A full parametric and linear specification may be insufficient to capture complicated patterns in studies exploring complex features, such as those investigating age-related changes in brain functional abilities. Alternatively, a partially linear model (PLM) consisting of both parametric and nonparametric elements may have a better fit. This model has been widely applied in economics, environmental science, and biomedical studies. In this article, we introduce a novel statistical inference framework that equips PLM with high estimation efficiency by effectively synthesizing summary information from external data into the main analysis. Such an integrative scheme is versatile in assimilating various types of reduced models from the external study. The proposed method is shown to be theoretically valid and numerically convenient, and it ensures a high-efficiency gain compared to classic methods in PLM. Our method is further validated using two data applications by evaluating the risk factors of brain imaging measures and blood pressure.

Abstract

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Homabay,Kakamega,Nakuru and Nairobi counties

Analysis unit

Private health facilities that provide T-safe services under the In Their Hands(ITH) Program.

Universe

1.Adolescent girls aged 15-19 who enrolled on the T-safe platform and received services and those who enrolled but did not receive services from the ITH facilities. 2.Service providers incharge of provision of T-safe services in the ITH facilities. 3.Mobilisers incharge of adolescent girls aged 15-19 recruitment into the T-safe program.

Sampling procedure

Qualitative Sampling

IDI participants were selected purposively from ITH intervention areas and facilities located in the four ITH intervention counties; Homa Bay, Nakuru, Kakamega and Nairobi respectively which were selected for the midline survey. Study participants were identified from selected intervention facilities. We interviewed one service provider of adolescent friendly ITH services per facility. Additionally, we conducted IDI's with adolescent girls' who were enrolled and using/had used the ITH platform to access reproductive health services or enrolled but may not have accessed the services for other reasons.

Sample coverage We successfully conducted a total of 122 In-depth Interviews with 54 adolescents enrolled on the T-Safe platform, including those who received services and those who were enrolled but did not receive services, 39 IDIS with service providers and 29 IDIs with mobilizers. The distribution per county included 51 IDI's in Nairobi City County (24 with adolescent girls, 17 with service providers and 10 with mobilisers), 15 IDI's in Nakuru County (2 with adolescent girls,8 with service providers and 5 with mobilisers), 34 IDI's in Homa Bay County (18 with adolescent girls,8 with service providers and 8 with mobilisers) and 22 IDI's in Kakamega County (10 with adolescent girls,6 with service providers and another 6 with mobilisers.)

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f]

Research instrument

The midline evaluation included qualitative in-depth interviews with adolescent T-Safe users, adolescents enrolled in the platform but did not use the services, providers and mobilizers to assess the adolescent user experience and quality of services as well as provider accountability under the T-Safe program. Generally,the aim of the qualitative study was to assess adolescents' T-Safe users experience across quality dimensions as well as provider's experiences and accountability. The dimensions assessed include adolescent's journey with the platforms, experience with the platform, perceptions of quality of services and how the ITH platforms changed provider behavior and accountability.

Adolescent in-depth interview included:Adolescent journey,Barriers to adolescents access to SRH services,Community attitudes towards adolescent use of contraceptives,Decision making,Factors influencing decision to visit a clinic,Motivating factors for girls to join ITH,Notable changes since the introduction of ITH,Parental support ,and Perceptions about T-Safe.

Service providers in-depth interview included;Personal and professional background,Provider's experience with ITH/T-safe platform,Notable changes/influences since the introduction of ITH/T-safe,Influence/Impact on the preference of adolescent service users and health care providers as a result of the program,Impact/influence of ITH on quality of care,Facilitators and barriers for adolescents to access SRH services,Mechanisms to address the barriers,Challenges related to the facility,Feedback about facility from adolescents,Types of support needed to improve SRH services provided to adolescents Scenarios of different clients accessing SRH services,and Free node.

Mobilisers in-depth interview included;Mobilizer responsibilities and designation,Job description,Motivation for joining ITH,Personal and professional background,Training,Mobilizer roles in ITH,Mobilization process ,Experience with ITH platform,Key messages shared with adolescent about ITH/ Tsafe during enrollment,Motivating factors for adolescents to join ITH/Tsafe,Community's attitude towards ITH/Tsafe,Challenges faced by mobilizers when mobilizing adolescents for Tsafe,Adolescents view regarding platform,Addressing the challenges ,andFree node

Cleaning operations

Qualitative interviews were audio-recorded and the audio recordings were transmitted to APHRC study team by uploading the audios to google drive which was only accessible to the team. Related interview notes, participant's description forms and Informed consent forms were transported to APHRC offices in Nairobi at the end of data collection where the data transcription and coding was conducted. Audio recordings from qualitative interviews were transcribed and saved in MS Word format. The transcripts were stored electronically in password protected computers and were only accessible to the evaluation team working on the project. A qualitative software analysis program (NVIVO) was used to assist in coding and analyzing the data. A “thematic analysis” approach was used to organize and analyze the data, and to assist in the development of a codebook and coding scheme. Data was analyzed by first reading the full IDI transcripts, becoming familiar with the data and noting the themes and concepts that emerged. A thematic framework was developed from the identified themes and sub-themes and this was then used to create codes and code the raw data.

Response rate

N/A

Sampling error estimates

N/A

Guided by common values, Covid Act Now is a multidisciplinary team of technologists, epidemiologists, health experts, and public policy leaders working to provide disease intelligence and data analysis on COVID in the U.S.

APIs, Visualizations and csv files of data are available for public use.

Data Analytics Outsourcing Market Size And Forecast

Data Analytics Outsourcing Market size was valued at USD 10.2 Billion in 2024 and is projected to reach USD 55.44 Billion by 2031, growing at a CAGR of 26% from 2024 to 2031.

Global Data Analytics Outsourcing Market Drivers

Growing Volume of Big Data: The increasing volume of big data is leading firms to outsource analytics. According to IDC, the global datasphere is expected to increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This tremendous rise in data volume has compelled firms to seek external expertise for efficient data management and analytics.

Cost-Effectiveness of Outsourcing: Outsourcing data analytics can be more cost-effective than having an in-house team. According to a Deloitte poll, 59% of organizations outsource primarily to save money. According to the same poll, 47% of organizations saved between 10 and 25% of their costs through outsourcing.

Shortage of Skilled Data Professionals: Due to a shortage of experienced data analytics workers, organizations are increasingly outsourcing. The U.S. Bureau of Labor Statistics predicts that employment of data scientists and mathematical scientific occupations will expand 31% between 2019 and 2029, substantially faster than the national average, indicating a significant skills gap.

To assess use of external evidence for overall survival (OS) estimation in oncology single-technology appraisals (STAs) by the National Institute for Health and Care Excellence (NICE). STAs for oncology drugs appraised by NICE between January 2021 and March 2023 were identified. For each eligible STA, OS extrapolation methods used, the rationale for using external data, the source and type of data, and information on acceptance by the evidence review group (ERG) and the appraisal committee were extracted. Initially, 215 STAs were identified, of which 82 were eligible for the study. Of these, 32 STAs used external data for OS extrapolation, including trial data (44%), real-world data (47%), clinical opinion (25%), meta-analysis (1%) and previous STA (1%). External data were used more frequently in state-transition models for post-event transitions and cure assumptions, and in partitioned-survival models to replace pivotal trial OS, inform long-term survival estimates or to estimate OS based on surrogacy analysis. Sensitivity analyses on use of external data was explored in 16 (50%) of the STAs. The committee accepted use of external data in half of the analysed STAs, acknowledging uncertainty in OS extrapolation. The analysis was limited to the STAs published between 2021 and 2023 and publicly available materials on the NICE website. This study provides an overview of external data used to estimate OS in oncology STAs conducted by NICE in recent years. External data, including trial data, real-world data and clinical opinions, were incorporated into recent oncology STAs at various modelling stages. ERGs and appraisal committees were generally accepting of the use of external data. However, it is crucial to conduct a sensitivity analysis and provide a justification for the methods and data source selection.

The ImmPort system serves as a long-term, sustainable archive of immunology research data generated by investigators mainly funded through the NIAID/DAIT. The core component of the ImmPort system is an extensive data warehouse containing an integration of experimental data and clinical trial data. The ImmPort system also provides data analysis tools and an immunology-focused ontology. The analytical tools created and integrated as part of the ImmPort system are available to any researcher within ImmPort after registration and approval by DAIT. Additionally, the data provided mainly by NIAID/DAIT funded researchers in ImmPort will be available to all registered users after the appropriate embargo time.

This file contains replication data and files for "External Validity: Framework, Design, and Analysis". Additionally, it contains the an online supplementary materials with analytical derivations, additional simulation results, supporting information for the literature review, and numerical results for all figures in the main manuscript an supplementary materials.

We conducted an analysis to confirm our observations that only a very small percentage of public research data is hosted in the Institutional Data Repositories, while the vast majority is published in the open domain-specific and generalist data repositories.

For this analysis, we selected 11 institutions, many of which have been our evaluation partners. For each institution, we counted the number of datasets published in their Institutional Data Repository (IDR) and tracked the number of public research datasets hosted in external data repositories via the Data Monitor API. External tracking was based on the corpus of 14+ mln data records checked against the institutional SciVal ID. One institution didn’t have an IDR.

We found out that 10 out of 11 institutions had most of their public research data hosted outside of their institution, where by research data we mean not only datasets, but a broader notion that includes, for example, software.

We will be happy to expand it by adding more institutions upon request.

Note: This is version 2 of the earlier published dataset. The number of datasets published and tracked in the Monash Institutional Data Repository has been updated based on the information provided by the Monash Library. The number of datasets in the NTU Institutional Data Repository now includes datasets only. Dataverses were excluded to avoid double counting.

Snapshot img

Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

What will be the Size of the Data Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics 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. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offeri

This dataset contains current job postings available on the City of New York’s official jobs site (http://www.nyc.gov/html/careers/html/search/search.shtml). Internal postings available to city employees and external postings available to the general public are included.

The global data analytics market in the insurance industry is projected to reach USD 21,180 million by 2033, exhibiting a CAGR of 7.3% from 2025 to 2033. The growing need for risk assessment, fraud detection, and enhanced customer experience drives market expansion. Insurance companies leverage data analytics to analyze vast amounts of data from various sources, including customer demographics, policy history, and external market trends. This analysis enables them to tailor risk profiles, optimize pricing premiums, and identify fraudulent claims effectively, leading to improved underwriting decisions and reduced operational costs. Moreover, data analytics helps insurers gain valuable insights into customer behavior, preferences, and risk appetite, allowing them to develop personalized products and enhance customer engagement. The market is segmented based on type (service and software) and application (pricing premiums, fraud prevention, waste reduction, and customer insights). Geographically, North America holds a dominant position, followed by Europe and Asia-Pacific. Key market players include Deloitte, Verisk Analytics, IBM, SAP AG, and LexisNexis. Strategic collaborations and partnerships among technology providers and insurance companies are expected to drive innovation and fuel growth in the data analytics market for insurance. The integration of advanced technologies like artificial intelligence (AI), machine learning (ML), and cloud computing will further enhance the accuracy and efficiency of data analysis, creating new growth opportunities in the market. Data analytics has revolutionized the insurance industry, empowering insurers to make data-driven decisions, optimize operations, and enhance customer experiences. This report provides a comprehensive overview of the data analytics market in insurance, covering key trends, market dynamics, and competitive landscapes.

The Data Analytics Outsourcing market is experiencing robust growth, projected to reach $8.11 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 34.33% from 2025 to 2033. This expansion is driven by several key factors. Businesses are increasingly relying on external expertise to manage the complexities of data analytics, particularly as the volume and variety of data continue to explode. The rising adoption of cloud computing and advanced analytics technologies, such as artificial intelligence (AI) and machine learning (ML), further fuels this demand. Cost optimization, access to specialized skills, and faster time-to-market are other compelling reasons for companies to outsource their data analytics needs. The market is segmented across various analytics types (CRM, supply chain, risk, financial, and others) and end-user industries (retail, automotive, manufacturing, BFSI, IT & Telecom, Oil & Gas, and others). The competitive landscape is populated by a mix of large multinational corporations and specialized boutique firms, creating diverse service offerings catering to the needs of different organizations. Growth within specific segments will vary; for example, the financial analytics segment is expected to experience particularly high growth due to increasing regulatory requirements and the need for sophisticated risk management. The Retail and BFSI sectors will likely be major drivers of demand due to their substantial data volumes and the potential for improved customer experience and operational efficiency through data-driven decision-making. While the market faces challenges like data security concerns and the need to integrate outsourced solutions seamlessly with existing internal systems, the overall trajectory remains strongly positive, promising significant expansion over the forecast period. The geographical distribution of the market is expected to show robust growth across all regions, although North America and Europe will likely maintain a larger share due to their advanced digital infrastructure and high adoption rates of data analytics technologies. Recent developments include: February 2024 - Wipro and IBM Expanded Partnership to Offer New AI Services and Support to Clients. Wipro launched an Enterprise AI-Ready Platform, leveraging IBM Watsonx, to advance enterprise adoption of Generative AI. As part of the expanded partnership, IBM and Wipro will establish a centralized tech hub to support joint clients in their AI pursuits. As part of this expanded partnership, Wipro associates will be trained in IBM hybrid cloud, AI, and data analytics technologies to help accelerate the development of joint solutions., September 2023 - IBM announced plans for new generative AI foundation models and enhancements coming to watsonx. These enhancements include a technical preview for watsonx. Governance, new fertile AI data services coming to watsonx. Data and the planned integration of watsonx.ai foundation models across select software and infrastructure products.. Key drivers for this market are: Increasing Volume and Variety of Data being Generated are the Major Driving Factors for this Industry, Increasing Adoption of Data Analytics Outsourcing in BFSI. Potential restraints include: Increasing Volume and Variety of Data being Generated are the Major Driving Factors for this Industry, Increasing Adoption of Data Analytics Outsourcing in BFSI. Notable trends are: Increasing Adoption of Data Analytics Outsourcing in BFSI is Driving the Market.

The materials of this dataset are used in: - N. Arınık, V. Labatut and R. Figueiredo, "Characterizing measures for the assessment of cluster analysis and community detection", Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques (MARAMI), 2020, url: https://hal.archives-ouvertes.fr/hal-02993542/- N. Arınık, V. Labatut and R. Figueiredo, "Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection", in IEEE Access, vol. 9, pp. 20255-20276, 2021, doi:https://doi.org/10.1109/ACCESS.2021.3054621.This dataset contains:* figs.zip which contains the plot files* data&results.zip which contains the necessary data to perform our analysis, as well as result files

Analysis of ‘External Monitoring Visits with Findings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e0ea4ff3-332e-4fde-aa77-d2c78f48384b on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Count of all audits and monitoring visits with findings during reporting period.

--- Original source retains full ownership of the source dataset ---

The most common type of data on which construction companies in the European Union performed data analytics in 2023 was ******************* data. Overall, over ** percent of construction companies in the EU had data analytics performed by their own employees or by an external provider.

Cuckoo Sandbox is the leading open sourceautomated malware analysis system. You can throw any suspicious file atit and in a matter of seconds Cuckoo will provide you back some detailedresults outlining what such file did when executed inside an isolatedenvironment.

Cuckoo Sandbox is free software that automated the task of analyzing any malicious file under Windows, OS X, Linux, and Android.

What can it do?

Cuckoo Sandbox is an advanced, extremely modular, and 100% open source automated malware analysis system with infinite application opportunities. By default it is able to:


Analyze many different malicious files (executables, office documents, pdf files, emails, etc) as well as malicious websites under Windows, Linux, Mac OS X, and Android virtualized environments.
Trace API calls and general behavior of the file and distill this into high level information and signatures comprehensible by anyone.
Dump and analyze network traffic, even when encrypted with SSL/TLS. With native network routing support to drop all traffic or route it through InetSIM, a network interface, or a VPN.
Perform advanced memory analysis of the infected virtualized system through Volatility as well as on a process memory granularity using YARA.


Due to Cuckoo s open source nature and extensive modular design one may customize any aspect of the analysis environment, analysis results processing, and reporting stage. Cuckoo provides you all the requirements to easily integrate the sandbox into your existing framework and backend in the way you want, with the format you want, and all of that without licensing requirements.

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Big Data Software As A Service Market Outlook

The global market size for Big Data Software as a Service (BDaaS) was valued at USD 15.7 billion in 2023 and is expected to reach USD 54.8 billion by 2032, growing at a remarkable compound annual growth rate (CAGR) of 14.8% during the forecast period. The surge in demand for real-time data analytics and the need for high-speed data processing are among the key growth factors propelling this market forward. Organizations of all sizes are increasingly recognizing the value of data-driven decision-making, further driving the adoption of BDaaS solutions.

One of the primary growth factors for the BDaaS market is the exponential increase in data generation across various sectors. With the proliferation of Internet of Things (IoT) devices, social media platforms, and digital transactions, organizations are drowning in data. The ability to process and analyze this data in real-time has become a critical business need. BDaaS solutions offer the scalability and flexibility needed to handle vast amounts of structured and unstructured data, making them indispensable for organizations aiming to gain actionable insights from their data.

Another significant factor contributing to the market's growth is the rising adoption of cloud computing. Cloud-based BDaaS solutions eliminate the need for significant upfront investments in hardware and software, making them accessible to small and medium enterprises (SMEs) as well as large enterprises. The pay-as-you-go model offered by cloud providers ensures that organizations can scale their data analytics capabilities according to their needs, further driving the adoption of BDaaS. Additionally, advancements in cloud technology, such as hybrid and multi-cloud environments, are providing organizations with more options to optimize their data analytics processes.

The increasing focus on regulatory compliance and data security is also driving the BDaaS market. Organizations are under immense pressure to adhere to stringent data protection regulations, such as GDPR in Europe and CCPA in California. BDaaS providers offer robust security features, including data encryption, access controls, and compliance management, which help organizations meet regulatory requirements. The enhanced security measures provided by BDaaS solutions are particularly attractive to industries dealing with sensitive information, such as healthcare and finance.

In this rapidly evolving landscape, the concept of Big Data Exchange is gaining traction as organizations seek to streamline their data management processes. Big Data Exchange refers to the platforms and systems that facilitate the sharing and trading of large datasets between entities. This concept is becoming increasingly important as businesses look to leverage external data sources to enhance their analytics capabilities. By participating in Big Data Exchange, organizations can access a wider array of data, which can lead to more comprehensive insights and informed decision-making. This exchange of data not only helps in breaking down silos within organizations but also fosters collaboration and innovation across industries. As the demand for diverse and high-quality data continues to grow, Big Data Exchange platforms are expected to play a crucial role in the BDaaS ecosystem.

From a regional perspective, North America is expected to dominate the BDaaS market during the forecast period, owing to the early adoption of advanced technologies and the presence of major market players in the region. However, the Asia Pacific region is anticipated to witness the highest growth rate, driven by the rapid digital transformation initiatives and increasing investments in data analytics infrastructure. Europe is also expected to experience significant growth, supported by stringent data protection regulations and the growing adoption of cloud-based solutions across various industry verticals.

Component Analysis

The BDaaS market is segmented into two primary components: software and services. Software solutions include tools for data storage, processing, and analysis, while services encompass consulting, implementation, and support services. The software segment is expected to hold the largest market share, driven by the increasing demand for advanced analytics tools and platforms. Organizations are investing heavily in software solutions that offer real-time data processing, predictive analytics, and data visualization capabilities. These tools enable busi

Analysis of ‘ICA03 - External Connection to the Internet as a Percentage of All Enterprises’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/35bcaead-1c12-449e-a946-8d88b79721d5 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

External Connection to the Internet as a Percentage of All Enterprises

--- Original source retains full ownership of the source dataset ---

In 2023, around **** percent of companies that used data analysis and related services in South Korea did so via external services. Around ** percent of companies utilized services both from internal and external sources.