The rapid rise in data production and use has outstripped countries’ ability to govern it. To build a new social contract on data, countries’ data governance frameworks need to safeguard against outcomes that harm people, while simultaneously enabling the potential for data to improve lives.

To guide countries in the development of these frameworks and facilitate cross-country learning, we developed a new data governance maturity model as part of the World Development Report 2021: Data for Better Lives. The model measures country-level data governance across legal, policy, infrastructural and institutional dimensions on a global scale.

The data governance maturity indicators as well as the datasets used to construct and validate the model are available for download. The data governance maturity indicators cover 198 economies, but the datasets used to validate the model have a smaller geographic coverage. Please respect the respective licenses of the datasets.

For more information on the model, measurement framework, and data please refer to the World Bank Technical Note "Measuring Data Governance Maturity of Countries: Towards a New Social Contract on Data". The analysis code can be found on the public repository: http://github.com/worldbank/data-governance-maturity.

This resource is a spreadsheet to allow input of maturity levels against the Jisc Digital Transformation Framework Maturity Model. This resource is ideal to be used alongside Alison Purvis' Maturity Model Cards which enable greater engagement from stakeholders with this spreadsheet supporting simple recording.Access Alison's Maturity Model Cards here or via the Related Materials below. The spreadsheet has a How to Use tab. Read this first. Enter data into the Data Input tab and reports will auto generate on other tabs.

This dataset contains scores and answers from the State Library of Queensland’s annual Open Data Pathway maturity assessment results. State Library completes the Open Data Institute Open Data Pathway maturity assessment annually to review the activities of our open data program.

The Open Data Pathway is a self-assessment tool developed by the Open Data Institute (ODI) in the UK: http://pathway.theodi.org/

The assessment poses a series of multiple-choice questions based around the themes of Data management processes, Knowledge & skills, Customer support & engagement, Investment & financial performance, and Strategic oversight. The answers we provide allow the tool to score our progress for each theme. These results help to identify areas of improvement and set specific actions to meet our open data goals for the financial year.

Digital twin technology has the potential to enhance construction efficiency, reduce costs, and minimize errors. However, its application during the construction phase remains at an early stage, largely constrained by the absence of standardized guidelines and principles. To address this challenge, it is essential to establish a comprehensive and universal maturity assessment framework to facilitate the effective implementation of this technology in the construction phase of building projects. This study focuses on two critical aspects: the development of the maturity assessment framework and its empirical validation. The proposed framework encompasses a maturity assessment indicator system covering five dimensions: acquisition layer, data layer, modeling layer, analysis layer, and application layer. For the first time, an optimized matter-element model based on dynamic thresholds and nonlinear correlation is introduced to improve the accuracy of maturity assessments. Furthermore, a feedback mechanism based on Importance-Performance Analysis (IPA) is utilized to clarify the formulation of optimization strategies. Finally, the framework is applied to the CAZ Innovation Industrial Park construction phase in Xinyang, Henan Province. The assessment results demonstrate that the system precisely measures the project’s maturity level and provides effective improvement recommendations. This study not only offers technological support for assessing and optimizing the digital twin maturity during the construction phase of building projects but also provides methodological insights into global digital twin maturity assessments.

This research carries out an in-depth assessment of the application of the FAIR (Findable, Accessible, Interoperable and Reusable) principles by the Swiss scientific community specialized in architecture, and consequently its positioning in the context of open science. The FAIR maturity assessment of research data is based on the use of maturity models. They provide a structured framework for implementing and improving data management practices.

This dataset accompanies the study “Framework for open data maturity – Country profiles and clusters”, developed by the Publications Office of the EU and the European Commission ([link to study]) as a complement to the Open Data Maturity (ODM) assessment. The dataset goes beyond the traditional ODM categories (beginners, followers, fast-trackers, trendsetters) by providing: • Six descriptive parameters with underlying metrics and country-specific values, showing how countries implement open data in practice. • Five macro clusters grouping countries with similar governance structures, data infrastructures, or other intrinsic characteristics. Its aim is to give a more detailed view of each country’s open data profile and to support peer learning, allowing countries in comparable situations to exchange experiences and best practices.

Digital twin technology has the potential to enhance construction efficiency, reduce costs, and minimize errors. However, its application during the construction phase remains at an early stage, largely constrained by the absence of standardized guidelines and principles. To address this challenge, it is essential to establish a comprehensive and universal maturity assessment framework to facilitate the effective implementation of this technology in the construction phase of building projects. This study focuses on two critical aspects: the development of the maturity assessment framework and its empirical validation. The proposed framework encompasses a maturity assessment indicator system covering five dimensions: acquisition layer, data layer, modeling layer, analysis layer, and application layer. For the first time, an optimized matter-element model based on dynamic thresholds and nonlinear correlation is introduced to improve the accuracy of maturity assessments. Furthermore, a feedback mechanism based on Importance-Performance Analysis (IPA) is utilized to clarify the formulation of optimization strategies. Finally, the framework is applied to the CAZ Innovation Industrial Park construction phase in Xinyang, Henan Province. The assessment results demonstrate that the system precisely measures the project’s maturity level and provides effective improvement recommendations. This study not only offers technological support for assessing and optimizing the digital twin maturity during the construction phase of building projects but also provides methodological insights into global digital twin maturity assessments.

This dataset contains data collected during a study "Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities" (Sustainable Cities and Society (SCS), vol.82, 103906) conducted by Martin Lnenicka (University of Pardubice), Anastasija Nikiforova (University of Tartu), Mariusz Luterek (University of Warsaw), Otmane Azeroual (German Centre for Higher Education Research and Science Studies), Dandison Ukpabi (University of Jyväskylä), Visvaldis Valtenbergs (University of Latvia), Renata Machova (University of Pardubice).

This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data by means of the expert assessment of 34 portals representing 22 smart cities, with 36 features.

It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities.

Purpose of the expert assessment The data in this dataset were collected in the result of the applying the developed benchmarking framework for assessing the compliance of open (government) data portals with the principles of transparency-by-design proposed by Lněnička and Nikiforova (2021)* to 34 portals that can be considered to be part of open data ecosystems in smart cities, thereby carrying out their assessment by experts in 36 features context, which allows to rank them and discuss their maturity levels and (4) based on the results of the assessment, defining the components and unique models that form the open data ecosystem in the smart city context.

Methodology Sample selection: the capitals of the Member States of the European Union and countries of the European Economic Area were selected to ensure a more coherent political and legal framework. They were mapped/cross-referenced with their rank in 5 smart city rankings: IESE Cities in Motion Index, Top 50 smart city governments (SCG), IMD smart city index (SCI), global cities index (GCI), and sustainable cities index (SCI). A purposive sampling method and systematic search for portals was then carried out to identify relevant websites for each city using two complementary techniques: browsing and searching. To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (Lněnička & Nikiforova, 2021)*. The benchmarking supposes the collection of quantitative data, which makes this task an acceptability task. A six-point Likert scale was applied for evaluating the portals. Each sub-dimension was supplied with its description to ensure the common understanding, a drop-down list to select the level at which the respondent (dis)agree, and a comment to be provided, which has not been mandatory. This formed a protocol to be fulfilled on every portal. Each sub-dimension/feature was assessed using a six-point Likert scale, where strong agreement is assessed with 6 points, while strong disagreement is represented by 1 point. Each website (portal) was evaluated by experts, where a person is considered to be an expert if a person works with open (government) data and data portals daily, i.e., it is the key part of their job, which can be public officials, researchers, and independent organizations. In other words, compliance with the expert profile according to the International Certification of Digital Literacy (ICDL) and its derivation proposed in Lněnička et al. (2021)* is expected to be met. When all individual protocols were collected, mean values and standard deviations (SD) were calculated, and if statistical contradictions/inconsistencies were found, reassessment took place to ensure individual consistency and interrater reliability among experts’ answers. *Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605 *Lněnička, M., Machova, R., Volejníková, J., Linhartová, V., Knezackova, R., & Hub, M. (2021). Enhancing transparency through open government data: the case of data portals and their features and capabilities. Online Information Review.

Test procedure (1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator (2) all sub-dimensions in one dimension are aggregated, and then the average value is calculated based on the number of sub-dimensions – the resulting average stands for a dimension value - eight values per portal (3) the average value from all dimensions are calculated and then mapped to the maturity level – this value of each portal is also used to rank the portals.

Description of the data in this data set Sheet#1 "comparison_overall" provides results by portal Sheet#2 "comparison_category" provides results by portal and category Sheet#3 "category_subcategory" provides list of categories and its elements

Format of the file .xls

Licenses or restrictions CC-BY

For more info, see README.txt

BackgroundDigital health maturity models allow healthcare organizations to evaluate digital health capability and to develop roadmaps for improving patient care through technology. There are many models available commercially for healthcare providers to use to assess their digital health maturity. Currently, there are limited evidence-based methods to assess the quality, utility, and efficacy of maturity models to select the most appropriate model for the given context.ObjectiveTo develop a framework to assess digital maturity models and facilitate recommendations for digital maturity model selection.MethodsA systematic, consultative, and iterative process was used. Literature analyses and a stakeholder needs analysis (n = 23) was conducted to develop content and design considerations. These considerations were incorporated into the initial version of the framework developed by researchers in a design workshop. External stakeholder review (n = 20) and improvements strengthened and finalized the framework.ResultsThe criteria of the framework include assessment of healthcare context, feasibility, integrity, completeness and actionability. Users can compare model performance in order to select the most appropriate model for their context.ConclusionThe framework provides healthcare stakeholders with a consistent and objective methodology to compare digital health maturity models, informing approaches to choosing a suitable model. This is a critical step as healthcare evolves towards a digital health system focused on improving the quality of care, reducing costs and improving the provider and consumer experience.

Data Product Readiness Scoring Market Outlook

According to our latest research, the global data product readiness scoring market size reached USD 1.18 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.4% from 2025 to 2033. This dynamic growth is primarily driven by the accelerating demand for data-driven decision-making across industries, the increasing complexity of data ecosystems, and the critical need for organizations to assess the maturity and usability of their data products before deployment. By 2033, the market is forecasted to attain a value of USD 5.15 billion, reflecting the pivotal role of data product readiness scoring in the evolving digital landscape.

The surge in digital transformation initiatives across enterprises globally is a key growth factor for the data product readiness scoring market. Organizations are increasingly leveraging advanced analytics, artificial intelligence, and machine learning to gain actionable insights from their data assets. However, the success of these initiatives is heavily contingent upon the quality, governance, and integration of data products. As a result, businesses are adopting readiness scoring solutions to systematically evaluate whether their data products meet established standards for quality, compliance, and usability. This trend is further amplified by the growing recognition that data-driven innovation hinges on the reliability and maturity of underlying data assets, thus propelling the adoption of readiness scoring frameworks.

Another significant driver is the rising regulatory scrutiny and compliance requirements in sectors such as BFSI, healthcare, and government. Strict mandates around data privacy, integrity, and traceability have compelled organizations to implement rigorous data governance practices. Data product readiness scoring tools enable these organizations to ensure that their data products are compliant with industry regulations before deployment, thereby reducing the risk of non-compliance penalties and reputational damage. This compliance-centric approach is particularly pronounced in regions such as North America and Europe, where regulatory landscapes are highly mature and constantly evolving, making readiness scoring an indispensable part of the data lifecycle.

The proliferation of cloud computing and the increasing adoption of hybrid and multi-cloud environments have also played a crucial role in market expansion. As organizations migrate their data assets to cloud platforms, the complexity of managing and integrating disparate data sources has grown exponentially. Data product readiness scoring solutions help organizations navigate this complexity by providing a standardized framework to assess data readiness across diverse environments. This capability not only accelerates the time-to-insight but also ensures that data products are scalable, interoperable, and aligned with business objectives, further fueling market growth.

Regionally, North America continues to dominate the data product readiness scoring market, accounting for the largest share in 2024. This leadership is attributed to the strong presence of technology giants, early adoption of advanced data management practices, and a highly regulated business environment. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increasing investments in data infrastructure, and the rising awareness of data quality and governance in developing economies. Europe remains a key market, characterized by stringent data protection regulations and a mature enterprise landscape, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions accelerate their digital transformation journeys.

Component Analysis

The data product readiness scoring market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses a wide array of platforms and tools designed to automate the assessment of data product maturity, quality, and compliance. These solutions leverage advanced algorithms, machine learning, and artificial intelligence to provide real-time insights into the readiness of data products for deployment. The increasing sophistication of these tools, coupled with their ability to integrate seamlessly with existing data management systems, has made software the dominant component

Digital twin technology has the potential to enhance construction efficiency, reduce costs, and minimize errors. However, its application during the construction phase remains at an early stage, largely constrained by the absence of standardized guidelines and principles. To address this challenge, it is essential to establish a comprehensive and universal maturity assessment framework to facilitate the effective implementation of this technology in the construction phase of building projects. This study focuses on two critical aspects: the development of the maturity assessment framework and its empirical validation. The proposed framework encompasses a maturity assessment indicator system covering five dimensions: acquisition layer, data layer, modeling layer, analysis layer, and application layer. For the first time, an optimized matter-element model based on dynamic thresholds and nonlinear correlation is introduced to improve the accuracy of maturity assessments. Furthermore, a feedback mechanism based on Importance-Performance Analysis (IPA) is utilized to clarify the formulation of optimization strategies. Finally, the framework is applied to the CAZ Innovation Industrial Park construction phase in Xinyang, Henan Province. The assessment results demonstrate that the system precisely measures the project’s maturity level and provides effective improvement recommendations. This study not only offers technological support for assessing and optimizing the digital twin maturity during the construction phase of building projects but also provides methodological insights into global digital twin maturity assessments.

Connected Vehicle Maturity Model Assessments Market Outlook

According to our latest research, the global Connected Vehicle Maturity Model Assessments market size reached USD 1.12 billion in 2024, demonstrating robust momentum driven by rapid technological advancements and regulatory initiatives. The market is projected to expand at a CAGR of 18.6% during the forecast period, reaching a value of USD 5.97 billion by 2033. The surge in connected vehicle deployments, increasing adoption of telematics, and the need for standardized maturity assessments are key growth drivers shaping the evolution of this market.

A primary growth factor for the Connected Vehicle Maturity Model Assessments market is the exponential increase in the integration of advanced connectivity solutions within automotive platforms. Automakers and technology providers are consistently innovating to enhance vehicle-to-everything (V2X) communication, leading to a proliferation of connected vehicles on global roads. This expansion necessitates comprehensive maturity assessments to evaluate technology readiness, process efficiency, and organizational capability. Furthermore, the rising complexity of automotive ecosystems, including the convergence of IoT, AI, and big data analytics, compels stakeholders to adopt standardized maturity models. These models help ensure optimal performance, compliance with industry standards, and seamless integration across platforms, thereby fueling market demand.

Another significant driver is the intensifying regulatory environment across major economies. Governments and regulatory bodies are mandating stringent compliance and security protocols for connected vehicles to safeguard user data and ensure road safety. This regulatory push is prompting OEMs, fleet operators, and mobility service providers to invest in maturity assessments that address security, compliance, and organizational preparedness. The adoption of these assessments not only helps organizations meet regulatory requirements but also enhances their competitive positioning by demonstrating commitment to safety, reliability, and innovation. As a result, the market is witnessing increased uptake among both established automotive manufacturers and emerging mobility players.

Additionally, the rapid digital transformation within the transportation and logistics industries is accelerating the adoption of Connected Vehicle Maturity Model Assessments. Fleet management companies, smart city planners, and logistics providers are leveraging these assessments to optimize operational efficiency, enhance service delivery, and drive sustainability initiatives. The ability to benchmark against industry best practices and identify areas for improvement is becoming a strategic imperative. Moreover, the shift towards cloud-based deployment models is enabling scalable and flexible assessment solutions, further broadening the market’s appeal across enterprises of all sizes.

Regionally, North America dominates the Connected Vehicle Maturity Model Assessments market, followed closely by Europe and Asia Pacific. North America’s leadership is attributed to early adoption of connected vehicle technologies, a strong regulatory framework, and the presence of major automotive OEMs and technology providers. Europe benefits from robust government initiatives promoting smart mobility and stringent data protection regulations, while Asia Pacific is rapidly emerging as a high-growth region due to rising vehicle production, urbanization, and investments in smart city infrastructure. Latin America and the Middle East & Africa are also witnessing gradual uptake, driven by growing awareness and investments in digital mobility solutions.

Assessment Type Analysis

The Assessment Type segment is pivotal in the Connected Vehicle Maturity Model Assessments market, encompassing Technology Readiness, Process Maturity, Organizational Capability, Security & Compliance, and Other specialized assessment types. Technology Readiness asse

The National Institute of Standards and Technology (NIST) provides a Cybersecurity Framework (CSF) for benchmarking and measuring the maturity level of cybersecurity programs across all industries. The City uses this framework and toolset to measure and report on its internal cybersecurity program. The foundation for this measure is the Framework Core, a set of cybersecurity activities, desired outcomes, and applicable references that are common across critical infrastructure/industry sectors. These activities come from the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) published standard, along with the information security and customer privacy controls it references (NIST 800 Series Special Publications). The Framework Core presents industry standards, guidelines, and practices in a manner that allows for communication of cybersecurity activities and outcomes across the organization from the executive level to the implementation/operations level. The Framework Core consists of five concurrent and continuous functions: identify, protect, detect, respond, and recover. When considered together, these functions provide a high-level, strategic view of the lifecycle of an organization’s management of cybersecurity risk. The Framework Core identifies underlying key categories and subcategories for each function, and matches them with example references, such as existing standards, guidelines, and practices for each subcategory. This page provides data for the Cybersecurity performance measure. Cybersecurity Framework cumulative score summary per fiscal year quarter (Performance Measure 5.12) The performance measure page is available at 5.12 Cybersecurity. Additional Information Source: Maturity assessment / https://www.nist.gov/topics/cybersecurityContact: Scott CampbellContact E-Mail: Scott_Campbell@tempe.govData Source Type: ExcelPreparation Method: The data is a summary of a detailed and confidential analysis of the city's cybersecurity program. Maturity scores of subcategories within NIST CFS are combined, averaged, and rolled up to a summary score for each major category.Publish Frequency: AnnualPublish Method: ManualData Dictionary

The foundation for this measure is the Framework Core, a set of cybersecurity activities, desired outcomes and applicable references that are common across critical infrastructure/industry sectors. These activities come from the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) published standard, along with the information security and customer privacy controls it references (NIST 800 Series Special Publications). The Framework Core presents industry standards, guidelines, and practices in a manner that allows for communication of cybersecurity activities and outcomes across the organization from the executive level to the implementation/operations level. The Framework Core consists of five concurrent and continuous functions – identify, protect, detect, respond, and recover. When considered together, these functions provide a high-level, strategic view of the lifecycle of an organization’s management of cybersecurity risk. The Framework Core identifies underlying key categories and subcategories for each function, and matches them with example references, such as existing standards, guidelines and practices for each subcategory. This page provides data for the Cybersecurity performance measure.Cybersecurity Framework (CSF) scores by each CSF category per fiscal year quarter (Performance Measure 5.12)The performance measure dashboard is available at 5.12 Cybersecurity.Additional InformationSource: Maturity assessment /https://www.nist.gov/topics/cybersecurityContact: Scott CampbellContact E-Mail: Scott_Campbell@tempe.govData Source Type: ExcelPreparation Method: The data is a summary of a detailed and confidential analysis of the city's cyber security program. Maturity scores of subcategories within NIST CFS are combined, averaged and rolled up to a summary score for each major category.Publish Frequency: Annual

Digital twin technology has the potential to enhance construction efficiency, reduce costs, and minimize errors. However, its application during the construction phase remains at an early stage, largely constrained by the absence of standardized guidelines and principles. To address this challenge, it is essential to establish a comprehensive and universal maturity assessment framework to facilitate the effective implementation of this technology in the construction phase of building projects. This study focuses on two critical aspects: the development of the maturity assessment framework and its empirical validation. The proposed framework encompasses a maturity assessment indicator system covering five dimensions: acquisition layer, data layer, modeling layer, analysis layer, and application layer. For the first time, an optimized matter-element model based on dynamic thresholds and nonlinear correlation is introduced to improve the accuracy of maturity assessments. Furthermore, a feedback mechanism based on Importance-Performance Analysis (IPA) is utilized to clarify the formulation of optimization strategies. Finally, the framework is applied to the CAZ Innovation Industrial Park construction phase in Xinyang, Henan Province. The assessment results demonstrate that the system precisely measures the project’s maturity level and provides effective improvement recommendations. This study not only offers technological support for assessing and optimizing the digital twin maturity during the construction phase of building projects but also provides methodological insights into global digital twin maturity assessments.

Cybersecurity Maturity Model for Airports Market Outlook

According to our latest research, the global cybersecurity maturity model for airports market size reached USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.6% expected from 2025 to 2033. By 2033, the market is projected to reach USD 4.17 billion. This impressive growth is primarily driven by the surging frequency and sophistication of cyber threats targeting the aviation sector, increased regulatory mandates for data protection, and the rapid digital transformation of airport operations worldwide.

One of the primary growth factors propelling the cybersecurity maturity model for airports market is the exponential rise in digitalization across airport operations. As airports increasingly adopt advanced technologies such as IoT-enabled infrastructure, automated passenger management systems, and cloud-based solutions, their attack surface expands considerably. This digital evolution, while enhancing operational efficiency and passenger experience, exposes airports to heightened cyber risks. Consequently, airport authorities are prioritizing the adoption of comprehensive cybersecurity maturity frameworks to systematically assess, implement, and improve their cyber defense postures. The integration of these maturity models allows airports to identify vulnerabilities, benchmark their security capabilities, and ensure compliance with global standards, thereby driving significant market demand.

Another major driver for this market is the tightening regulatory landscape and the growing emphasis on data privacy and protection. Regulatory bodies such as the International Civil Aviation Organization (ICAO), the European Union Aviation Safety Agency (EASA), and the U.S. Transportation Security Administration (TSA) are mandating rigorous cybersecurity protocols for airports. These directives require airports to implement robust cybersecurity maturity models to safeguard sensitive passenger data, critical infrastructure, and operational technologies. The need for regular security assessments, incident response planning, and continuous monitoring is compelling airports to invest in advanced cybersecurity solutions and services. This regulatory push, combined with the reputational and financial risks associated with cyber incidents, is a significant catalyst for market expansion.

Furthermore, the increasing frequency of high-profile cyberattacks targeting airports has heightened awareness and urgency among stakeholders. Incidents involving ransomware, data breaches, and disruption of airport services have underscored the critical importance of cybersecurity maturity. Airports are now recognizing that traditional, reactive security measures are insufficient to counter today’s sophisticated threats. Instead, a proactive, maturity-based approach is essential to achieve resilience and ensure business continuity. The adoption of cybersecurity maturity models enables airports to systematically progress from basic to advanced security practices, fostering a culture of continuous improvement. This paradigm shift towards proactive risk management is expected to sustain market growth over the forecast period.

Regionally, North America currently dominates the cybersecurity maturity model for airports market, supported by substantial investments in airport infrastructure modernization and a strong regulatory framework. However, the Asia Pacific region is poised for the fastest growth, driven by rapid airport expansion, increasing passenger traffic, and heightened awareness about cyber threats. Europe also exhibits significant market potential, owing to stringent data protection regulations and robust aviation security initiatives. Meanwhile, the Middle East and Africa, along with Latin America, are witnessing gradual adoption, with governments and airport operators ramping up efforts to enhance their cybersecurity maturity in response to evolving threats.

Component Analysis

The component segment of the cybersecurity maturity model for airports market is bifurcated into solutions and services. Solutions encompass a wide array of cybersecurity technologies specifically tailored for airport environments, including advanced threat detection systems, security information and event management (SIEM) platforms, and identity access management (IAM) solutions. The demand for these solutions is escalating as airports strive to prote

Digital twin technology has the potential to enhance construction efficiency, reduce costs, and minimize errors. However, its application during the construction phase remains at an early stage, largely constrained by the absence of standardized guidelines and principles. To address this challenge, it is essential to establish a comprehensive and universal maturity assessment framework to facilitate the effective implementation of this technology in the construction phase of building projects. This study focuses on two critical aspects: the development of the maturity assessment framework and its empirical validation. The proposed framework encompasses a maturity assessment indicator system covering five dimensions: acquisition layer, data layer, modeling layer, analysis layer, and application layer. For the first time, an optimized matter-element model based on dynamic thresholds and nonlinear correlation is introduced to improve the accuracy of maturity assessments. Furthermore, a feedback mechanism based on Importance-Performance Analysis (IPA) is utilized to clarify the formulation of optimization strategies. Finally, the framework is applied to the CAZ Innovation Industrial Park construction phase in Xinyang, Henan Province. The assessment results demonstrate that the system precisely measures the project’s maturity level and provides effective improvement recommendations. This study not only offers technological support for assessing and optimizing the digital twin maturity during the construction phase of building projects but also provides methodological insights into global digital twin maturity assessments.

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Master Data Management (MDM) Solutions Market Size 2024-2028

The master data management (mdm) solutions market size is forecast to increase by USD 20.29 billion, at a CAGR of 16.72% between 2023 and 2028.

Major Market Trends & Insights

North America dominated the market and accounted for a 33% growth during the forecast period.
By the Deployment - Cloud segment was valued at USD 7.18 billion in 2022
By the End-user - BFSI segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 0 billion
Market Future Opportunities: USD 0 billion
CAGR : 16.72%
North America: Largest market in 2022

Market Summary

The market is witnessing significant growth as businesses grapple with the increasing volume and complexity of data. According to recent estimates, the global MDM market is expected to reach a value of USD115.7 billion by 2026, growing at a steady pace. This expansion is driven by the growing advances in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) technologies, which enable more effective data management and analysis. Despite this progress, data privacy and security concerns remain a major challenge. A 2021 survey revealed that 60% of organizations reported data privacy as a significant concern, while 58% cited security as a major challenge. MDM solutions offer a potential solution, providing a centralized and secure platform for managing and governing data across the enterprise. By implementing MDM solutions, businesses can improve data accuracy, consistency, and completeness, leading to better decision-making and operational efficiency.

What will be the Size of the Master Data Management (MDM) Solutions Market during the forecast period?

Explore market size, adoption trends, and growth potential for master data management (mdm) solutions market Request Free SampleThe market continues to evolve, driven by the increasing complexity of managing large and diverse data volumes. Two significant trends emerge: a 15% annual growth in data discovery tools usage and a 12% increase in data governance framework implementations. Role-based access control and data security assessments are integral components of these solutions. Data migration strategies employ data encryption algorithms and anonymization methods for secure transitions. Data quality improvement is facilitated through data reconciliation tools, data stewardship programs, and data quality monitoring via scorecards and dashboards. Data consolidation projects leverage data integration pipelines and versioning control. Metadata repository design and data governance maturity are crucial for effective MDM implementation. Data standardization methods, data lineage visualization, and data profiling reports enable data integration and improve data accuracy. Data stewardship training and masking techniques ensure data privacy and compliance. Data governance KPIs and metrics provide valuable insights for continuous improvement. Data catalog solutions and data versioning control enhance data discovery and enable efficient data access. Data loss prevention and data quality dashboard are essential for maintaining data security and ensuring data accuracy.

How is this Master Data Management (MDM) Solutions Industry segmented?

The master data management (mdm) solutions industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. DeploymentCloudOn-premisesEnd-userBFSIHealthcareRetailOthersGeographyNorth AmericaUSCanadaEuropeGermanyUKAPACChinaRest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Master data management solutions have gained significant traction in the business world, with market adoption increasing by 18.7% in the past year. This growth is driven by the need for organizations to manage and maintain accurate, consistent, and secure data across various sectors. Metadata management, data profiling methods, and data deduplication techniques are essential components of master data management, ensuring data quality and compliance with regulations. Data stewardship roles, data warehousing solutions, and data hub architecture facilitate effective data management and integration. Cloud-based master data management solutions, which account for 35.6% of the market share, offer agility, scalability, and real-time data availability. Data virtualization platforms, data validation processes, and data consistency checks ensure data accuracy and reliability. Hybrid MDM deployments, ETL processes, and data governance policies enable seamless data integration and management. Data security protocols, data qualit

Digital twin technology has the potential to enhance construction efficiency, reduce costs, and minimize errors. However, its application during the construction phase remains at an early stage, largely constrained by the absence of standardized guidelines and principles. To address this challenge, it is essential to establish a comprehensive and universal maturity assessment framework to facilitate the effective implementation of this technology in the construction phase of building projects. This study focuses on two critical aspects: the development of the maturity assessment framework and its empirical validation. The proposed framework encompasses a maturity assessment indicator system covering five dimensions: acquisition layer, data layer, modeling layer, analysis layer, and application layer. For the first time, an optimized matter-element model based on dynamic thresholds and nonlinear correlation is introduced to improve the accuracy of maturity assessments. Furthermore, a feedback mechanism based on Importance-Performance Analysis (IPA) is utilized to clarify the formulation of optimization strategies. Finally, the framework is applied to the CAZ Innovation Industrial Park construction phase in Xinyang, Henan Province. The assessment results demonstrate that the system precisely measures the project’s maturity level and provides effective improvement recommendations. This study not only offers technological support for assessing and optimizing the digital twin maturity during the construction phase of building projects but also provides methodological insights into global digital twin maturity assessments.