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Sources: Cubus Official COS Boozt BIK BOK AS Royal Design Group Holding AB Bagaren och Kocken AB Rum21 Svenskt Tenn Kökets favoriter lannamobler.se KWA Garden furniture Confident Living Stalands Möbler Trendrum AB Svenssons Nordiska Galleriet Jotex Jollyroom Monki New Bubbleroom Sweden AB Wegot KitchenTime AB Lindex NA-KD.com Olsson & Gerthel Nordic Nest Bonprix Nederland Vero Moda Care of Carl Cervera Zoovillage ARKET Kappahl DesignTorget Mio AB Afound

This is the raw data for our manuscript on analysis of data sources found in systematic reviews in 2019.

Market Analysis of Internet Financial Data Terminal Services The global market for Internet financial data terminal services is projected to reach a valuation of XXX million by 2033, expanding at a CAGR of XX%. The surge in demand for real-time financial data, the proliferation of online trading platforms, and the growing adoption of cloud-based solutions drive market growth. The segment of institutional investors holds a dominant market share due to their need for comprehensive data for investment decision-making. Mobile versions of financial data terminals are gaining traction, providing investors with access to market information on the go. Key trends shaping the market include the integration of artificial intelligence (AI) for data analysis and visualization, the increasing adoption of open-source platforms, and the growing focus on data security. Major players in the market include Bloomberg, Refinitiv, FactSet, S&P, and Moody's Analytics. The Asia-Pacific region is expected to experience the fastest growth due to the rapid expansion of the financial industry in emerging economies like China and India. However, stringent data privacy regulations and competition from free data sources pose challenges to market players.

Doorda's UK Vulnerability Data provides a comprehensive database of over 1.8M postcodes sourced from 30 data sources, offering unparalleled insights for location intelligence and analytics purposes.

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This paper presents a large-scale document-level comparison of two major bibliographic data sources: Scopus and Dimensions. The focus is on the differences in their coverage of documents at two levels of aggregation: by country and by institution. The main goal is to analyze whether Dimensions offers as good new opportunities for bibliometric analysis at the country and institutional levels as it does at the global level. Differences in the completeness and accuracy of citation links are also studied. The results allow a profile of Dimensions to be drawn in terms of its coverage by country and institution. Dimensions’ coverage is more than 25% greater than Scopus which is consistent with previous studies. However, the main finding of this study is the lack of affiliation data in a large fraction of Dimensions documents. We found that close to half of all documents in Dimensions are not associated with any country of affiliation while the proportion of documents without this data in Scopus is much lower. This situation mainly affects the possibilities that Dimensions can offer as instruments for carrying out bibliometric analyses at the country and institutional level. Both of these aspects are highly pragmatic considerations for information retrieval and the design of policies for the use of scientific databases in research evaluation.

Context

This is a sample dataset that contains the reference information among various information sources such as TV, Newspaper and online articles.

Content

There are two datasets 1. InputFileEdges.csv contains the information about the edges between nodes. The fields in this dataset are as follows: (i) from: source (or) starting node id of the edge (ii) to: target (or) ending node id of the edge (iii) weight: the number of times they were connected (or) referenced each other (iv) type: the type of the link (hyperlink or mention) between these nodes

1. InputFileNodes.csv contains the information about the nodes. The fields in this dataset are as follows: (i) id: unique node id (ii) media: the media information of the node (e.g. NY Times, Washington Post, Wall Street Journal etc.) (iii) media.type: the type of the media (1 represents Newspaper; 2 represents TV; and 3 represents Online) (iv) type.label: the type of the media (Newspaper, TV, and Online) (v) audience.size: the audience size for each media

The Alternative Data Market size was valued at USD 7.20 billion in 2023 and is projected to reach USD 126.50 billion by 2032, exhibiting a CAGR of 50.6 % during the forecasts period. The use and processing of information that is not in financial databases is known as the alternative data market. Such data involves posts in social networks, satellite images, credit card transactions, web traffic and many others. It is mostly used in financial field to make the investment decisions, managing risks and analyzing competitors, giving a more general view on market trends as well as consumers’ attitude. It has been found that there is increasing requirement for the obtaining of data from unconventional sources as firms strive to nose ahead in highly competitive markets. Some current trend are the finding of AI and machine learning to drive large sets of data and the broadening utilization of the so called “Alternative Data” across industries that are not only the finance industry. Recent developments include: In April 2023, Thinknum Alternative Data launched new data fields to its employee sentiment datasets for people analytics teams and investors to use this as an 'employee NPS' proxy, and support highly-rated employers set up interviews through employee referrals. , In September 2022, Thinknum Alternative Data announced its plan to combine data Similarweb, SensorTower, Thinknum, Caplight, and Pathmatics with Lagoon, a sophisticated infrastructure platform to deliver an alternative data source for investment research, due diligence, deal sourcing and origination, and post-acquisition strategies in private markets. , In May 2022, M Science LLC launched a consumer spending trends platform, providing daily, weekly, monthly, and semi-annual visibility into consumer behaviors and competitive benchmarking. The consumer spending platform provided real-time insights into consumer spending patterns for Australian brands and an unparalleled business performance analysis. .

Checklist/listing for socio-demographic, tourism, transport and entrepreneurial open data sets used in the VertiNet project for the needs analysis of Vertiports - use for other mobility-related issues also possible

Use of the data sources included in the checklist in accordance with "Guide to the determination of vertiport needs within administrative areas" possible

Snapshot img

Cloud Analytics Market Size 2024-2028

The cloud analytics market size is forecast to increase by USD 74.08 billion at a CAGR of 24.4% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The adoption of hybrid and multi-cloud setups is on the rise, as these configurations enhance data connectivity and flexibility. Another trend driving market growth is the increasing use of cloud security applications to safeguard sensitive data.
However, concerns regarding confidential data security and privacy remain a challenge for market growth. Organizations must ensure robust security measures are in place to mitigate risks and maintain trust with their customers. Overall, the market is poised for continued expansion as businesses seek to leverage the benefits of cloud technologies for data processing and data analytics.

What will be the Size of the Cloud Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing volume of data generated by businesses and the demand for advanced analytics solutions. Cloud-based analytics enables organizations to process and analyze large datasets from various data sources, including unstructured data, in real-time. This is crucial for businesses looking to make data-driven decisions and gain valuable insights to optimize their operations and meet customer requirements. Key industries such as sales and marketing, customer service, and finance are adopting cloud analytics to improve key performance indicators and gain a competitive edge. Both Small and Medium-sized Enterprises (SMEs) and large enterprises are embracing cloud analytics, with solutions available on private, public, and multi-cloud platforms.
Big data technology, such as machine learning and artificial intelligence, are integral to cloud analytics, enabling advanced data analytics and business intelligence. Cloud analytics provides businesses with the flexibility to store and process data In the cloud, reducing the need for expensive on-premises data storage and computation. Hybrid environments are also gaining popularity, allowing businesses to leverage the benefits of both private and public clouds. Overall, the market is poised for continued growth as businesses increasingly rely on data-driven insights to inform their decision-making processes.

How is this Cloud Analytics Industry segmented and which is the largest segment?

The cloud analytics 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 2017-2022 for the following segments.

Solution

  Hosted data warehouse solutions
  Cloud BI tools
  Complex event processing
  Others


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Solution Insights

The hosted data warehouse solutions segment is estimated to witness significant growth during the forecast period.

Hosted data warehouses enable organizations to centralize and analyze large datasets from multiple sources, facilitating advanced analytics solutions and real-time insights. By utilizing cloud-based infrastructure, businesses can reduce operational costs through eliminating licensing expenses, hardware investments, and maintenance fees. Additionally, cloud solutions offer network security measures, such as Software Defined Networking and Network integration, ensuring data protection. Cloud analytics caters to diverse industries, including SMEs and large enterprises, addressing requirements for sales and marketing, customer service, and key performance indicators. Advanced analytics capabilities, including predictive analytics, automated decision making, and fraud prevention, are essential for data-driven decision making and business optimization.

Furthermore, cloud platforms provide access to specialized talent, big data technology, and AI, enhancing customer experiences and digital business opportunities. Data connectivity and data processing in real-time are crucial for network agility and application performance. Hosted data warehouses offer computational power and storage capabilities, ensuring efficient data utilization and enterprise information management. Cloud service providers offer various cloud environments, including private, public, multi-cloud, and hybrid, catering to diverse business needs. Compliance and security concerns are addressed through cybersecurity frameworks and data security measures, ensuring data breaches and thefts are minimized.

Get a glance at the Cloud Analytics Industry report of share of various segments Request Free Sample

The Hosted data warehouse soluti

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

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

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

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

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

Dataset Details:

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

Analysis Highlights:

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

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

Why This Matters:

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

Acknowledgments:

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

Please Note:

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

The Department of Homeland Security (DHS) Intelligence and Analysis (I&A) Open Source Enterprise (OSE) is an intelligence collection program within DHS I&A that collects information, through publicly available sources, and reports that information in support of Departmental missions. The collection and reporting by the DHS I&A OSE addresses statutory and other federal legal requirements for the Undersecretary of Intelligence and Analysis relating to the collection and usage of publicly available information. In the execution of this intelligence mission, Personally Identifiable Information (PII) may be collected and reported.

The Alternative Data Vendor market is experiencing robust growth, driven by the increasing demand for non-traditional data sources to enhance investment strategies and business decision-making. The market's expansion is fueled by the proliferation of digital data, advancements in data analytics, and a growing need for more comprehensive and nuanced insights across various sectors. The BFSI (Banking, Financial Services, and Insurance) sector remains a significant driver, leveraging alternative data for credit scoring, fraud detection, and risk management. However, growth is also witnessed in industrial, IT and telecommunications, and retail and logistics sectors as businesses seek competitive advantages through data-driven decision-making. The diverse types of alternative data, including credit card transactions, web data, sentiment analysis, and public data, cater to a wide range of applications. While data privacy and regulatory concerns pose challenges, the market is overcoming these hurdles through robust data anonymization and compliance strategies. The competitive landscape features both established players like S&P Global and Bloomberg, along with emerging technology-driven companies, fostering innovation and market expansion. We project a steady compound annual growth rate (CAGR) resulting in a substantial market expansion over the next decade. This growth is expected to be distributed across regions, with North America and Europe maintaining leading positions due to early adoption and developed data infrastructure. The forecast period from 2025 to 2033 anticipates continued market expansion, propelled by factors such as increasing data availability from IoT devices, refined analytical techniques, and expanding applications across new sectors. The market's segmentation by application and data type is expected to further evolve, with niche players focusing on specific data sets and industries. This specialized approach allows for deeper insights and catering to specific client needs. Geographic expansion will continue, with growth in Asia-Pacific particularly driven by the increasing adoption of digital technologies and expanding economic activity. Strategic partnerships and mergers and acquisitions will likely shape the competitive landscape, fostering consolidation and further innovation in alternative data solutions. Despite challenges related to data quality, security, and ethical considerations, the overall outlook for the Alternative Data Vendor market remains highly positive, with substantial growth opportunities over the long term.

The data discovery market is experiencing robust growth, fueled by the increasing volume and complexity of data generated across various industries. The market, currently valued in the billions (a precise figure cannot be provided without the missing "XX" market size value, but a reasonable estimate based on similar market reports and a 21% CAGR would place it in the several billion-dollar range in 2025), is projected to maintain a Compound Annual Growth Rate (CAGR) of 21% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the digital transformation initiatives across enterprises are leading to a surge in data generation, creating a critical need for efficient data exploration and analysis tools. Secondly, the rise of big data analytics and the growing demand for data-driven decision-making across sectors, including BFSI, telecom, retail, and manufacturing, are significantly bolstering market demand. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of data discovery platforms, making them more user-friendly and effective in extracting valuable insights. The market is segmented by component (software and services), enterprise size (SMEs and large enterprises), and industry vertical, with BFSI, telecommunications, and retail showing strong adoption. However, despite the impressive growth trajectory, certain restraints exist. The high initial investment costs associated with implementing data discovery solutions can pose a challenge for smaller organizations. Additionally, the complexity of data integration and the need for skilled professionals to manage and interpret the results can hinder wider adoption. Nonetheless, the ongoing technological advancements and the increasing awareness of the strategic value of data are expected to mitigate these limitations, driving further market penetration. The competitive landscape includes both established players like SAS Institute and Salesforce (via Tableau), and emerging innovative companies, signifying a dynamic and evolving market with ample opportunities for growth and innovation. The geographical distribution of the market is likely to be skewed towards mature markets like North America and Europe initially, with Asia Pacific exhibiting strong growth potential in the coming years. Recent developments include: August 2022: CoreLogic, a major global provider of analytics-driven and property data solutions, expanded its partnership with Google Cloud to assist in the introduction of its novel CoreLogic Discovery Platform. Discovery Platform, which is fully built on Google Cloud's safe and sustainable technology, offers a complete asset analytics platform and cloud-based data interchange for enterprises in a variety of industries., June 2022: Select Star established an official collaboration with dbt Labs. Dbt has been one of Select Star's most significant integrations, with over 15,000 models and 225,000 columns linked up to date. Select Star is intended to facilitate the data discovery required by companies in order to harness the potential of their data and generate effective outcomes. As a result, Select Star and Dbt Labs have a shared goal, to empower analytics engineers to convert information better and keep appropriate documentation so that business users and data analysts can trust their data., June 2022: TD SYNNEX's SNX Tech Data established a collaboration with Instructure INST, a Learning Management Systems ("LMS") company, to utilize advanced learning capabilities in India. TD SYNNEX earned a substantial advantage with this deal, in addition to developing its data, Internet of Things, and analytics products. By enabling end-to-end business analytics powered by self-service data discovery, corporate reporting, mobile apps, and embedded analytics, TD SYNNEX's partners were able to offer complete business analytics propelled by data-driven business culture.. Key drivers for this market are: Increasing Number of Multi-Structured Data Sources, Growing Importance for Data-Driven Decision-Making. Potential restraints include: Data Security and Privacy Concerns. Notable trends are: The Banking, Financial Services, and Insurance Sector Holds a Dominant Position.

Finance Data Fusion Market Outlook

The global finance data fusion market size is projected to grow at a CAGR of 12.5% from 2024 to 2032, with the market value increasing from USD 2.5 billion in 2023 to an estimated USD 7.4 billion by 2032. This impressive growth is driven by an intensifying demand for real-time analytics, the increasing complexity of financial transactions, and the need for improved risk management and fraud detection mechanisms in the financial sector.

One of the primary growth factors propelling the finance data fusion market is the rising necessity for robust risk management solutions. Financial institutions are increasingly recognizing the importance of integrating diverse data sources to gain comprehensive insights into potential risks. With the advent of big data and advanced analytics, data fusion technologies enable organizations to synthesize information from multiple datasets, including market data, transactional data, and social media feeds, thereby enhancing their ability to predict and manage risks in a dynamic market environment. This capability is particularly critical in an era where financial stability and regulatory compliance are paramount.

Another significant driver of market growth is the surging demand for enhanced fraud detection systems. Financial fraud has become increasingly sophisticated, necessitating the adoption of advanced technologies that can detect and mitigate fraudulent activities in real-time. Data fusion solutions allow for the integration of diverse data points, providing a holistic view of customer behavior and transaction patterns. This multi-dimensional analysis significantly improves the accuracy of fraud detection systems, enabling financial institutions to safeguard their assets and maintain customer trust. The growing reliance on digital payment systems further underscores the need for advanced fraud detection technologies.

Furthermore, the growing importance of customer analytics in the financial sector is contributing to the market's expansion. Financial institutions are leveraging data fusion technologies to gain deeper insights into customer preferences, behavior, and needs. By integrating data from various sources, such as transaction histories, social media interactions, and demographic information, organizations can create detailed customer profiles that drive personalized marketing strategies and improve customer satisfaction. The ability to deliver tailored financial products and services based on comprehensive data analysis is a key competitive advantage in the financial industry.

Regionally, North America is expected to dominate the finance data fusion market, owing to its advanced financial infrastructure and the early adoption of innovative technologies. The presence of major financial institutions and a highly developed regulatory framework further supports market growth in this region. Europe and Asia Pacific are also anticipated to witness substantial growth, driven by increasing investments in financial technology and the rising demand for advanced data analytics solutions. In contrast, Latin America and the Middle East & Africa are projected to experience moderate growth, influenced by varying levels of technological adoption and economic development.

Component Analysis

The finance data fusion market can be segmented by component into software, hardware, and services. The software segment is expected to hold the largest market share, driven by the increasing adoption of advanced analytic tools and platforms that enable the integration and analysis of diverse data sources. Financial institutions are investing heavily in software solutions that provide real-time insights and predictive analytics, facilitating more informed decision-making and enhancing operational efficiency. The proliferation of cloud-based software solutions is also contributing to the segment's growth, as they offer scalable and cost-effective alternatives to traditional on-premises systems.

The hardware segment, although smaller in comparison to software, plays a crucial role in supporting data fusion activities. High-performance computing systems, storage solutions, and network infrastructure are essential for managing and processing the vast amounts of data generated in the financial sector. As financial institutions continue to expand their data capabilities, the demand for robust and scalable hardware solutions is expected to rise. Innovations in hardware technology, such as advanced processors and high-speed storage devices, are further driving the segment's growth.

A description of procedures and sources of data used in the meta-analysis.

The Alternative Data Provider market, currently valued at $1.252 billion (2025), is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 9% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing demand for more granular and timely insights across various sectors – BFSI (Banking, Financial Services, and Insurance), industrial, IT and telecommunications, retail and logistics – fuels the adoption of alternative data sources beyond traditional financial data. Secondly, the sophistication of analytical techniques and AI/ML-powered solutions allows for more effective processing and interpretation of diverse data types, including credit card transactions, web data, sentiment analysis, and public records. This enables businesses to make more informed, data-driven decisions. Finally, the emergence of specialized providers catering to niche needs within these sectors has created a competitive yet innovative marketplace. While regulatory hurdles and data privacy concerns pose challenges, the overall market trajectory remains positive, indicating strong potential for future growth and investment. The market segmentation reveals a diverse landscape. Application-wise, BFSI currently holds a significant share due to the sector's reliance on real-time insights for risk management and investment strategies. However, the IT and telecommunications and Retail and Logistics sectors are exhibiting strong growth potential, driving demand for alternative data solutions to improve operational efficiency and customer understanding. Regarding data types, credit card transactions and web data are currently dominant, but sentiment and public data are gaining traction due to their ability to provide nuanced understanding of market trends and consumer behavior. Leading companies such as Preqin, Dataminr, and others are constantly innovating their offerings, focusing on the development of advanced analytics and data integration capabilities to capture a larger market share in this dynamic space. Geographical expansion, particularly in the Asia-Pacific region driven by increasing digital adoption and economic growth, presents significant opportunities for future market expansion.

The Alternative Data Market offers a diverse range of products and services, broadly categorized as: Data Type: This categorization focuses on the source of the data, offering granular options to meet specific analytical needs. Options include, but are not limited to:

 Credit & Debit Card Transactions
 Email Receipts
 Geo-location (Foot Traffic)
 Social Media Data
 Mobile App Usage Data
 Web Scraping Data

Industry: This segmentation tailors data offerings to specific industry requirements and analytical frameworks. Examples include:

 Automotive
 BFSI
 Energy
 Industrial
 IT & Telecommunications
 Media & Entertainment
 Real Estate & Construction
 Retail
 Transportation & Logistics
 Healthcare
 Other Industries

Recent developments include: June 2021 The Quandl Data Platform, a brand-new alternative data platform, was released by Quandl Inc. Users of the platform have access to a wide range of diverse data sources, including data from social media, satellite pictures, and site traffic statistics. There are also tools for data scrubbing, processing, and visualization., October 2021 In order to give users with a variety of alternative data sources, 1010data established a new platform called 1010data AltData. This platform offers consumer purchase information, geolocation data, and credit card transaction data. Additionally, the platform provides data processing and visualization features.. Notable trends are: Increasing demand from hedge funds is driving the market growth.

The unstructured data management market is experiencing robust growth, driven by the exponential increase in unstructured data generated across various industries. The market's expansion is fueled by the rising adoption of cloud-based solutions, increasing demand for advanced analytics capabilities, and the growing need for efficient data governance and compliance. Organizations are increasingly recognizing the value of extracting insights from unstructured data sources like text, images, audio, and video, leading to significant investments in solutions that enable storage, processing, and analysis. The competitive landscape is marked by a mix of established players like NetApp, Microsoft, and IBM, alongside specialized vendors offering niche solutions. While the initial investment in infrastructure and expertise can be a barrier to entry for some organizations, the long-term benefits of improved decision-making, enhanced operational efficiency, and competitive advantage outweigh these initial costs. We project a consistent compound annual growth rate (CAGR) resulting in substantial market expansion over the forecast period. Specific industry trends, such as the growing adoption of AI and machine learning for unstructured data processing, further accelerate market growth. The development of sophisticated data governance frameworks and regulations focusing on data privacy and security also contribute to the market's expansion. However, challenges remain, including data complexity, integration with existing systems, and the need for skilled professionals to manage and analyze unstructured data. Despite these challenges, the market's overall trajectory remains positive, with continued innovation and technological advancements paving the way for further growth and the emergence of new solutions to address the ever-increasing volume and complexity of unstructured data. We anticipate that the market will continue to see consolidation, with larger players acquiring smaller, specialized companies to expand their offerings and enhance their market position.