Account Aggregators Market Outlook

The global account aggregators market size is projected to grow from USD 1.8 billion in 2023 to USD 6.4 billion by 2032, driven by a robust CAGR of 15.4%. The growing need for data-driven decision-making and efficient financial management systems are key factors propelling this market's growth. Organizations across various sectors are increasingly adopting account aggregation solutions to streamline access to financial data, thereby enhancing their ability to make informed business decisions.

One of the primary factors driving the growth of the account aggregators market is the increasing digitalization of financial services. As more consumers and businesses transition to online banking and digital financial solutions, the need for secure and efficient data aggregation becomes paramount. Account aggregators facilitate this by enabling seamless access to financial data from multiple sources, improving transparency and financial management. In addition, the rising demand for personalized financial services is prompting financial institutions to leverage account aggregation to gain deeper insights into user behavior and preferences.

Regulatory frameworks and government initiatives also play a significant role in the market's expansion. Various governments and regulatory bodies are mandating the adoption of open banking and data sharing protocols, which necessitate the use of account aggregation services. For instance, the European Union's PSD2 directive and India's Account Aggregator framework are designed to promote data portability and interoperability, thereby fostering a competitive and innovative financial ecosystem. These regulations not only ensure consumer data protection but also encourage the development of new financial products and services.

Technological advancements such as artificial intelligence (AI) and machine learning (ML) are further enhancing the capabilities of account aggregators. These technologies enable more accurate data analysis and predictive analytics, allowing businesses to forecast trends and make proactive decisions. Additionally, the integration of blockchain technology is expected to enhance data security and transparency, addressing concerns related to data breaches and fraud. As these technologies continue to evolve, they are likely to drive increased adoption of account aggregation solutions across various sectors.

From a regional perspective, North America is expected to dominate the account aggregators market, followed by Europe and Asia Pacific. The early adoption of advanced financial technologies and a highly developed financial infrastructure contribute to North America's leading market position. Europe is also witnessing significant growth due to stringent regulatory requirements and a strong emphasis on open banking initiatives. Meanwhile, Asia Pacific is emerging as a lucrative market, driven by rapid economic development, increasing internet penetration, and supportive government policies aimed at digital financial inclusion.

Component Analysis

The account aggregators market is segmented by components into software and services. The software segment is expected to hold a significant share of the market owing to the increasing adoption of advanced financial management solutions. Account aggregation software enables seamless integration and access to financial data from multiple accounts, providing users with a comprehensive view of their financial status. This segment is witnessing continuous innovation, with companies developing user-friendly interfaces and advanced analytics capabilities to meet the growing demand for personalized financial services.

Services, on the other hand, encompass a range of offerings including consulting, integration, and maintenance services. As organizations adopt account aggregation software, the need for expert consulting and integration services becomes crucial to ensure smooth implementation and operation. Maintenance services are also essential to address any technical issues and ensure the software's optimal performance. The growing demand for these services is driving significant revenue growth in this segment, as businesses seek to maximize the benefits of their account aggregation solutions.

Within the software segment, there is a growing trend towards cloud-based solutions. Cloud-based account aggregation software offers several advantages, including scalability, flexibility, and cost-effectiveness. These solutions enable businesses to access financial data from anywhere, at a

Aggregated Foot Traffic Data is derived from Unacast's proprietary machine learning model. Unlike typical aggregated products that rely solely on aggregating the underlying GPS device-level supply, our machine learning model is more robust and less dependent on GPS data fluctuations because it is based on a magnitude of data sources.

Aggregated Foot Traffic Data is designed to enable users to analyze foot traffic trends to places of commercial interest. Unacast offers Aggregated Foot Traffic Data to millions of points of interest (POIs), Census Block Groups (CBGs), and custom locations within the United States.

Companies use Unacast Aggregated Foot Traffic Data for: - Site performance - Site selection - Market analysis - Competitor analysis - Business intelligence - Advertising and marketing - Benchmarking - Operational and staffing strategies

Aggregated Foot Traffic Data is best used along with Unacast’s Aggregated Trade Areas Data and Aggregated Demographic Data. Together, these datasets provide a comprehensive view of visitor profiles, activity, and traveler origin. These machine learning datasets are built with a privacy-first mindset to give you peace of mind as you solve your biggest business problems.

This record provides an overview of the scope and research output of NESP Marine Biodiversity Hub Project B3 - "Enhancing access to relevant marine information –developing a service for searching, …Show full descriptionThis record provides an overview of the scope and research output of NESP Marine Biodiversity Hub Project B3 - "Enhancing access to relevant marine information –developing a service for searching, aggregating and filtering collections of linked open marine data". For specific data outputs from this project, please see child records associated with this metadata. This project aims to improve the searchability and delivery of sources of linked open data, and to provide the ability to forward collections of discovered data to web services for subsequent processing through the development of a linked open data search tool. This work will improve access to existing data collections, and facilitate the development of new applications by acting as an aggregator of links to streams of marine data. The work will benefit managers (i.e. Department of the Environment staff) by providing fast and simple access to a wide range of marine information products, and offering a means of quickly synthesizing and aggregating multiple sources of information. Planned Outputs • Delivery of open source code to perform the search functions described above. • A simple initial web interface for performing the search and retrieval of results. • Expanded collections of data holdings available in linked open format, including the use of semantic mark-up to enable fully-automated data aggregation and web services. In particular, addition of linked-open data capability to a pilot collection of existing data sets (GA, CERF and NERP data sets).

Carrier Aggregation Solutions Market Outlook

The Carrier Aggregation Solutions Market size is projected to witness substantial growth, with an estimated market value of USD 8.5 billion in 2023, expected to reach USD 18.4 billion by 2032, growing at a CAGR of 9.1% from 2024 to 2032. This growth is primarily driven by the rising demand for high-speed internet and enhanced network capabilities as mobile communication continues to evolve. The technological advancements in mobile network infrastructure have facilitated the adoption of carrier aggregation solutions, which enable more efficient spectrum utilization and improved data speeds, meeting the increasing requirements of both consumers and businesses for seamless connectivity.

One of the core growth factors for the carrier aggregation solutions market is the unprecedented surge in mobile data traffic. With the proliferation of smart devices and the advent of technologies such as IoT and AI, there is a significant increase in the volume of data being transmitted across networks. Carrier aggregation, by allowing the combination of multiple frequency bands, addresses this demand by providing higher bandwidth and faster data speeds. This is particularly crucial as consumers and enterprises alike rely heavily on mobile applications and services that require robust and reliable connectivity. Furthermore, the steady rollout of 5G technology is expected to further enhance this demand, as it brings with it the promise of significantly faster speeds and lower latency.

Another significant driver is the competitive landscape among telecommunications providers. In a bid to improve customer satisfaction and retention, telecom operators are increasingly adopting carrier aggregation solutions to deliver superior network performance. Enhanced data speeds and improved coverage are becoming key differentiators in the highly saturated telecommunications industry. As a result, operators are investing heavily in upgrading their network infrastructure, which includes implementing carrier aggregation technologies. This trend is not only prominent in developed regions but is also gaining traction in developing economies where telecom companies are eager to offer next-generation services to an expanding user base.

From an industrial perspective, the integration of carrier aggregation solutions is becoming more prevalent across various sectors such as automotive, healthcare, and IT networking. In the automotive sector, for instance, the reliance on reliable high-speed networks is critical for the deployment of connected car technologies and autonomous vehicles. Similarly, in healthcare, the growth of telemedicine and other digital health services necessitates robust network support. Carrier aggregation provides the necessary bandwidth and network reliability required for these applications, thereby driving its adoption across these diverse sectors.

Regionally, the Asia Pacific is expected to dominate the market, driven by the large population base, increasing smartphone penetration, and rapid adoption of advanced technologies. North America and Europe are also significant markets due to the early uptake of new telecom technologies and strong investment in network infrastructure. However, growth in the Middle East & Africa and Latin America is projected to accelerate as these regions continue to develop their telecommunications infrastructure and strive for better connectivity solutions.

Component Analysis

Carrier Aggregation Solutions are comprised of three primary components: hardware, software, and services. The hardware segment is integral as it includes the physical equipment required for network infrastructure, such as base stations and antennas, which are essential for implementing carrier aggregation. This segment is experiencing growth due to continuous investments from telecom operators looking to upgrade their existing infrastructure to support higher data speeds and improved network reliability. Innovations in hardware design and the integration of AI-driven components are further propelling this segment, making it a crucial part of the overall carrier aggregation solutions market.

In the software segment, growth is driven by the increasing need for sophisticated network management tools that enable telecom operators to efficiently manage and optimize the allocation of resources across multiple frequency bands. Advanced software solutions facilitate better control over network performance, allowing operators to dynamically aggregate carriers based on real-time demand, which enhances customer experience. This segment is also

The size of the Global Healthcare Customer Data Platform market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 27.11% during the forecast period.The Global Healthcare Customer Data Platform Market is a fast-emerging business that enables healthcare organizations to unlock power in their data. A Healthcare Customer Data Platform, or HCDP, is a single piece of software, designed to unite patient data from all the sources, including EHRs, clinical trials, wearables, and social media, in one, central repository to allow healthcare providers to access a holistic and action-oriented view of each patient's health journey. HCDPs revolutionize healthcare by driving personalized patient experiences. Critical analysis of large amounts of data identifies important patterns and trends, which will enable healthcare providers to tailor treatments, medications, and communication for individuals. For instance, HCDPs enable proactive care management with regard to the possible health risks of different patients and immediate interventions whenever health risks get closer. The capabilities of HCDPs optimize operational efficiency further. HCDPs automate what has been a time-consuming data collection and analysis for the various purposes. These systems simplify workflow procedures, therefore minimizing administrative burdens. Healthcare professionals can maximally be engaged in the provision of quality healthcare services rather than fumbling about paperwork. Conclusion This Global Healthcare Customer Data Platform Market is expected to grow exponentially due to the increasing importance of data-driven insights. Patient outcomes improvement, operational efficiency, and complete overhaul of service delivery will be the core approach that dominates the way healthcare providers embrace HCDPs. Recent developments include: In March 2022, GE Healthcare planned to introduce the Edison Digital Health Platform, a vendor-agnostic hosting and data aggregation platform with an integrated artificial intelligence (AI) engine. The platform was developed to enable hospitals and healthcare systems to effectively deploy clinical, workflow, analytics, and AI tools within the healthcare environment., In March 2022, Salesforce introduced innovations across Customer 360 for Health, including Patient Data Platform and Patient Commerce Portal updates. These new features, powered by Marketing Cloud and Commerce Cloud, enable healthcare and life sciences companies to create personalized digital experiences while safeguarding patient data.. Key drivers for this market are: Technological Advancements, Growing Burden of Diseases. Potential restraints include: High Price and Maintenance, Lack of Proper IT infrastructure. Notable trends are: The Cloud-Based Segment is Expected to Account for the Significant Market Share During the Forecast Period.

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient’s Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient’s electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.

Snapshot img

Industrial Multiprotocol Gateways Market Size 2024-2028

The industrial multiprotocol gateways market size is forecast to increase by USD 230.5 million at a CAGR of 11.22% between 2023 and 2028.

The market is witnessing significant growth, driven primarily by the increasing prominence of IoT gateways. These gateways facilitate seamless communication between various industrial protocols and enable interoperability between different systems. Another key driver is the development of system-on-chip solutions for multiprotocol communication, which reduces complexity and cost for manufacturers. However, the market also faces challenges, including barriers created by traditional mechanisms for communication and interoperability. These obstacles necessitate the adoption of more flexible and open standards to ensure compatibility and ease of integration.
Companies seeking to capitalize on market opportunities should focus on developing innovative solutions that address these challenges and offer improved functionality, reliability, and security. By staying abreast of industry trends and addressing the specific needs of their customers, they can effectively navigate the competitive landscape and maintain a strong market position.

What will be the Size of the Industrial Multiprotocol Gateways Market during the forecast period?

Request Free Sample

The market continues to evolve, driven by the increasing demand for seamless connectivity and data integration across diverse industrial automation systems. These gateways facilitate the communication between various industrial protocols, enabling data aggregation and conversion for real-time monitoring and analysis. Fault detection and data security are integral components of these solutions, ensuring operational efficiency and industrial security. System integration and edge computing optimize costs by enabling local processing and reducing the need for extensive data transfer. Predictive maintenance and building automation applications further enhance the value proposition of these gateways. Ethernet/IP, OPC UA, and other connectivity solutions play a crucial role in enabling smart factories and digital transformation by facilitating machine-to-machine communication and cloud connectivity.
Data logging, process control, and energy management are among the numerous applications that benefit from these advanced technologies. Hardware platforms, CAN bus, serial communication, and industrial Ethernet are some of the technologies that support the development of these gateways. The ongoing digital transformation in industries is fueling the demand for advanced data analytics and device management capabilities, further expanding the market's potential.

How is this Industrial Multiprotocol Gateways Industry segmented?

The industrial multiprotocol gateways industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Remote monitoring
  Product optimization
  Preventive maintenance


End-user

  Process industries
  Discrete industries


Technology

  IoT Gateways
  System-on-Chip
  Cloud-Based


Protocol

  PROFINET
  EtherCAT
  Modbus


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Application Insights

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

The market encompasses a range of technologies and applications that facilitate seamless communication and integration between various industrial systems. One significant segment of this market is remote monitoring, which involves the use of software applications to monitor and manage industrial processes from a distance. These applications enable real-time data collection and analysis from connected devices, such as sensors and machines, using technologies like cloud computing, the Internet of Things (IoT), and data analytics. By leveraging remote monitoring, businesses can optimize operational efficiency, improve industrial security, and enhance industrial automation. Industrial ethernet, Ethernet/IP, CAN bus, and serial communication are among the protocols used for connectivity solutions in this market.

OPC UA, protocol conversion, and data aggregation are essential components of these applications. Smart factories, building automation, energy management, and predictive maintenance are some of the key areas where remote monitoring is being adopted to drive cost optimization and improve performance. Edge Computing and machine-to-machine communication are also gaini

Data Concentrator Units (DCUs) Market Outlook

The global Data Concentrator Units (DCUs) market size is anticipated to reach approximately $1.8 billion by 2023 and is projected to grow to $3.5 billion by 2032, with a compound annual growth rate (CAGR) of 7.6% during the forecast period. This growth is driven by the increasing adoption of smart grid technologies, which necessitate efficient data management and communication solutions. The rising demand for advanced metering infrastructure (AMI) and the need for energy conservation are also significant contributors to market expansion.

One of the primary growth factors for the DCUs market is the escalating adoption of smart grid technologies globally. Smart grids require sophisticated data management solutions to ensure the efficient transmission and distribution of electricity. DCUs play a crucial role in aggregating data from various metering devices and transmitting it to central systems for analysis. This improves grid reliability and operational efficiency, thereby driving the demand for DCUs. Additionally, governments worldwide are investing heavily in smart grid projects to enhance energy efficiency and reduce carbon footprints, further propelling market growth.

Another key growth driver is the rising need for advanced metering infrastructure (AMI). AMI systems, which include smart meters and DCUs, enable two-way communication between utilities and consumers. This facilitates real-time monitoring and management of energy consumption, leading to better energy conservation and cost savings. The increasing deployment of AMI systems in residential, commercial, and industrial sectors is significantly boosting the demand for DCUs. Moreover, the growing awareness about the benefits of smart meters among consumers is expected to accelerate market growth during the forecast period.

The integration of renewable energy sources into the power grid is also contributing to the growth of the DCUs market. Renewable energy sources, such as solar and wind, are intermittent and require precise monitoring and management to ensure grid stability. DCUs help in aggregating and transmitting data from renewable energy sources to the central control systems, enabling efficient grid management. As countries worldwide strive to increase the share of renewable energy in their energy mix, the demand for DCUs is expected to rise. Additionally, the development of microgrids, which rely on DCUs for data aggregation and communication, is further driving market growth.

The introduction of Rail Type Dc Energy Meter into the smart grid ecosystem is revolutionizing the way energy consumption is monitored and managed. These meters are designed to provide precise and reliable data on energy usage, making them an essential component in the efficient operation of modern power grids. By integrating Rail Type Dc Energy Meter, utilities can achieve enhanced accuracy in energy measurement, which is crucial for optimizing grid performance and reducing energy losses. Furthermore, these meters support advanced data analytics, enabling utilities to gain deeper insights into consumption patterns and make informed decisions for energy distribution. As the demand for smart grid solutions continues to rise, the adoption of Rail Type Dc Energy Meter is expected to grow, contributing to the overall efficiency and sustainability of energy systems.

Regionally, North America and Europe are anticipated to hold significant shares in the DCUs market due to the early adoption of smart grid technologies and substantial investments in grid modernization projects. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by rapid urbanization, industrialization, and increasing investments in smart grid infrastructure. Countries such as China, India, and Japan are leading in the adoption of smart grids, thereby boosting the demand for DCUs. Additionally, government initiatives and policies aimed at improving energy efficiency and reducing carbon emissions are expected to further propel market growth in the region.

Type Analysis

The DCUs market is segmented by type into single-phase and three-phase units. Single-phase DCUs are predominantly used in residential applications where the power consumption is relatively low. These units are simpler in design and more cost-effective, making them a popular choice for residential metering. The growing adoption of smart homes and the increasing number of residential

Broadband Aggregation Service Market Outlook

As of 2023, the broadband aggregation service market size was valued at approximately USD 25 billion and is projected to reach USD 45 billion by 2032, growing at a CAGR of roughly 7.1%. The primary growth drivers for this market include the increasing demand for high-speed internet, the proliferation of smart devices, and the rising need for data-intensive applications.

The growing digitization across various sectors is a critical factor propelling the broadband aggregation service market. As businesses continue to embrace digital transformation, the demand for high-speed, reliable internet connectivity has surged. This has led to increased investments in broadband infrastructure and services, particularly in urban areas. Additionally, the rise of remote working culture, accelerated by the COVID-19 pandemic, has further amplified the need for robust broadband services, making broadband aggregation services indispensable for both residential and commercial users.

Another significant growth factor is the rapid adoption of Internet of Things (IoT) devices. The IoT ecosystem relies heavily on consistent and high-speed internet connections to function effectively. This has created a substantial demand for broadband aggregation services, which can efficiently manage and distribute internet bandwidth to multiple connected devices. Moreover, the advent of technologies such as 5G is expected to boost the market further, as it promises to deliver faster internet speeds and improved connectivity, thereby enhancing the overall user experience.

The expansion of broadband services into rural and underserved areas also contributes to market growth. Governments and private sector players are increasingly focusing on bridging the digital divide by extending broadband infrastructure to these regions. Initiatives such as public-private partnerships and government-funded projects are playing a crucial role in this expansion. For instance, various countries are launching national broadband plans aimed at providing high-speed internet access to every citizen, thereby driving the demand for broadband aggregation services.

Regionally, the Asia Pacific is poised to witness significant growth in the broadband aggregation service market. With a burgeoning population, rapid urbanization, and increasing internet penetration, countries like China, India, and Japan are experiencing a surge in demand for broadband services. Similarly, North America and Europe are also expected to see substantial growth, driven by technological advancements and high adoption rates of digital services. Latin America and the Middle East & Africa, though currently smaller markets, are anticipated to grow steadily due to increasing investments in broadband infrastructure.

Service Type Analysis

The broadband aggregation service market can be segmented by service type into residential, commercial, and industrial. In the residential segment, the demand for high-speed internet has seen an unprecedented rise due to the increasing number of smart homes and the proliferation of streaming services. Consumers are now seeking uninterrupted and high-quality internet connections to support activities such as online gaming, video conferencing, and smart device management. Broadband aggregation services play a critical role in meeting these consumer needs by ensuring efficient bandwidth distribution and minimizing connection disruptions.

In the commercial segment, businesses are increasingly relying on broadband aggregation services to support their operations. High-speed internet is essential for various business functions, including communication, cloud computing, and data transfer. The rise of e-commerce, online services, and digital marketing has further amplified the need for reliable internet connections in the commercial sector. Additionally, the growing trend of remote work has led companies to invest in robust broadband solutions to ensure seamless connectivity for their employees, regardless of their location.

The industrial segment is also witnessing significant growth, driven by the adoption of Industry 4.0 technologies. Industrial facilities require high-speed, reliable internet connections to support automation, real-time data processing, and IoT applications. Broadband aggregation services enable these facilities to manage their internet bandwidth efficiently, ensuring that critical systems remain operational without interruptions. Moreover, the increasing use of remote monitoring and control systems in industries further unde

This data is from the Smart Triage + QI: A digital triaging platform to improve quality of care for critically ill children study. Data collected for this study occurred from December 2021 to July 2023. Objective(s): This is a pre-post intervention study involving pediatric patients presenting to the study hospitals in seek of medical care for an acute illness. The purpose of this project was to implement Smart Triage + QI to improve the quality of care at four health care facilities in Uganda. The primary objective of the program is to enable healthcare workers to recognize the most urgent children more rapidly and allocate existing resources more efficiently. The second objective is to use the proactive processes of QI to identify and examine opportunities for ongoing improvement to strengthen the health system. The study involved two phases: (I) Baseline Period, and (II) Intervention Period. Phase II also involved a community sub-study at 1 site to identify key messaging for an appropriate methods for disseminating educational materials for VHTs and caregivers on Smart Triage. Data Description: Data was collected at the time of triage by trained study nurses using a custom-built mobile application. All data entered into the mobile application was stored an encrypted database. Data was uploaded directly from the mobile device to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). Outcomes were obtained from facility records or telephone follow-up at 7-10 days and the data was collected electronically. Starting in June 2022, outcomes were also collected via automated follow-up (SMS/WhatsApp) messages at one site. Time-specific outcomes were tracked using an RFID tagging system with study personnel as backup. Limitations: There is missing data and some variables were not collected at all sites. Ethics Declaration: This study was approved by the Makerere University Higher Degrees research and Ethics Committee (SPH-2021-41), the Uganda National Institute of Science and Technology (HS 1745ES). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Note: Find data at source. ・ This data hub, COVID-EMDA+ (Coronavirus Disease - Electricity Market Data Aggregation+), is specifically designed to track the potential impacts of COVID-19 on the existing U.S. electricity markets. Many different data sources are merged and harmonized here in order to enable further interdisciplinary researches. (https://github.com/tamu-engineering-research/COVID-EMDA)Publication: https://www.cell.com/joule/fulltext/S2542-4351(20)30398-6#%20This data hub contains five major components: U.S. electricity market data, public health data, weather data, mobile device location data, and satellite images. For some categories, multiple data sources are carefully gathered to ensure accuracy.Electricity Market Data includes the generation mix, metered load profiles and day-ahead locational marginal prices data. We also include the day-ahead load forecasting, congestion price, forced outage and renewable curtailment data as the supplementary source. (Link: CAISO, MISO, ISO-NE, NYISO, PJM, SPP, ERCOT, EIA, EnergyOnline) Public Health Data includes the COVID-19 confirmed cases, deaths data, infection rate and fatal rate. We aggregate and fine-tune the data to market and city levels. (Link: John Hopkins CSSE)

Weather Data includes temperature, relative humidity, wind speed and dew point data. Typical weather stations are selected according to their geological locations and data quality. (Link: Iowa State Univ IEM, NOAA)

Mobile Device Location Data includes social distancing data and patterns of visits to Point of Interests (POIs). These data are derived by aggregating and processing the real-time GPS location of cellphone users by Census Block Group. To obtain the access to the original data, please click the link below and apply for SafeGraph's permission (totally free). (Link: Mobility Data from SafeGraph)

Night Time Light (NTL) Satellite Data includes the raw satellite image taken at night time in each area. (Link: NTL Images from NASA) The original data sources for the COVID-EMDA+ data hub are listed at https://www.cell.com/cms/10.1016/j.joule.2020.08.017/attachment/a9f9c743-1252-41f4-bba3-913f3b01aa5a/mmc1.pdf.

Intelligent Invoice Management System

Project Description:
The Intelligent Invoice Management System is an advanced AI-powered platform designed to revolutionize traditional invoice processing. By automating the extraction, validation, and management of invoice data, this system addresses the inefficiencies, inaccuracies, and high costs associated with manual methods. It enables businesses to streamline operations, reduce human error, and expedite payment cycles.

Problem Statement:
Manual invoice processing involves labor-intensive tasks such as data entry, verification, and reconciliation. These processes are time-consuming, prone to errors, and can result in financial losses and delays. The diversity of invoice formats from various vendors adds complexity, making automation a critical need for efficiency and scalability.

Proposed Solution:
The Intelligent Invoice Management System automates the end-to-end process of invoice handling using AI and machine learning techniques. Core functionalities include:
1. Invoice Generation: Automatically generate PDF invoices in at least four formats, populated with synthetic data.
2. Data Development: Leverage a dataset containing fields such as receipt numbers, company details, sales tax information, and itemized tables to create realistic invoice samples.
3. AI-Powered Labeling: Use Tesseract OCR to extract labeled data from invoice images, and train YOLO for label recognition, ensuring precise identification of fields.
4. Database Integration: Store extracted information in a structured database for seamless retrieval and analysis.
5. Web-Based Information System: Provide a user-friendly platform to upload invoices and retrieve key metrics, such as:
- Total sales within a specified duration.
- Total sales tax paid during a given timeframe.
- Detailed invoice information in tabular form for specific date ranges.

Key Features and Deliverables:
1. Invoice Generation:
- Generate 20,000 invoices using an automated script.
- Include dummy logos, company details, and itemized tables for four items per invoice.

1. Label Definition and Format:

   • Define structured labels (TBLR, CLASS Name, Recognized Text).
   • Provide labels in both XML and JSON formats for seamless integration.

2. OCR and AI Training:

   • Automate labeling using Tesseract OCR for high-accuracy text recognition.
   • Train and test YOLO to detect and classify invoice fields (TBLR and CLASS).

3. Database Management:

   • Store OCR-extracted labels and field data in a database.
   • Enable efficient search and aggregation of invoice data.

4. Web-Based Interface:

   • Build a responsive system for users to upload invoices and retrieve data based on company name or NTN.
   • Display metrics and reports for total sales, tax paid, and invoice details over custom date ranges.

Expected Outcomes: - Reduction in manual effort and operational costs.
- Improved accuracy in invoice processing and financial reporting.
- Enhanced scalability and adaptability for diverse invoice formats.
- Faster turnaround time for invoice-related tasks.

By automating critical aspects of invoice management, this system delivers a robust and intelligent solution to meet the evolving needs of businesses.

Snapshot img

Competitive Intelligence Tools Market Size 2025-2029

The competitive intelligence tools market size is forecast to increase by USD 27.95 billion, at a CAGR of 9.5% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the exponential increase in data and the rising adoption of smart connected devices. These trends are transforming business landscapes, creating opportunities for companies to gain valuable insights and make informed decisions. However, this data-driven evolution also presents challenges. Data privacy and security concerns are becoming increasingly prominent, as organizations grapple with the responsibility of safeguarding sensitive information. Effective management of these challenges will be crucial for companies seeking to capitalize on market opportunities and navigate the competitive landscape successfully. To stay ahead, they must invest in advanced analytics tools, implement robust data security measures, and adopt a proactive approach to competitive intelligence.
The ability to harness the power of data while addressing privacy and security concerns will be a key differentiator in the market. Companies must invest in robust solutions that balance the need for insights with the imperative to protect data. By doing so, they can turn data into a strategic asset, driving growth and innovation. Online platforms that offer competitive intelligence tools integrate various data sources, including IoT devices, social media, and AI, to provide comprehensive insights for strategic planning.

What will be the Size of the Competitive Intelligence Tools Market during the forecast period?

Request Free Sample

In today's business landscape, competitive intelligence has become a crucial element for organizations to gain a competitive edge. The market for competitive intelligence tools is witnessing significant growth, driven by the increasing demand for data-driven decision-making. This market encompasses various solutions, including statistical analysis, data warehousing, scenario analysis, and competitive intelligence consulting. Data is the backbone of these tools, and unstructured data is gaining prominence. Data reliability is paramount, necessitating data security protocols and data validation. Customized reports, data transformation, and data mining techniques enable businesses to derive valuable insights from their data. Data aggregation, market research reports, and industry analysis reports provide a comprehensive view of the competitive landscape.
Interactive dashboards, predictive modeling, and API integration offer real-time monitoring and user experience enhancements. Data science expertise, data modeling, and integration capabilities are essential for trend analysis and data insights. Deep learning algorithms, data cleansing, and data modeling contribute to data accuracy and consistency. Data standardization and data completeness ensure data quality and data governance policies. Competitive intelligence strategy relies on data enrichment, hypothesis testing, and data visualization dashboards to provide actionable insights. Proprietary algorithms and user interface design further differentiate offerings. Data overload is a common challenge, leading to the adoption of predictive analysis and machine learning technologies to help businesses make sense of vast amounts of data.

How is this Competitive Intelligence Tools Industry segmented?

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

Deployment

  Cloud-based
  On-premises


Distribution Channel

  Large enterprises
  SMEs


End-user

  IT and telecom
  Healthcare
  Retail
  Financial services
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. In today's business landscape, cloud-based competitive intelligence tools are gaining significant traction as organizations seek to reduce hardware expenses and minimize in-house storage requirements. These solutions, available as software as a service (SaaS) and subscription-based models, encompass various offerings such as data analytics software, financial intelligence tools, and knowledge management systems. In October 2024, EY (Ernst and Young) introduced EY Competitive Edge, a cloud-based platform that leverages generative AI (GenAI) technologies and is built on Microsoft Azure. This innovative solution delivers real-time, customized insights into markets, companies, and industries. Th

Health data and environmental data are commonly collected at different levels of aggregation. A persistent challenge of using a spatial regression model to link these data is that their associations can vary as a function of aggregation. This results into ecological fallacy if association at one aggregation level is used for inferencing at another level. We address this challenge by presenting a hierarchically adaptable spatial regression model. In essence, the model extends the spatially varying coefficient model to allow the response to be count data at larger aggregation levels than that of the covariates. A Bayesian hierarchical approach is used for inferencing the model parameters. Robust inference and optimal prediction over geographical space and at different spatial aggregation levels are studied by simulated data sets. The spatial associations at different spatial supports are largely different, but can be efficiently inferred when prior knowledge of the associations is available. The model is applied to study hand, foot and mouth disease (HFMD) in Da Nang city, Viet Nam. Decrease in vegetated areas corresponds with elevated HFMD risks. A study to the identifiability of the parameters shows a strong need for a highly informative prior distribution. We conclude that the model is robust to the underlying aggregation levels of the calibrating data for association inference and it is ready for application in health geography.

The drug-gene interaction database (DGIdb) is a resource that aggregates interaction data from over 40 different resources into one platform with the primary goal of making the druggable genome accessible to clinicians and researchers. By providing a public, computationally accessible database, DGIdb enables therapeutic insights through broad aggregation of drug-gene interaction data.

As part of our aggregation process, DGIdb preserves data regarding interaction types, directionality, and other attributes that enable filtering or biochemical insight. However, source data are often incomplete and may not contain the therapeutic relevance of the interaction. In this report, we address these missing data and demonstrate a pipeline for extracting physiological context from free-text sources. We apply existing large language models (LLMs) to tag and extract indications, cancer types, and relevant pharmacogenomics from free-text, FDA approved labels. We are then able to utilize the Variant Interpretation for Cancer Consortium (VICC) normalization services to ground extracted data back to formally grouped concepts.

In a preliminary test set of 355 FDA labels, we were able to normalize 59.4% of extracted chemical entities back to ontologically-grounded therapeutic concepts. We can link this therapeutic context data back to interaction records already searchable within DGIdb. By using LLMs to extract this data set, we can supplement our existing interaction data with relevant indications, pharmacogenomic data and mutational statuses that may inform the therapeutic relevance of a particular interaction. Inclusion of these data will be invaluable for variant interpretation pipelines where mutational status can lead to the identification of a lifesaving therapeutic.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

The dataset represents the code base developed for the generation of water quality metrics from various data sources (eReefs Biogeochemical models, MODIS Satellite imaging and AIMS in situ sampling).

The water quality metric used underpinning previous Report Cards (until 2015) presented a number of significant shortcomings: - It was solely based on remote sensing-derived data. Concerns were raised about the appropriateness of exclusively relying on remote sensing to evaluate inshore water quality, considering well-documented challenges in obtaining accurate estimates from optically complex waters and the fact that only limited valid satellite observations are available in the wet season due to cloud cover; - It was limited to reporting on two indicators and did not incorporate other water quality data and indicators collected through the Marine Monitoring Program (MMP) and the Integrated Marine Observing System (IMOS); - It appeared relatively insensitive to large terrestrial inputs into the GBR lagoon during large rainfall and runoff events, most likely due to the binary assessment of compliance relative to the water quality guidelines and aggregation and averaging over large spatial and temporal scales;

In 2016, based on the limitations described above, the Reef Plan Independent Science Panel (ISP) expressed a lack of confidence in the water quality metric that underpinned Report Cards (prior to 2015) and recommended that a new approach be identified for the Report Card 2016 and future Report Cards. The ISP also acknowledged substantial advancements in modelling water quality through the eReefs biogeochemical models and the fact that recent research and method development had improved our ability to construct report card metrics.

Methods:

• Index scoring strategies were systematically assessed to meet key objectives of sensitivity and representativeness, to allow data aggregation and to enable the integration of additional water quality measures when these become available in the future. A preferred method was identified as the scaled modified amplitude method with fixed caps sets at half and twice the threshold values, which was tested both theoretically and using historical datasets.
• Aggregation strategies were reviewed and a hierarchical aggregation scheme was developed to allow multiple measures and sub-indicators to be combined into a single metric and to allow spatial and temporal aggregation. The process was designed to maintain the richness of information and allow the propagation of uncertainty, which were key project objectives.
• The resulting water quality metric calculation process and parameters were applied to the development of the marine water quality metric component of Reef Report Card 2016, covering the reporting period 1 October 2015 to 30 September 2016.

Format:

The data are in the form of R code. Note, the code cannot be run as a standalone entity as it relies on non-public input data sources.

Abstract

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

Geographic coverage

National Coverage

Analysis unit

The main statistical unit to be used for the survey is the establishment. For simple businesses that undertake a single activity at a single location there is a one-to-one relationship between the establishment and the enterprise. For large and complex enterprises, however, it is desirable to separate each activity of an enterprise into establishments to provide the most detailed information possible for industrial analysis. The business register will need to be developed in such a way that records the links between establishments and their parent enterprises. The business register will be created from administrative records and may not have enough information to recognize all establishments of complex enterprises. Large businesses will be contacted prior to the survey post-out to determine if they have separate establishments. If so, the extended structure of the enterprise will be recorded on the business register and a questionnaire will be sent to the enterprise to be completed for each establishment.

SBS has decided to follow the New Zealand simplified version of its statistical units model for the 2009 BAS. Future surveys may consider location units and enterprise groups if they are found to be useful for statistical collections.

It should be noted that while establishment data may enable the derivation of detailed benchmark accounts, it may be necessary to aggregate up to enterprise level data for the benchmarks if the ongoing data used to extrapolate the benchmark forward (mainly VAGST) are only available at the enterprise level.

Universe

The BAS's covered all employing units, and excluded small non-employing units such as the market sellers. The surveys also excluded central government agencies engaged in public administration (ministries, public education and health, etc.). It only covers businesses that pay the VAGST. (Threshold SAT$75,000 and upwards).

Kind of data

Sample survey data [ssd]

Sampling procedure

-Total Sample Size was 1240 -Out of the 1240, 902 successfully completed the questionnaire. -The other remaining 338 either never responded or were omitted (some businesses were ommitted from the sample as they do not meet the requirement to be surveyed) -Selection was all employing units paying VAGST (Threshold SAT $75,000 upwards)

WILL CONFIRM LATER!!

OSO LE MEA E LE FAASA...AEA :-)

Mode of data collection

Mail Questionnaire [mail]

Research instrument

1. General instructions, authority for the survey, etc;
2. Business demography information on ownership, contact details, structure, etc.;
3. Employment;
4. Income;
5. Expenses;
6. Inventories;
7. Profit or loss and reconciliation to business accounts' profit and loss;
8. Fixed assets - purchases, disposals, book values
9. Thank you and signature of respondent.

Supplementary Pages Additional pages have been prepared to collect data for a limited range of industries. 1.Production data. To rebase and redevelop the Industrial Production Index (IPI), it is intended to collect volume of production information from a selection of large manufacturing businesses. The selection of businesses and products is critical to the usefulness of the IPI. The products must be homogeneous, and be of enough importance to the economy to justify collecting the data. Significance criteria should be established for the selection of products to include in the IPI, and the 2009 BAS provides an opportunity to collect benchmark data for a range of products known to be significant (based on information in the existing IPI, CPI weights, export data, etc.) as well as open questions for respondents to provide information on other significant products. 2.Tourism. There is a strong demand for estimates of tourism value added. To estimate tourism value added using the international standard Tourism Satellite Account methodology requires the use of an input-output table, which is beyond the capacity of SBS at present. However, some indicative estimates of the main parts of the economy influenced by tourism can be derived if the necessary data are collected. Tourism is a demand concept, based on defining tourists (the international standard includes both international and domestic tourists), what products are characteristically purchased by tourists, and which industries supply those products. Some questions targeted at those industries that have significant involvement with tourists (hotels, restaurants, transport and tour operators, vehicle hire, etc.), on how much of their income is sourced from tourism would provide valuable indicators of the size of the direct impact of tourism.

Cleaning operations

Partial imputation was done at the time of receipt of questionnaires, after follow-up procedures to obtain fully completed questionnaires have been followed. Imputation followed a process, i.e., apply ratios from responding units in the imputation cell to the partial data that was supplied. Procedures were established during the editing stage (a) to preserve the integrity of the questionnaires as supplied by respondents, and (b) to record all changes made to the questionnaires during editing. If SBS staff writes on the form, for example, this should only be done in red pen, to distinguish the alterations from the original information.

Additional edit checks were developed, including checking against external data at enterprise/establishment level. External data to be checked against include VAGST and SNPF for turnover and purchases, and salaries and wages and employment data respectively. Editing and imputation processes were undertaken by FSD using Excel.

Sampling error estimates

NOT APPLICABLE!!

The Mapping Manuscript Migrations (MMM) project was funded from 2017 to 2020 by the Digging into Data Challenge of the Trans-Atlantic Platform. The project partners were the University of Oxford, the University of Pennsylvania, Aalto University, and the Institut de recherche et d'histoire des textes. The project's goal was to bring together data from different sources relating to the history and provenance of medieval and Renaissance manuscripts, enabling large-scale browsing and searching through a semantic Web portal as well as by direct access to the data. Three separate datasets covering more than 200,000 manuscripts, were combined into a unified knowledge graph, using Linked Open Data technologies. This approach includes a unified data model which is based on the CIDOC-CRM and FRBRoo ontologies, as well as more than 20 million RDF triples. Overlapping vocabularies for persons, places, and organizations in the source datasets were reconciled against identifiers from VIAF, GeoNames, and the Getty Thesaurus of Geographical Names. Works and manuscripts were reconciled by semi-automatic matching techniques based on string similarities. The three source datasets were: (1) Schoenberg Database of Manuscripts from the Schoenberg Institute for Manuscript Studies, University of Pennsylvania; (2) Bibale database from the Institut de recherche et d'histoire des textes (IRHT-CNRS, Paris) and (3) Medieval Manuscripts in Oxford Libraries catalogue from the Bodleian Libraries, University of Oxford. To test and demonstrate its usefulness, the MMM Knowledge Graph is in use in the MMM Semantic Portal. Based on the Sampo-UI software developed at Aalto University, the portal enables browsing, searching, and filtering across the project's triple store, together with map-based visualizations of the results.

Hundreds of thousands of European pre-modern manuscripts have survived until the present day. As the result of changes in their ownership over the centuries, they are now spread all over the world. Collectively they constitute a great cultural and scholarly treasure. There are many sources of data relating to them, and new sources continue to proliferate in the digital environment. This project will link disparate datasets from Europe and North America to provide an international view of the history and provenance of these manuscripts. The aggregated data will enable researchers to analyse and visualize these topics at scales ranging from individual manuscripts to thousands of manuscripts. We will be able to show how these manuscripts have travelled across time and space to their current locations, where they continue to find new audiences. The project will also be of particular relevance and value to libraries and other collecting institutions. The results of its analyses will situate their manuscript collections in the broader historical context of patterns and trends in collecting, while its methodology and its body of data will provide a very important resource for further aggregation and exploration in the future. The data linkage techniques and visualization methodologies deployed by the project will be of wider applicability to all kinds of cultural heritage objects and collections as well as manuscripts.

Dataset Description: This dataset contains materials from a the Smart Discharges Transition to Scale parent study within the Smart Discharges program of research. Materials include the parent study protocol and associated documents. See the Metadata section below for links to related publications and datasets. Background: In Uganda, approximately 5% of children admitted with severe infections die after they have been discharged from the hospital, mostly at home. Most of these deaths are preventable as they are largely due to the way that discharges are done and how follow-ups are planned. Health workers and caregivers are often unaware of this period of vulnerability and are poorly equipped to identify and handle this critical situation. Our previous work focused on developing and evaluating models and technology to predict, before discharge, an individual child’s risk of recurrent illness, as well as to provide additional post-discharge support to at-risk children. The goal of this project is to determine how best to scale the Smart Discharges Program through a four-phased approach, each corresponding to a specific objective. Phase I : aims to understand the reasons for suboptimal discharge by evaluating the pediatric discharge process from hospital admission through the transition to care within the community. Phase II : aims to assess pediatric discharge policies and facility readiness for change in a nationally representative sample of health facilities in Uganda. Phase III : aims to evaluate the effects of the Smart Discharges Health Worker Training Program on discharge care practices and procedures. Phase IV : aims to complete the facility-based linkage to care through the use of a community-based follow-up system. Methods: Each of the four project phases utilizes different research methodologies. Phase I is a mixed methods prospective study utilizing patient journey mapping, discharge process mapping, and focus group discussions at 3 Ugandan Hospitals. Phase II is a cross-sectional, survey-based study conducted at 36 health facilities providing in-patient pediatric care in Uganda. Phase III and IV : (implemented together) is a quality improvement intervention at 16 health facilities in Uganda. Discussion: Ultimately this work is focused on ensuring widespread adoption of Smart Discharges practices throughout Uganda by building capacity that ensures sustainability. Exploring and characterizing the existing pediatric discharge process, including human and health system factors that impact this process, will allow us to operationalize the Smart Discharges innovation into an effective health-systems approach to this neglected issue. Ethics Declaration: Ethics approvals have been obtained from the Makerere University School of Public Health (MakSPH) Institutional Review Board (PI: 850; PII: 851; PIII/IV: 836), the Uganda National Council of Science and Technology (UNCST) in Uganda (PI: HS929ES; PII: HS928ES; PIII/IV: HS926ES) and the University of British Columbia in Canada (PI-IV: H20-02519). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab Coordinator at sepsiscolab@bcchr.ca or visit our website.