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.

Discrete InSitu (within stream) Water Quality data summary for Glacier National Park (2007-2009). Water Quality values are summarized at the event scale.

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.

Twenty RNA-seq datasets generated from human peripheral blood mononuclear cell (PBMC)1. Accession number, RNA Integrity Numbers (RIN), and the median Transcript Integrity Numbers (medTIN), total reads, total reads with mapping quality >30, and number of gene with at least 10 reads are listed. The PBMC samples were stored at room temperature for 0 h, 12 h, 24 h, 48 h and 84 h. Each time point contains 4 individuals (replicates). (XLS 9 kb)

Wetlands Ecological Integrity Discrete Water Quality Logger data at Florissant Fossil Beds National Monument, Glacier National Park, Great Sand Dunes National Park, and Rocky Mountain National Park 2007-2021.

DNA was extracted from museum eggshell specimens. Two mitochondrial mini barcodes (12S rDNA) were amplified via PCR and sequenced via NGS.

IntroductionClinical trial registries serve a key role in tracking the trial enterprise. We are interested in the record of trials sites in India. In this study, we focused on the European Union Clinical Trial Registry (EUCTR). This registry is complex because a given study may have records from multiple countries in the EU, and therefore a given study ID may be represented by multiple records. We wished to determine what steps are required to identify the studies that list sites in India that are registered with EUCTR.MethodsWe used two methodologies. Methodology A involved downloading the EUCTR database and querying it. Methodology B used the search function on the registry website.ResultsDiscrepant information, on whether or not a given study listed a site in India, was identified at three levels: (i) the methodology of examining the database; (ii) the multiple records of a given study ID; and (iii) the multiple fields within a given record. In each of these situations, there was no basis to resolve the discrepancy, one way or another.DiscussionThis work contributes to methodologies for more accurate searches of trial registries. It also adds to the efforts of those seeking transparency in trial data.

Data provided as part of the "Find Your Watershed" viewer on the Watershed Protection pages of http://www.austintexas.gov/

The Electronic Device History Record (eDHR) solution market is experiencing robust growth, projected to reach a market size of $283 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 10.6% from 2025 to 2033. This expansion is driven by increasing regulatory compliance needs within the medical device and pharmaceutical industries, the rising adoption of cloud-based solutions for enhanced accessibility and collaboration, and the growing demand for improved data management and traceability throughout the product lifecycle. The market is segmented by deployment type (cloud-based and on-premises) and application (medical, diagnostics, and other). Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and accessibility advantages. The medical and diagnostics segments are the major revenue contributors, fueled by the stringent regulatory requirements and the need for comprehensive device history tracking to ensure patient safety and product quality. Key players like Siemens, Tulip, and MasterControl are actively shaping the market landscape through continuous innovation and strategic partnerships. The market's growth is also influenced by factors like rising digitization in healthcare, increasing adoption of Industry 4.0 technologies, and the growing awareness among manufacturers regarding the benefits of eDHR systems. The future growth of the eDHR solution market will be further propelled by the expanding adoption of advanced analytics capabilities within these systems, enabling improved decision-making and proactive risk management. The increasing integration of eDHR solutions with other enterprise systems, such as Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP), will streamline data flow and enhance overall operational efficiency. However, challenges such as the high initial investment costs associated with implementing eDHR solutions and the need for skilled professionals to manage and maintain these systems might somewhat restrain market growth. Nevertheless, the long-term benefits in terms of improved compliance, reduced operational costs, and enhanced product quality are expected to outweigh these challenges, ensuring a sustained growth trajectory for the eDHR market in the coming years.

The US Geological Survey (USGS) conducted a study (Carlisle and others 2017) with a national-scale dataset composed of ecological data from the USGS National Water-Quality Assessment Project and the US Environmental Protection Agency, matched to USGS streamgaging sites. In a follow-up study (Carlisle and others 2019), additional data from three regional assessments conducted by USGS were combined with data from the original study, and these new data are published here. Using all of the aforementioned datasets, the follow-up study (Carlisle and others, 2019) then developed regional-scale model predictions of the relation between streamflow modification and indicators of biological integrity. These model predictions, presented as graphics, are published here.

This data-set by the Electoral Integrity Project evaluates the quality of elections held around the world. Based on a rolling survey collecting the views of election experts, this research provides independent and reliable evidence to compare whether countries meet international standards of electoral integrity. PEI-4.0 cumulative release covers 180 national parliamentary and presidential contests held worldwide in 139 countries from 1 July 2012 to 31 December 2015. For each contest, 40 election experts receive an electronic invitation to fill the survey. The survey includes assessments from 2,080 election experts, with a mean response rate of 28%. The study collects 49 indicators to compare elections. These indicators are clustered to evaluate eleven stages in the electoral cycle as well as generating an overall summary Perception of Electoral Integrity (PEI) 100-point index and comparative ranking. The datasets are available for analysis at three levels: COUNTRY-level (139 cases); ELECTION-level (180 cases), and also EXPERT-level (2,080). Each dataset can be downloaded in STATA, SPSS, CSV and EXCEL formats.

An STM Working Group on Image Alteration and Duplication Detection has been working on best-practice recommendations that outline a structured approach to support editors and others applying image integrity screening as part of pre-publication quality control checks or post-publication investigation of image and data integrity issues at scholarly journals, books, preprint servers, or data repositories. It provides principles and a three-tier classification for different types of image and data aberrations commonly detected in image integrity screens of figures in research papers and for a consideration of impact on the scholarly study; it also recommends actions journal editors may take to protect the scholarly record. With these recommendations, the STM Working Group aims to contribute a consistent, structured and efficient framework for handling image integrity issues both within and between journals and publishers. The framework should support editors in safeguarding research integrity and fortifying the scientific process for the benefit of the scientific community.

The recommendations are open for comments until October 31st. The final recommendations, in which we will attempt to process all suggestions and recommendation, will be presented at the STM Innovations Seminar on December 7th.

To comment on the document, please register and use the 'comment' icon on the top right of your screen, or send your comments to rossum@stm-assoc.org.

This data-set by the Electoral Integrity Project evaluates the quality of elections held around the world. Based on a rolling survey collecting the views of election experts, this research provides independent and reliable evidence to compare whether countries meet international standards of electoral integrity. PEI-4.5 cumulative release covers 213 national parliamentary and presidential contests held worldwide in 153 countries from 1 July 2012 to 30 June 2016. For each contest, 40 election experts receive an electronic invitation to fill the survey. The survey includes assessments from 2417 election experts, with a mean response rate of 28%. The study collects 49 indicators to compare elections. These indicators are clustered to evaluate eleven stages in the electoral cycle as well as generating an overall summary Perception of Electoral Integrity (PEI) 100-point index and comparative ranking. The datasets are available for analysis at three levels: COUNTRY-level (153 cases); ELECTION-level (213 cases), and also EXPERT-level (2,417). Each dataset can be downloaded in STATA, SPSS, CSV and EXCEL formats.

Twelve RNA-seq datasets generated from human brain Glioblastoma (GBM) cell line2. Accession number, RNA Integrity Numbers (RIN), the median Transcript Integrity Numbers (medTIN), total read pairs, read pairs with mapping quality > 30, and number of genes with at least 10 reads are listed. (XLS 7 kb)

Internal auditing demands innovative and secure solutions in today’s business environment, with increasing competitive pressure and frequent occurrences of risky and illegal behaviours. Blockchain along with secure databases like encryption improves internal audit security through immutability and transparency. Hence integrating blockchain with homomorphic encryption and multi-factor authentication improves privacy and mitigates computational overhead. Recently, blockchain applications for internal audits in the enterprise sector are still emerging. Thus, blockchain technology in auditing provides the benefits of enhanced transparency and immutability in data processing, which can establish new solutions for internal auditing but still lacks encryption techniques. The research proposed a framework called “BlockCryptoAudit” to enhance internal audit processes through cryptographic encryption methods and blockchain technology, ensuring secure and transparent audit operations. The proposed approach integrates an additive homomorphic Paillier encryption scheme with blockchain to create a safe and tamper-resident audit trail. Utilizing homomorphic Paillier encryption, BlockCryptoAudit ensures that computations may be performed on encrypted audit data while safeguarding data privacy. The applied blockchain hyperledger component guarantees the immutability and transparency of encrypted audit records, resulting in a decentralized and tamper-resistant record. By limiting data accessibility to authorized individuals based on specified responsibilities, role-based access restrictions handled using smart contracts further strengthen security. The study protects audit data’s security and confidentiality by encrypting it and putting it on a blockchain. The study compares the proposed BlockCryptoAudit with models like B-OAP, BSE-DF, and EG-FLB regarding risk mitigation, audit quality, security overhead, and audit trail effectiveness. With little security overhead, BlockCryptoAudit beats out B-OAP, BSE-DF, and EG-FLB in terms of risk mitigation (98%) and audit quality (99%). It is an effective way to improve internal audit processes and guarantee data integrity due to its high performance.

Research integrity and research fairness have gained considerable momentum in the past decade and have direct implications for global health epidemiology. Research integrity and research fairness principles should be equally nurtured to produce high quality impactful research – but bridging the two can lead to practical and ethical dilemmas. In order to provide practical guidance to researchers and epidemiologist, we set out to develop good epidemiological practice guidelines specifically for global health epidemiology, targeted at stakeholders involved in the commissioning, conduct, appraisal and publication of global health research.

We developed preliminary guidelines based on targeted online searches on existing best practices for epidemiological studies and sought to align these with key elements of global health research and research fairness. We validated these guidelines through a Delphi consultation study, to reach a consensus among a wide representation of stakeholders.

A total of 45 experts provided input on the first round of GEP e-Delphi consultation, and 40 in the second. Respondents covered a range of organisations (including for example academia, ministries, NGOs, research funders, technical agencies) involved in epidemiological studies from countries around the world. A selection of eight experts were invited for a face-to-face meeting. The final guidelines consists of a set of six standards and 42 accompanying criteria including study preparation, study protocol and ethical review, data collection, data management, analysis, reporting and dissemination.

This database only includes anonymised responses of participants who agreed to their data being shared in this depository , i.e.19 out of the 45 (Round 1) and 40 (Round 2) participants.

Understanding how climate change will contribute to ongoing declines in sagebrush ecological integrity is critical for informing natural resource management. We assessed potential future changes in sagebrush ecological integrity under a range of scenarios using an individual plant-based simulation model, integrated with remotely sensed estimates of current sagebrush ecological integrity. The simulation model allowed us to estimate how climate change, wildfire, and invasive annuals interact to alter the potential abundance of key plant functional types that influence sagebrush ecological integrity: sagebrush, perennial grasses, and annual grasses. We provide GeoTIFFs of biome-wide projections of future sagebrush ecological integrity (as described in Holdrege et al., 2024) under two representative concentration pathways (RCP4.5 and RCP8.5) and time-periods (2031-2060 and 2071-2100) and we provide these projections for multiple model assumptions. Additionally, this data set provides accompanying projections of three of the components of sagebrush ecological integrity, which are the Q (‘quality’, see Doherty et al., 2022) scores for sagebrush, perennial forbs and grasses, and annual forbs and grasses. Additional GeoTIFFs included provide current (2017-2020) Q scores and sagebrush ecological integrity, as well as projected changes in the extent of Core Sagebrush Areas, Growth Opportunity Areas, and Other Rangeland Areas.

Data Classification Market size was valued at USD 1664.66 Million in 2024 and is projected to reach USD 9486.25 Million by 2031, growing at a CAGR of 24.3% during the forecast period 2024-2031.

Global Data Classification Market Drivers

The market drivers for the Data Classification Market can be influenced by various factors. These may include:

Increasing Data Volume: In order to maintain data security, compliance, and effective use, there is an increasing requirement to manage and classify the data produced by enterprises in an exponentially growing amount.
Regulatory Compliance: Organizations must categorize their data based on the sensitivity levels required by strict data protection laws like the GDPR, CCPA, HIPAA, and others. Adoption of data classification solutions is driven by compliance requirements, which guarantee adherence to regulatory standards and prevent heavy penalties.

Data Security Concerns: Organizations are concentrating on strengthening their data security procedures due to the increase in cyber threats and data breaches. Classifying data makes it easier to find sensitive information and implement the right security measures to keep it safe from theft or unwanted access.

Growing Adoption of Cloud Services: As cloud computing services become more widely used, strong data classification techniques are required to guarantee data security and compliance, particularly when data is transferred between different cloud environments and storage locations.
Increasing Awareness of Data Privacy: The need for solutions that allow for better management and protection of sensitive data through classification and encryption is being driven by heightened awareness of data privacy issues among consumers and enterprises.
Combining Data Loss Prevention (DLP) Systems: Through the identification, monitoring, and prevention of sensitive information leakage or unlawful transfer, data categorization integrated with DLP systems improves data protection capabilities.
Emergence of AI and Machine Learning Technologies: By incorporating these technologies into data categorization systems, data may be identified and classified more automatically and accurately, saving labor and increasing efficiency.
Demand for Data Governance and Lifecycle Management: In order to maintain data quality, integrity, and compliance throughout its lifecycle, organizations are realizing more and more how important it is to have effective data governance and lifecycle management. A key component of putting into practice efficient data governance procedures is data classification.

Pharmaceutical Manufacturing Software Market size was valued at USD 2.84 Billion in 2023 and is projected to reach USD 12.13 Billion by 2030, growing at a CAGR of 22.9% during the forecast period 2024-2030.

Global Pharmaceutical Manufacturing Software Market Drivers

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

Strict Standards for Regulatory Compliance: The pharmaceutical sector is bound by stringent laws, which encompass Good Manufacturing Practices (GMP), FDA rules in the US, and additional global standards including EU GMP. Software for pharmaceutical production helps businesses comply with these requirements, which increases demand for software solutions that make paperwork and regulatory compliance easier.

Growing Complexity of Manufacturing Processes: The emergence of biologics, customized medicine, and sophisticated drug delivery systems are some of the reasons contributing to the growing complexity of pharmaceutical manufacturing processes. Pharmaceutical producers benefit from software systems that provide capabilities like batch tracking, real-time monitoring, and process automation, which help them manage complexity and increase operational efficiency

Emphasis on Quality and Risk Management: To guarantee the safety and effectiveness of pharmaceutical products, quality management is crucial in the pharmaceutical sector. Software for pharmaceutical production helps businesses maintain high levels of quality throughout the manufacturing process by giving them tools for risk assessment, deviation management, and quality control.

Expanding Adoption of Industry 4.0 Technologies: Automation, the Internet of Things (IoT), artificial intelligence (AI), and data analytics are some of the technologies that are revolutionizing the pharmaceutical industry. The market for advanced manufacturing software is being driven by software solutions that take advantage of these technologies to help businesses optimize production processes, cut waste, and improve decision-making.

Pharmaceutical firms are facing pressure to enhance their operational efficiency and minimize expenses without compromising on product quality and compliance. Manufacturing software solutions save costs and boost productivity by streamlining manufacturing processes, allocating resources optimally, and minimizing downtime.

Growing Contract Manufacturing Organizations (CMOs) and Contract: Development and Manufacturing Organizations (CDMOs) Outsourcing: A large number of pharmaceutical businesses contract manufacture and develop drugs. Software solutions that facilitate communication, data sharing, and process integration between pharmaceutical companies and their outsourcing partners are therefore becoming more and more necessary.

Transition to Personalized Treatment and Smaller Batch Production: Pharmaceutical producers must adapt and become more nimble in their production methods in light of the move to personalized treatment and smaller batch sizes. Manufacturing software solutions that facilitate flexible scheduling, quick changeover, and batch customization enable businesses to adjust to the shifting needs of small-batch production and tailored treatment.

Globalization of Pharmaceutical Supply Chains: With production facilities dispersed over several nations and regions, pharmaceutical supply chains are becoming more and more global. Pharmaceutical organizations benefit from manufacturing software solutions that include multilingual support, multi-site capability, and regulatory compliance features for effective management of global supply chains.

Emphasis on Data Integrity and Security: Given the sensitive nature of data related to pharmaceutical manufacturing, data integrity, and security are major issues in the pharmaceutical sector. Software solutions that guarantee data encryption, access control, and integrity are manufactured to assist businesses in adhering to data privacy laws and safeguarding their intellectual property.

The rise of cloud-based manufacturing software solutions: As opposed to conventional on-premises software systems, cloud-based manufacturing software solutions are more affordable, scalable, and easily accessible. Because of its flexibility, ability to collaborate in real-time, and reduced initial expenses, cloud-based manufacturing software is becoming more and more popular among pharmaceutical organizations.

IntroductionIn India, regulatory trials, which require the drug regulator’s permission, must be registered with the Clinical Trials Registry-India (CTRI) as of 19 March 2019. In this study, for about 300 trials, we aimed to identify the CTRI record that matched the trial for which the regulator had given permission. After identifying ‘true pairs’, our goal was to determine whether the sites and Principal Investigators mentioned in the permission letter were the same as those mentioned in the CTRI record.MethodsWe developed a methodology to compare the regulator’s permission letters with CTRI records. We manually validated 151 true pairs by comparing the titles, the drug interventions, and the indications. We then examined discrepancies in their trial sites and Principal Investigators.ResultsOur findings revealed substantial variations in the number and identity of sites and Principal Investigators between the permission letters and the CTRI records.DiscussionThese discrepancies raise concerns about the accuracy and transparency of regulatory trials in India. We recommend easier data extraction from regulatory documents, cross-referencing regulatory documents and CTRI records, making public the changes to approval letters, and enforcing oversight by Institutional Ethics Committees for site additions or deletions. These steps will increase transparency around regulatory trials running in India.