukpga/2000/36/section/40 Qualifications or Declarations of Interest The NHSBSA does not hold information on the medical assessors' qualifications or declarations of interest. This is because their medical qualifications and experience are the responsibility of the third-party medical assessment supplier.

Unique values and counts of metadata subject fields.

Unique values and counts of metadata subject fields.