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 size and share of the market is categorized based on Application (Data profiling tools, Data cleansing tools, Data enrichment tools, Data validation tools, Data governance tools) and Product (Data accuracy improvement, Data integrity management, Data standardization, Data compliance, Data integration) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

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 ManagementExecutive SummaryIn the age of data and information, it is imperative that the City of Virginia Beach strategically utilize its data assets. Through expanding data access, improving quality, maintaining pace with advanced technologies, and strengthening capabilities, IT will ensure that the city remains at the forefront of digital transformation and innovation. The Data and Information Management team works under the purpose:“To promote a data-driven culture at all levels of the decision making process by supporting and enabling business capabilities with relevant and accurate information that can be accessed securely anytime, anywhere, and from any platform.”To fulfill this mission, IT will implement and utilize new and advanced technologies, enhanced data management and infrastructure, and will expand internal capabilities and regional collaboration.Introduction and JustificationThe Information technology (IT) department’s resources are integral features of the social, political and economic welfare of the City of Virginia Beach residents. In regard to local administration, the IT department makes it possible for the Data and Information Management Team to provide the general public with high-quality services, generate and disseminate knowledge, and facilitate growth through improved productivity.For the Data and Information Management Team, it is important to maximize the quality and security of the City’s data; to develop and apply the coherent management of information resources and management policies that aim to keep the general public constantly informed, protect their rights as subjects, improve the productivity, efficiency, effectiveness and public return of its projects and to promote responsible innovation. Furthermore, as technology evolves, it is important for public institutions to manage their information systems in such a way as to identify and minimize the security and privacy risks associated with the new capacities of those systems.The responsible and ethical use of data strategy is part of the City’s Master Technology Plan 2.0 (MTP), which establishes the roadmap designed by improve data and information accessibility, quality, and capabilities throughout the entire City. The strategy is being put into practice in the shape of a plan that involves various programs. Although these programs was specifically conceived as a conceptual framework for achieving a cultural change in terms of the public perception of data, it basically covers all the aspects of the MTP that concern data, and in particular the open-data and data-commons strategies, data-driven projects, with the aim of providing better urban services and interoperability based on metadata schemes and open-data formats, permanent access and data use and reuse, with the minimum possible legal, economic and technological barriers within current legislation.Fundamental valuesThe City of Virginia Beach’s data is a strategic asset and a valuable resource that enables our local government carry out its mission and its programs effectively. Appropriate access to municipal data significantly improves the value of the information and the return on the investment involved in generating it. In accordance with the Master Technology Plan 2.0 and its emphasis on public innovation, the digital economy and empowering city residents, this data-management strategy is based on the following considerations.Within this context, this new management and use of data has to respect and comply with the essential values applicable to data. For the Data and Information Team, these values are:Shared municipal knowledge. Municipal data, in its broadest sense, has a significant social dimension and provides the general public with past, present and future knowledge concerning the government, the city, society, the economy and the environment.The strategic value of data. The team must manage data as a strategic value, with an innovative vision, in order to turn it into an intellectual asset for the organization.Geared towards results. Municipal data is also a means of ensuring the administration’s accountability and transparency, for managing services and investments and for maintaining and improving the performance of the economy, wealth and the general public’s well-being.Data as a common asset. City residents and the common good have to be the central focus of the City of Virginia Beach’s plans and technological platforms. Data is a source of wealth that empowers people who have access to it. Making it possible for city residents to control the data, minimizing the digital gap and preventing discriminatory or unethical practices is the essence of municipal technological sovereignty.Transparency and interoperability. Public institutions must be open, transparent and responsible towards the general public. Promoting openness and interoperability, subject to technical and legal requirements, increases the efficiency of operations, reduces costs, improves services, supports needs and increases public access to valuable municipal information. In this way, it also promotes public participation in government.Reuse and open-source licenses. Making municipal information accessible, usable by everyone by default, without having to ask for prior permission, and analyzable by anyone who wishes to do so can foster entrepreneurship, social and digital innovation, jobs and excellence in scientific research, as well as improving the lives of Virginia Beach residents and making a significant contribution to the city’s stability and prosperity.Quality and security. The city government must take firm steps to ensure and maximize the quality, objectivity, usefulness, integrity and security of municipal information before disclosing it, and maintain processes to effectuate requests for amendments to the publicly-available information.Responsible organization. Adding value to the data and turning it into an asset, with the aim of promoting accountability and citizens’ rights, requires new actions, new integrated procedures, so that the new platforms can grow in an organic, transparent and cross-departmental way. A comprehensive governance strategy makes it possible to promote this revision and avoid redundancies, increased costs, inefficiency and bad practices.Care throughout the data’s life cycle. Paying attention to the management of municipal registers, from when they are created to when they are destroyed or preserved, is an essential part of data management and of promoting public responsibility. Being careful with the data throughout its life cycle combined with activities that ensure continued access to digital materials for as long as necessary, help with the analytic exploitation of the data, but also with the responsible protection of historic municipal government registers and safeguarding the economic and legal rights of the municipal government and the city’s residents.Privacy “by design”. Protecting privacy is of maximum importance. The Data and Information Management Team has to consider and protect individual and collective privacy during the data life cycle, systematically and verifiably, as specified in the general regulation for data protection.Security. Municipal information is a strategic asset subject to risks, and it has to be managed in such a way as to minimize those risks. This includes privacy, data protection, algorithmic discrimination and cybersecurity risks that must be specifically established, promoting ethical and responsible data architecture, techniques for improving privacy and evaluating the social effects. Although security and privacy are two separate, independent fields, they are closely related, and it is essential for the units to take a coordinated approach in order to identify and manage cybersecurity and risks to privacy with applicable requirements and standards.Open Source. It is obligatory for the Data and Information Management Team to maintain its Open Data- Open Source platform. The platform allows citizens to access open data from multiple cities in a central location, regional universities and colleges to foster continuous education, and aids in the development of data analytics skills for citizens. Continuing to uphold the Open Source platform with allow the City to continually offer citizens the ability to provide valuable input on the structure and availability of its data. Strategic areasIn order to deploy the strategy for the responsible and ethical use of data, the following areas of action have been established, which we will detail below, together with the actions and emblematic projects associated with them.In general, the strategy pivots on the following general principals, which form the basis for the strategic areas described in this section.Data sovereigntyOpen data and transparencyThe exchange and reuse of dataPolitical decision-making informed by dataThe life cycle of data and continual or permanent accessData GovernanceData quality and accessibility are crucial for meaningful data analysis, and must be ensured through the implementation of data governance. IT will establish a Data Governance Board, a collaborative organizational capability made up of the city’s data and analytics champions, who will work together to develop policies and practices to treat and use data as a strategic asset.Data governance is the overall management of the availability, usability, integrity and security of data used in the city. Increased data quality will positively impact overall trust in data, resulting in increased use and adoption. The ownership, accessibility, security, and quality, of the data is defined and maintained by the Data Governance Board.To improve operational efficiency, an enterprise-wide data catalog will be created to inventory data and track metadata from various data sources to allow for rapid data asset discovery. Through the data catalog, the city will

Data collected to assess water quality conditions in the natural creeks, aquifers, and lakes in the Austin area. This is raw data, provided directly from our Field Sample database (FSDB) and should be considered provisional. Data may or may not have been reviewed by project staff. Data quality (QC) flags have been provided to aid in the assessment of the data; R-flagged data should be considered suspect, but is provided as it represents taxpayer expenditure and the efforts undertaken to characterize the status of our environment. Note that some data over time will be improved and edited for accuracy and that QC flags can change based upon changes in project criteria. Additional data may be available from other agencies (USGS, TCEQ, LCRA) and should be requested from them directly; some of this data may appear in those datasets.

Success.ai provides indispensable access to B2B contact data combined with LinkedIn, e-commerce, and private company details, enabling businesses to drive robust B2B lead generation and enrich their marketing strategies across various industries globally.

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Strategic Use Cases Powered by Success.ai:

• B2B Lead Generation: Utilize detailed company data to identify and connect with potential business clients effectively.
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• B2B Marketing: Deploy precise marketing strategies based on accurate B2B contact and company data. -Sales Data Enrichment: Equip your sales team with the latest data to refine their approach and close deals faster.
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Begin your journey with Success.ai today and leverage our B2B contact data to enhance your company’s strategic marketing and sales objectives. Contact us for customized solutions that propel your business to new heights of data-driven success.

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Data Wrangling Market size was valued at USD 1.63 Billion in 2024 and is projected to reach USD 3.2 Billion by 2031, growing at a CAGR of 8.80 % during the forecast period 2024-2031.

Global Data Wrangling Market Drivers

Growing Volume and Variety of Data: As digitalization has progressed, organizations have produced an exponential increase in both volume and variety of data. Data from a variety of sources, including social media, IoT devices, sensors, and workplace apps, is included in this, both structured and unstructured. Data wrangling tools are an essential part of contemporary data management methods because they allow firms to manage this heterogeneous data landscape effectively.

Growing Adoption of Advanced Analytics: To extract useful insights from data, companies in a variety of sectors are utilizing advanced analytics tools like artificial intelligence and machine learning. Nevertheless, access to clean, well-researched data is essential to the accomplishment of many analytics projects. The need for data wrangling solutions is fueled by the necessity of ensuring that data is accurate, consistent, and clean for usage in advanced analytics models.

Self-service data preparation solutions are becoming more and more necessary as data volumes rise. These technologies enable business users to prepare and analyze data on their own without requiring significant IT assistance. Platforms for data wrangling provide non-technical users with easy-to-use interfaces and functionalities that make it simple for them to clean, manipulate, and combine data. Data wrangling solutions are being used more quickly because of this self-service approach’s ability to increase agility and facilitate quicker decision-making within enterprises.

Emphasis on Data Governance and Compliance: With the rise of regulated sectors including healthcare, finance, and government, data governance and compliance have emerged as critical organizational concerns. Data wrangling technologies offer features for auditability, metadata management, and data quality control, which help with adhering to data governance regulations. The adoption of data wrangling solutions is fueled by these features, which assist enterprises in ensuring data integrity, privacy, and regulatory compliance.

Big Data Technologies’ Emergence: Companies can now store and handle enormous amounts of data more affordably because to the emergence of big data technologies like Hadoop, Spark, and NoSQL databases. However, efficient data preparation methods are needed to extract value from massive data. Organizations may accelerate their big data analytics initiatives by preprocessing and cleansing large amounts of data at scale with the help of data wrangling solutions that seamlessly interact with big data platforms.

Put an emphasis on cost-cutting and operational efficiency: Organizations are under pressure to maximize operational efficiency and cut expenses in the cutthroat business environment of today. Organizations can increase productivity and reduce resource requirements by implementing data wrangling solutions, which automate manual data preparation processes and streamline workflows. Furthermore, the danger of errors and expensive aftereffects is reduced when data quality problems are found and fixed early in the data pipeline.

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...

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.

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

Lucror Analytics: Proprietary Fixed Income Data for Credit Quality & Bond Valuation

At Lucror Analytics, we provide cutting-edge corporate data solutions tailored to fixed income professionals and organizations in the financial sector. Our datasets encompass issuer and issue-level credit quality, bond fair value metrics, and proprietary scores designed to offer nuanced, actionable insights into global bond markets that help you stay ahead of the curve. Covering over 3,300 global issuers and over 80,000 bonds, we empower our clients to make data-driven decisions with confidence and precision.

By leveraging our proprietary C-Score, V-Score , and V-Score I models, which utilize CDS and OAS data, we provide unparalleled granularity in credit analysis and valuation. Whether you are a portfolio manager, credit analyst, or institutional investor, Lucror’s data solutions deliver actionable insights to enhance strategies, identify mispricing opportunities, and assess market trends.

What Makes Lucror’s Fixed Income Data Unique?

Proprietary Credit and Valuation Models Our proprietary C-Score, V-Score, and V-Score I are designed to provide a deeper understanding of credit quality and bond valuation:

C-Score: A composite score (0-100) reflecting an issuer's credit quality based on market pricing signals such as CDS spreads. Responsive to near-real-time market changes, the C-Score offers granular differentiation within and across credit rating categories, helping investors identify mispricing opportunities.

V-Score: Measures the deviation of an issue’s option-adjusted spread (OAS) from the market fair value, indicating whether a bond is overvalued or undervalued relative to the market.

V-Score I: Similar to the V-Score but benchmarked against industry-specific fair value OAS, offering insights into relative valuation within an industry context.

Comprehensive Global Coverage Our datasets cover over 3,300 issuers and 80,000 bonds across global markets, ensuring 90%+ overlap with prominent IG and HY benchmark indices. This extensive coverage provides valuable insights into issuers across sectors and geographies, enabling users to analyze issuer and market dynamics comprehensively.

Data Customization and Flexibility We recognize that different users have unique requirements. Lucror Analytics offers tailored datasets delivered in customizable formats, frequencies, and levels of granularity, ensuring that our data integrates seamlessly into your workflows.

High-Frequency, High-Quality Data Our C-Score, V-Score, and V-Score I models and metrics are updated daily using end-of-day (EOD) data from S&P. This ensures that users have access to current and accurate information, empowering timely and informed decision-making.

How Is the Data Sourced? Lucror Analytics employs a rigorous methodology to source, structure, transform and process data, ensuring reliability and actionable insights:

Proprietary Fixed Income Data Models: Our scores are derived from proprietary quant algorithms based on CDS spreads, OAS, and other issuer and bond data.

Global Data Partnerships: Our collaborations with S&P and other reputable data providers ensure comprehensive and accurate datasets.

Data Cleaning and Structuring: Advanced processes ensure data integrity, transforming raw inputs into actionable insights.

Primary Use Cases

1. Portfolio Construction & Rebalancing Lucror’s C-Score provides a granular view of issuer credit quality, allowing portfolio managers to evaluate risks and identify mispricing opportunities. With CDS-driven insights and daily updates, clients can incorporate near-real-time issuer/bond movements into their credit assessments.

2. Portfolio Optimization The V-Score and V-Score I allow portfolio managers to identify undervalued or overvalued bonds, supporting strategies that optimize returns relative to credit risk. By benchmarking valuations against market and industry standards, users can uncover potential mean-reversion opportunities and enhance portfolio performance.

3. Risk Management With data updated daily, Lucror’s models provide dynamic insights into market risks. Organizations can use this data to monitor shifts in credit quality, assess valuation anomalies, and adjust exposure proactively.

4. Strategic Decision-Making Our comprehensive datasets enable financial institutions to make informed strategic decisions. Whether it’s assessing the fair value of bonds, analyzing industry-specific credit spreads, or understanding broader market trends, Lucror’s data delivers the depth and accuracy required for success.

Why Choose Lucror Analytics? Lucror Analytics is committed to providing high-quality, actionable data solutions tailored to the evolving needs of the financial sector. Our unique combination of proprietary models, rigorous sourcing of high-quality data, and customizable delivery ensures that users have the insights they need to make smarter deci...

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Two files contain the …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Two files contain the preliminary site data and water quality data required by the Bureau of Meteorology (BoM) under the conditions of the Water Act 2008. Please understand that in order to achieve these preliminary files, there has been quite a deal of work over a short amount of time. This has been greatly assisted by (and in fact would not have been possible without) the BoM’s financial assistance in terms of funding of Project NSW 6.1 - Remodelling, update and migration of the DECC water quality database. Note however, that due to the relatively short timeframe involved, a number of caveats still need to be placed on these preliminary files until a full QA/QC and data integrity and consistency check has been completed on the database. This is currently being implemented and it is recommended that additional contact is made with DECC prior to the release or use of this data. DECC will be continuing to refine and QA/QC this database and will inform BoM if this affects any data in these preliminary data files. Some of this water data has been collected under an agreement with the Murray Darling Basin Commission (now the Murray Darling Authority). Part of this agreement deals with confidentiality regarding the identification of sites on individual landholder properties. In particular: “By providing locations at this (valley name or zone name only) accuracy there is reduced risk that future sampling at that location is confounded by intentional activities at the site. Types of impacts that might be envisaged include the unauthorised collection of rare or endangered fish or macroinvertebrate species at identifiable SRA sample sites, the undesired identification of SRA sites that exist on private property, comparisons of data collected at SRA sites to deduce some causal effect due to the landholding on which those sites exist and so on†. Any supply of/access to/reporting of this data should take such confidentialities into account. With the Site data, latitude and longitudes or eastings and northings are still be checked/added for those sites without such data. An updated Site file will be forwarded to BoM once it is finalised. Lastly, this data is supplied in good faith, exercising all due care and attention. No representation is made about the accuracy, completeness or suitability of the information for any particular purpose. DECC does not accept liability for any damage which may occur to any person or organization taking action or not on the basis of these data. Dataset History This data was provided to the Bureau of Meteorology under the water regulations from the NSW Department of Environment & Heritage Dataset Citation NSW - Department of Environment and Heritage (2009) NSW Department of Environment and Heritage Historic Water Quality Data. Bioregional Assessment Source Dataset. Viewed 07 April 2016, http://data.bioregionalassessments.gov.au/dataset/4c5f7318-2567-4614-aa35-46aa0eb045f2.

Information Stewardship Application Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024-2031.

Global Information Stewardship Application Market Drivers

The market drivers for the Information Stewardship Application Market can be influenced by various factors. These may include:

Growing Data Volume: The demand for information stewardship apps is being driven by the exponential expansion in data generation across many industries, which calls for effective data management and governance solutions.
Regulatory Compliance: Organizations are being pushed to implement information stewardship apps in order to assure compliance and avoid heavy penalties due to stringent rules and compliance requirements linked to data privacy and security, such as GDPR and CCPA.
Data Quality Management: Information stewardship systems that assist in cleaning, validating, and controlling data quality are becoming more and more popular as decision-making processes depend on good data quality and accuracy.
Risk management: As a result of organizations’ growing recognition of the value of data governance in reducing the risks associated with data breaches and misuse, information stewardship solutions are being used at a higher rate.
Initiatives for Digital Transformation: As businesses go through digital transformation, there is an increasing focus on using data as a strategic asset, which drives the requirement for strong data governance frameworks that are made possible by information stewardship tools.
Cloud Adoption: Information stewardship apps are growing in demand as a result of the movement of data to cloud platforms, which necessitates improved data governance and stewardship to guarantee data integrity and security.
Industry-Specific Requirements: Because of the sensitive nature of their data, some industries, like healthcare, banking, and retail, have particular requirements for data governance. As a result, the usage of customized information stewardship solutions has expanded.
Integration with Business Intelligence Tools: By improving data visibility and accessibility, information stewardship apps can be integrated with business intelligence and analytics tools to drive market growth.
Rise in Data-Driven Decision Making: As organizations increasingly rely on data to influence their decisions, the need for accurate and dependable data is becoming more pressing, which is driving up demand for information stewardship software.
Technological Developments: Information stewardship applications are becoming more capable and efficient because to ongoing developments in fields like artificial intelligence, machine learning, and big data analytics.

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.

Data Observability Software Market Analysis The global data observability software market is anticipated to reach USD XXX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The rising adoption of cloud-based data platforms and the need for real-time data monitoring and analysis are key drivers of this growth. Organizations are increasingly relying on data to make strategic decisions, leading to the demand for software that can provide visibility and insights into data quality, reliability, and performance. The market is segmented based on application into large enterprises and SMEs, and by type into cloud-based and on-premise solutions. Cloud-based solutions are gaining popularity due to their scalability, flexibility, and lower cost of ownership. Major companies in this market include Monte Carlo, Metaplane, SquaredUp, IBM, Unravel Data, Soda, and Sifflet. North America is currently the largest regional market, followed by Europe and Asia Pacific. The adoption of data observability software is expected to continue to accelerate as organizations become more data-driven and the need for ensuring data integrity and reliability increases.

The scientific community has entered an era of big data. However, with big data comes big responsibilities, and best practices for how data are contributed to databases have not kept pace with the collection, aggregation, and analysis of big data. Here, we rigorously assess the quantity of data for specific leaf area (SLA) available within the largest and most frequently used global plant trait database, the TRY Plant Trait Database, exploring how much of the data were applicable (i.e., original, representative, logical, and comparable) and traceable (i.e., published, cited, and consistent). Over three-quarters of the SLA data in TRY either lacked applicability or traceability, leaving only 22.9% of the original data usable compared to the 64.9% typically deemed usable by standard data cleaning protocols. The remaining usable data differed markedly from the original for many species, which led to altered interpretation of ecological analyses. Though the data we consider here make up only 4.5% of SLA data within TRY, similar issues of applicability and traceability likely apply to SLA data for other species as well as other commonly measured, uploaded, and downloaded plant traits. We end with suggested steps forward for global ecological databases, including suggestions for both uploaders to and curators of databases with the hope that, through addressing the issues raised here, we can increase data quality and integrity within the ecological community.

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Enterprise Information Management Market Size 2025-2029

The enterprise information management (EIM) market size is forecast to increase by USD 106.1 billion at a CAGR of 17.5% between 2024 and 2029.

Enterprise Information Management (EIM) is a critical business function that encompasses Enterprise Content Management (ECM) and Enterprise Data Management (EDM). The market is witnessing significant growth due to the increasing demand for digitalization and digital transformation. Businesses are recognizing the importance of managing their information effectively to enhance operational efficiency and improve decision-making. However, the integration challenges related to unscalable applications pose a significant hurdle in implementing EIM solutions. ECM plays a vital role in managing unstructured data, such as documents, images, and videos, while EDM focuses on managing structured data, such as financial and transactional data. The integration of these two functional areas is essential for a comprehensive EIM strategy.
Moreover, the adoption of advanced technologies like artificial intelligence (AI) is gaining momentum in EIM. AI-enabled solutions can automate routine tasks, provide insights from data, and enhance the overall value of EIM systems. The market is expected to continue growing as businesses increasingly recognize the importance of effective information management in the digital age.

What will be the Size of the Enterprise Information Management (EIM) Market During the Forecast Period?

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The market encompasses data management, content management, and information governance solutions that enable organizations to effectively collect, process, store, and deliver critical information. This market is experiencing significant growth due to the increasing volume, velocity, and complexity of data, driven by digital transformation, cloud computing, and high performance computing. Integration challenges persist as organizations seek to manage diverse information assets across their lifecycle, ensuring availability, integrity, security, usability, and data quality. Manufacturing companies, among others, are investing in EIM solutions to optimize operations and gain a competitive advantage. Artificial intelligence (AI) and machine learning technologies are increasingly integrated into EIM solutions to enhance data analysis and decision-making capabilities.
Open-source solutions are transforming big business processes by improving timeliness, reducing fraud, and enhancing enterprise management across various sectors, including banking, financial services, insurance, energy and power, IT and telecommunication, transportation and logistics, hospitality, and aerospace & defense. These cloud-based software platforms help mitigate mismanagement and breach while supporting risk management and accessibility, enabling efficient digital workflows and business management. In addition, software development in these industries is driving innovation and improving operational efficiency.

How is this Enterprise Information Management (EIM) Industry segmented and which is the largest segment?

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

Deployment

  On-premises
  Cloud-based


End-user

  BFSI
  Healthcare
  Manufacturing
  Retail
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan


  South America



  Middle East and Africa

By Deployment Insights

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

Enterprise Information Management (EIM) refers to the practices and technologies used by organizations to manage their data and information throughout its lifecycle. This includes data management, content management, information governance, and addressing integration challenges. EIM solutions provide integrated software for data quality, availability, integrity, security, usability, collection, storage, organization, integration, dissemination, decision making, and business operations. The market for EIM is driven by digital transformation initiatives, regional expansion, and the increasing volume of digital technologies, IoT devices, social media, and online transactions. Large enterprises and SMEs alike seek cost optimization, flexibility, and cost effectiveness through EIM solutions.

AI, high performance computing, machine learning, data analytics, automation, and predictive analytics are integral to EIM, enabling organizations to gain a competitive advantage in the technological innovation-driven business landscape. EIM solutions are used in various sectors, including finance

Metadata Management Tools Market size was valued at USD 8.09 Billion in 2024 and is projected to reach USD 25.07 Billion by 2031, growing at a CAGR of 20.7% from 2024 to 2031.

Global Metadata Management Tools Market Drivers

The requirements for data governance and compliance: Organizations use metadata management technologies to guarantee compliance, data quality, and data lineage due to growing legal requirements and the need for strong data governance.
The swift expansion of big data and analytics: Large-scale data generated by enterprises requires efficient metadata management in order to be understood, tracked, and used. This is due to the growth of big data and analytics programs.
Initiatives for Digital Transformation: Digitally transforming organizations understand the value of metadata in managing heterogeneous data sources, promoting interoperability, and guaranteeing data integration between systems.
The intricacy of data ecosystems: Organizations’ data ecosystems becoming more complex as they deal with a wider range of data sources, types, and architectures. Tools for metadata management aid in sifting through and understanding this complexity.
Cloud Usage: Metadata management technologies are becoming more and more necessary as cloud environments and hybrid or multi-cloud architectures are used to guarantee data visibility, control, and governance across various platforms.
A greater emphasis on master data management and data quality: The need for metadata management tools to preserve and improve the integrity of organizational data is being driven by the increased understanding of the significance of master data management (MDM) and data quality.

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.

One type of electronic monitoring of Alaska groundfish catch has been conducted by Pacific States Marine Fisheries Commission using an electronic monitoring (EM) system to collect catch accounting data using video and sensor data of selected fishing vessels in Alaska. Video recordings of fish catch composition aboard selected vessels are collected are stored on hard drives in an effort to track vessel catch and discards to accurately debit discarded catch from the individual fishing quota (IFQ) account of each account holder. This information is collected in place of the sampling for species composition of the catch conducted by human at-sea catch monitors or observers. Reviewers of the videos enter data from the drives and maintain data integrity and quality. Raw, reviewed electronic monitoring data collected by Pacific States Marine Fisheries Commission must have additional data items added to it to conform to the standard format of data normally collected by Alaska observers in order for the data to be processed by catch accounting of the NMFS Alaska Regional Office. The EM_OBSINT tables contain these transformed data. These data, like data collected by Alaska groundfish observers, and transmitted electronically to the AFSC and are the source data for those interfaces used for fishery management, scientific inquiry and fishing activity monitoring by industry.