This project was done to analyze sales data: to identify trends, top-selling products, and revenue metrics for business decision-making. I did this project offered by MeriSKILL, to learn more and be exposed to real-world projects and challenges that will provide me with valuable industry experience and help me develop my data analytical skills.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20837845%2Fe3561db319392bf9cc8b7d3fcc7ed94d%2F2019%20Sales%20dashboard.png?generation=1717273572595587&alt=media" alt=""> More on this project is on Medium

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

Snapshot img

Alternative Data Market Size 2025-2029

The alternative data market size is valued to increase USD 60.32 billion, at a CAGR of 52.5% from 2024 to 2029. Increased availability and diversity of data sources will drive the alternative data market.

Major Market Trends & Insights

North America dominated the market and accounted for a 56% growth during the forecast period.
By Type - Credit and debit card transactions segment was valued at USD 228.40 billion in 2023
By End-user - BFSI segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 6.00 million
Market Future Opportunities: USD 60318.00 million
CAGR from 2024 to 2029 : 52.5%

Market Summary

The market represents a dynamic and rapidly expanding landscape, driven by the increasing availability and diversity of data sources. With the rise of alternative data-driven investment strategies, businesses and investors are increasingly relying on non-traditional data to gain a competitive edge. Core technologies, such as machine learning and natural language processing, are transforming the way alternative data is collected, analyzed, and utilized. Despite its potential, the market faces challenges related to data quality and standardization. According to a recent study, alternative data accounts for only 10% of the total data used in financial services, yet 45% of firms surveyed reported issues with data quality.
Service types, including data providers, data aggregators, and data analytics firms, are addressing these challenges by offering solutions to ensure data accuracy and reliability. Regional mentions, such as North America and Europe, are leading the adoption of alternative data, with Europe projected to grow at a significant rate due to increasing regulatory support for alternative data usage. The market's continuous evolution is influenced by various factors, including technological advancements, changing regulations, and emerging trends in data usage.

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

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Alternative Data Market Segmented ?

The alternative data 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.

Type

  Credit and debit card transactions
  Social media
  Mobile application usage
  Web scrapped data
  Others


End-user

  BFSI
  IT and telecommunication
  Retail
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.

Alternative data derived from credit and debit card transactions plays a significant role in offering valuable insights for market analysts, financial institutions, and businesses. This data category is segmented into credit card and debit card transactions. Credit card transactions serve as a rich source of information on consumers' discretionary spending, revealing their luxury spending tendencies and credit management skills. Debit card transactions, on the other hand, shed light on essential spending habits, budgeting strategies, and daily expenses, providing insights into consumers' practical needs and lifestyle choices. Market analysts and financial institutions utilize this data to enhance their strategies and customer experiences.

Natural language processing (NLP) and sentiment analysis tools help extract valuable insights from this data. Anomaly detection systems enable the identification of unusual spending patterns, while data validation techniques ensure data accuracy. Risk management frameworks and hypothesis testing methods are employed to assess potential risks and opportunities. Data visualization dashboards and machine learning models facilitate data exploration and trend analysis. Data quality metrics and signal processing methods ensure data reliability and accuracy. Data governance policies and real-time data streams enable timely access to data. Time series forecasting, clustering techniques, and high-frequency data analysis provide insights into trends and patterns.

Model training datasets and model evaluation metrics are essential for model development and performance assessment. Data security protocols are crucial to protect sensitive financial information. Economic indicators and compliance regulations play a role in the context of this market. Unstructured data analysis, data cleansing pipelines, and statistical significance are essential for deriving meaningful insights from this data. New

Unlock the Insights: Dive into the Comprehensive COVID-19 Dataset and Uncover Key Patterns, Trends, and Impacts Worldwide. Empower Your Analysis with Rich, Reliable, and Up-to-Date Data.

The COVID-19 dataset provides a comprehensive collection of information related to the global pandemic. It encompasses various aspects such as the number of cases, deaths, recoveries, testing, and more. The dataset serves as a valuable resource for researchers, analysts, and individuals seeking to understand the impact and spread of the virus.

The dataset is compiled from reputable sources, including official government reports, health organizations, and reliable data repositories. It ensures the accuracy and reliability of the information, making it a trusted source for COVID-19 data analysis.

The dataset is inspired by the need for reliable and up-to-date information about the COVID-19 pandemic. It aims to provide a comprehensive resource that enables researchers, analysts, and individuals to gain insights, track trends, and make data-driven decisions to combat the global health crisis.

By leveraging this dataset, users can gain a deeper understanding of the pandemic's progression, identify patterns, and contribute to the ongoing efforts in managing and mitigating the impact of COVID-19.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

Statistical analysis of the relationship between climate, weather and COVID and international and domestic tourist arrivals

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigators if further information is needed. The current study subjected the body of empirical literature on school violence to a meta-analysis or "quantitative synthesis", to determine the key individual-, school-, and community-level factors that influence violence in school. The data are based on 693 studies of school violence that contributed a total of 8,551 effect size estimates--3,840 for delinquency/aggression (44.91%) and 4,711 for victimization (55.09%). These effect sizes were drawn from 545 independent data sets and 68 different countries. The majority of effect size estimates (56.22%) were based on U.S. samples. A total of 31 different predictors of school violence were coded at the individual, institutional, and community levels. The collection includes one Stata file, Meta-Analysis-Data-for-NACJD.dta (n=8,551; 9 variables).

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

Analysis of ‘Skills | LinkedIn Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://datacatalog.worldbank.org/dataset/skills-linkedin-data on 30 September 2021.

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

Data that captures the evolution of skill requirements over time across the workforce based on updates to LinkedIn member profiles. This dataset is part of the LinkedIn World Bank Group partnership, which helps governments and researchers understand rapidly evolving labor markets with detailed and dynamic data. It allows leaders to benchmark and compare labor markets across the world; analyze skills, occupations, migration, and industries; and leverage real-time data to make policy changes. Visualizations for many of these data are available at linkedindata.worldbank.org. The data cover 2015-2019, are refreshed on an annual basis, and are available for 140+ countries.

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

Links provide access to the following input datasets: - Current range data/shapefiles can be accessed using the IUCN Red List website. Search the species of interest to download the available data. - Species observation data from iNaturalist and GBIF. Search the species of interest to download the available data. - GTOPO30 elevation data from USGS. Use Entity ID GT30W100N40 and GT30W100N90 to access elevation data or the eastern US. - WorldClim baseline and future climate projections. - Conterminous United States Land Cover Projections for 1992 to 2100 from USGS. - 2023 Crop Frequency Layer for US from USDA. Identifies crop specific planting frequency using land cover information from 2008-2023. - Watershed Boundary Dataset (WBD) for US from USGS Metadata for each dataset is provided by each data source. This dataset is associated with the following publication: Reimer, J., J. Bousquin, and J. Awkerman. Amphibian species vulnerability to agricultural pressure resulting from changing climate suitability in the southeastern US. Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, USA, vjaf109, (2025).

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

The following datasets are used for the Water Rights Demand Analysis project and are formatted to be used in the calculations. The State Water Resources Control Board Division of Water Rights (Division) has developed a methodology to standardize and improve the accuracy of water diversion and use data that is used to determine water availability and inform water management and regulatory decisions. The Water Rights Demand Data Analysis Methodology (Methodology https://www.waterboards.ca.gov/drought/drought_tools_methods/demandanalysis.html ) is a series of data pre-processing steps, R Scripts, and data processing modules that identify and help address data quality issues related to both the self-reported water diversion and use data from water right holders or their agents and the Division of Water Rights electronic water rights data.

The global open source tools market was valued at USD 35.43 billion in 2021, and it is projected to reach a value of USD 87.77 billion by 2033, exhibiting a CAGR of 8.8% during the forecast period (2025-2033). The market growth is primarily driven by the increasing adoption of open source tools by businesses of all sizes, the rising demand for data analytics and artificial intelligence (AI), the growing need for cost-effective software solutions, and the increasing awareness of the benefits of open source software. In terms of segments, the computer vision segment is expected to grow at the highest CAGR during the forecast period, owing to the increasing use of computer vision technology in various industries, such as manufacturing, healthcare, and transportation. The data visualization segment is also expected to grow at a significant rate, due to the growing demand for data visualization tools to help businesses understand and communicate their data more effectively. The North America region is expected to dominate the market throughout the forecast period, followed by Europe and Asia-Pacific. The presence of a large number of technology companies and the early adoption of open source tools in North America is contributing to the growth of the market in this region.

The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. With data from the 2007 Commodity Flow Survey and additional sources, FAF version 3 (FAF3) provides estimates for tonnage, value, and domestic ton-miles by region of origin and destination, commodity type, and mode for 2007, the most recent year, and forecasts through 2040. Also included are state-to-state flows for these years plus 1997 and 2002, summary statistics, and flows by truck assigned to the highway network for 2007 and 2040.

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?

Request Free Sample

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 solutions s

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.

Volume and stats: - 1.8M Postcodes - 5 Vulnerability areas covered - 1-100 Vulnerability rating

Our Residential Real Estate Data offers a multitude of use cases: - Market Analysis - Identify Vulnerable Consumers - Mitigate Lead Generation Risk - Risk Management - Location Planning

The key benefits of leveraging our Residential Real Estate Data include: - Data Accuracy - Informed Decision-Making - Competitive Advantage - Efficiency - Single Source

Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.

Alternative Data Analytics for Trading AI Market Outlook

As per our latest research, the global Alternative Data Analytics for Trading AI market size reached USD 4.7 billion in 2024, reflecting robust adoption across financial institutions and trading firms. The market is projected to grow at a CAGR of 23.5% during the forecast period, reaching a remarkable USD 37.6 billion by 2033. This exceptional growth is driven by the increasing demand for actionable insights from unconventional data sources, the rapid evolution of AI-based trading strategies, and the intensifying need for competitive differentiation in global capital markets.

A primary growth factor fueling the expansion of the Alternative Data Analytics for Trading AI market is the ongoing digital transformation within the financial services industry. As traditional data sources become saturated and less effective at generating alpha, investment managers and traders are turning to alternative data—such as satellite imagery, social media sentiment, and transactional records—to gain unique market perspectives. The integration of AI and machine learning technologies with these diverse data streams enables the extraction of predictive signals and actionable intelligence, which significantly enhances trading performance and portfolio optimization. This trend is further accelerated by the proliferation of big data platforms and advanced analytics tools, making it feasible for firms of all sizes to process, analyze, and derive value from massive, unstructured datasets in real time.

Another significant driver is the evolving regulatory landscape and the increasing emphasis on transparency and risk management in global financial markets. Regulatory bodies are encouraging the adoption of sophisticated analytics to ensure compliance, detect anomalies, and mitigate systemic risks. Alternative data analytics platforms, powered by AI, not only facilitate better risk assessment but also help in identifying fraudulent activities, market manipulation, and emerging market trends. This regulatory impetus, coupled with the growing sophistication of AI models, is compelling both buy-side and sell-side institutions to invest in alternative data solutions, thereby propelling market growth.

Additionally, the democratization of alternative data is expanding the market's reach beyond institutional investors to include retail traders and smaller asset managers. Cloud-based deployment models, open-source analytics frameworks, and API-driven data marketplaces are making alternative data more accessible and affordable. As a result, there is a notable surge in demand from retail investors and fintech startups seeking to leverage AI-powered trading signals derived from non-traditional data sources. This broadening end-user base is expected to sustain the market's momentum over the next decade, as more participants seek to capitalize on the informational edge provided by alternative data analytics.

From a regional perspective, North America commands the largest share of the Alternative Data Analytics for Trading AI market, owing to its advanced financial ecosystem, high concentration of hedge funds and asset managers, and early adoption of AI technologies. Europe follows closely, driven by stringent regulatory requirements and the growing presence of fintech innovation hubs. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding capital markets, and increasing investments in AI infrastructure. Latin America and the Middle East & Africa, while currently representing smaller shares, are expected to witness accelerated growth as local financial institutions embrace alternative data analytics to enhance trading efficiency and market competitiveness.

Data Type Analysis

The Data Type segment is a cornerstone of the Alternative Data Analytics for Trading AI market, encompassing a diverse array of sources such as Social Media Data, Satellite Data, Web Scraping Data, Financial Transaction Data, Sensor Data, and Others.

Analysis of ‘Data related to credit risk 2008’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/data-related-to-credit-risk-2008 on 12 November 2021.

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

Aggregated statistical data on a key aspect of the implementation of prudential framework in each Member State.

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