This dataset contains information on how much money is spent by verified advertisers on political advertising across Google Ad Services. In addition, insights on demographic targeting used in political ad campaigns by these advertisers are also provided. Finally, links to the actual political ad in the Google Transparency Report are provided. Data for an election expires 7 years after the election. After this point, the data are removed from the dataset and are no longer available. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

This dataset provides a curated subset of the anonymized Google Analytics event data for three months of the Google Merchandise Store. The full dataset is available as a BigQuery Public Dataset.

The data includes information on items sold in the store and how much money was spent by users over time. It is both comprehensive enough to invite real analysis yet simple enough to facilitate teaching.

Original Data

Acknowledgement

Foto von Arthur Osipyan auf Unsplash

📹Project Video available on YouTube - https://youtu.be/WmJYHz_qn5s

Realistic Synthetic - AI Financial & Market Data for Gemini(Google), ChatGPT(OpenAI), Llama(Meta)

This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI). There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc. Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating. The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

These are the main Features/Columns available in the dataset :

1) Date: This column indicates the specific calendar day for which the financial and AI-related data is recorded. It allows for time-series analysis of the trends and impacts.

2) Company: This column specifies the name of the company to which the data in that particular row belongs. Examples include "OpenAI" and "Meta".

3) R&D_Spending_USD_Mn: This column represents the Research and Development (R&D) spending of the company, measured in Millions of USD. It serves as an indicator of a company's investment in innovation and future growth, particularly in the AI sector.

4) AI_Revenue_USD_Mn: This column denotes the revenue generated specifically from AI-related products or services, also measured in Millions of USD. This metric highlights the direct financial success derived from AI initiatives.

5) AI_Revenue_Growth_%: This column shows the percentage growth of AI-related revenue for the company on a daily basis. It indicates the pace at which a company's AI business is expanding or contracting.

6) Event: This column captures any significant events or announcements made by the company that could potentially influence its financial performance or market perception. Examples include "Cloud AI launch," "AI partnership deal," "AI ethics policy update," and "AI speech recognition release." These events are crucial for understanding sudden shifts in stock impact.

7) Stock_Impact_%: This column quantifies the percentage change in the company's stock price on a given day, likely in response to the recorded financial metrics or events. It serves as a direct measure of market reaction.

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Dowiedz się więcej

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

The dataset consists of 113 responses directly taken from a Google Form survey consisting of four demographic questions (age, sex, country, income), a single item on Love for Money, and WHO-5 Wellbeing Questionnaire. This is a completely raw , anonymous dataset. This data was collected as part of a study examining the relationship between income and wellbeing mediated/moderated by love for money.

Project is still being worked on.

Initially, this dataset was just for a Google Data Analytics project, where I was given a task to accomplish with the data in a spreadsheet: look at the table given in the spreadsheet, and see if there's a correlation between temperature and revenue in ice cream sales. Eventually, I did see the pattern: higher temperatures usually meant more revenue, which seems realistic. However, I wanted to dig further into the data and perform a deeper analysis using a visualization, and maybe even a regression. My new questions were, "How strong is this correlation?" and "Can we represent the data using a linear regression?"

With the phone book era far in the past, database and directory publishers have been forced to transform their business approach, focusing on their digital presence. Despite many publishers rapidly moving away from print services, they are experiencing immovable competition from online search engines and social media platforms within the digital space, negatively affecting revenue growth potential. Industry revenue has been eroding at a CAGR of 4.4% over the past five years and in 2024, a 3.9% drop has led to the industry revenue totaling $4.4 billion. Profit continues to drop in line with revenue, accounting for 4.7% of revenue as publishers invest more in their digital platforms. Interest in printed directories has disappeared as institutional clients and consumers have continued their shift to convenient online resources. Declining demand for print advertising has curbed revenue growth and online revenue has only slightly mitigated this downturn. Though many traditional publishers, such as Yellow Pages, now operate under parent companies with digital resources, directory publishers remain low on the list of options businesses have to choose from in digital advertising. Due to the convenience and connectivity that Facebook and Google services offer, traditional directory publishers have a limited ability to compete. Many providers have rebranded and tailored their services toward client needs, though these efforts have only had a marginal impact on revenue growth. The industry is forecast to decline at an accelerated CAGR of 5.2% over the next five years, reaching an estimated $3.4 billion in 2029, as businesses and consumers continually turn to digital alternatives for information and advertising opportunities. As AI and digital technology innovation expands, social media company products will likely improve at a faster rate than the digital offerings that directory publishers can provide. Though these companies will seek external partnerships to cut costs, they face an uphill battle to boost their visibility and reverse consumer habit trends.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

Context

Form 990 (officially, the "Return of Organization Exempt From Income Tax"1) is a United States Internal Revenue Service form that provides the public with financial information about a nonprofit organization. It is often the only source of such information. It is also used by government agencies to prevent organizations from abusing their tax-exempt status. Source: https://en.wikipedia.org/wiki/Form_990

Content

Form 990 is used by the United States Internal Revenue Service to gather financial information about nonprofit/exempt organizations. This BigQuery dataset can be used to perform research and analysis of organizations that have electronically filed Forms 990, 990-EZ and 990-PF. For a complete description of data variables available in this dataset, see the IRS’s extract documentation: https://www.irs.gov/uac/soi-tax-stats-annual-extract-of-tax-exempt-organization-financial-data.

Update Frequency: Annual

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:irs_990

https://cloud.google.com/bigquery/public-data/irs-990

Dataset Source: U.S. Internal Revenue Service. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @rawpixel from Unplash.

Inspiration

What organizations filed tax exempt status in 2015?

What was the revenue of the American Red Cross in 2017?

Bitcoin Cash is a cryptocurrency that allows more bytes to be included in each block relative to it’s common ancestor Bitcoin. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? 자세히 알아보기

Exempt organization information is extracted monthly from the Internal Revenue Service’s Business Master File. This is a cumulative file, and the data are the most recent information the IRS has for these organizations.

If you have any questions about the tax-exempt organizations or the content of the files, please contact TE/GE Customer Account Services toll-free line at 1-877-829-5500

State is determined from the filing address and generally represent the location of an organization’s headquarters, which may or may not represent the state(s) in which an organization has operations.

Records are sorted by Employer Identification Number (EIN).

This dataset is for Connecticut only.

The IRS exempt organization data have been accumulated since the inception of the tax-exempt statutes. A determination letter is issued to an organization upon the granting of an exemption and is considered valid throughout the life of the organization, as long as the organization complies with the provisions of its exemption. If an organization's exemption is revoked, an announcement to inform potential donors of the revocation is published in the Internal Revenue Bulletin. In addition, the organization’s name is removed from publicly accessible venues, including this file.

Updated nightly.

NOTE: Split-interest trusts are not included in this database.

FIELDS AVAILABLE All exempt organization records on this file will contain the following data fields: Column Name Contents EIN Employer Identification Number (EIN) NAME Primary Name of Organization ICO In Care of Name STREET Street Address CITY City STATE State ZIP Zip Code GROUP Group Exemption Number SUBSECTION Subsection Code AFFILIATION Affiliation Code CLASSIFICATION Classification Code(s) RULING Ruling Date DEDUCTIBILITY Deductibility Code FOUNDATION Foundation Code ACTIVITY Activity Codes ORGANIZATION Organization Code STATUS Exempt Organization Status Code TAX_PERIOD Tax Period ASSET_CD Asset Code INCOME_CD Income Code FILING_REQ_CD Filing Requirement Code PF_FILING_REQ_CD PF Filing Requirement Code ACCT_PD Accounting Period ASSET_AMT Asset Amount INCOME_AMT Income Amount (includes negative sign if amount is negative) REVENUE_AMT Form 990 Revenue Amount (includes negative sign if amount is negative) NTEE_CD National Taxonomy of Exempt Entities (NTEE) Code SORT_NAME Sort Name (Secondary Name Line)

Firmographic Data for Company Intelligence, B2B Segmentation & KYB Firmographic data is the backbone of modern B2B decision-making, powering everything from lead scoring and segmentation to compliance, financial benchmarking, and market expansion planning. Canaria’s enriched Firmographic Data product delivers deep visibility into U.S. companies by combining standardized insights on revenue ranges, employee count, and business category with optional location verification through Google Maps metadata.

This clean and analysis-ready firmographic dataset is built for precision. Every record is structured, normalized, and deduplicated to support automated workflows across CRMs, BI dashboards, compliance tools, financial models, and sales platforms. Updated weekly, our firmographic data ensures that teams stay ahead of organizational shifts, while providing the matchability and granularity required to fuel market intelligence at scale.

If you're working with fragmented company information, incomplete lead lists, or outdated third-party data, Canaria’s Firmographic Data bridges the gap between surface-level signals and operational insight.

Use Cases: What This Firmographic Data Solves Canaria’s firmographic data offering is used by sales, risk, finance, compliance, and strategy teams to strengthen daily operations, strategic planning, and automation initiatives.

Company Analysis • Leverage firmographic data to assess a company’s size, structure, and potential impact within its industry • Identify organizational tiers using clean employee size brackets, location counts, and business hierarchy insights • Analyze firmographic profiles at the branch level, matched with Google Maps data to verify presence, operating hours, and reviews • Map the operational footprint of enterprises across ZIP codes, cities, and regions for trend tracking or competitive benchmarking

Know Your Business (KYB) & Regulatory Compliance • Use firmographic signals such as company type, headquarters address, incorporation location, and estimated size for KYB verification • Identify shell entities or mismatched records using cross-source validation with Google Maps-matched firmographic data • Flag risk-prone entities based on abnormal size-revenue-industry patterns or gaps in metadata • Enhance onboarding pipelines and due diligence platforms by auto-enriching firmographic gaps at scale • Comply with local and international KYB regulations with standardized firmographic data structures

Financial Intelligence & Private Market Benchmarking • Use estimated firmographic variables like annual revenue range, employee count, and industry focus to model private market behavior • Benchmark companies against similar-sized peers within the same vertical, region, or revenue bracket • Replace missing financials with proxy signals from enriched firmographic datasets for internal modeling and client analysis • Feed investor signals and fund models with data on size trends, regional density, and revenue tier shifts • Correlate firmographic data with job postings, hiring behavior, and sentiment for growth prediction models

Market Research, TAM/SAM Modeling & Industry Intelligence • Conduct high-resolution market mapping by combining industry codes, company counts, and firm size across specific geographies • Map sector saturation and whitespace using city, ZIP code, or state-level firmographic intelligence • Analyze shifts in vertical presence, workforce concentration, and mid-market vs. enterprise distribution • Tailor customer segmentation models using clean and consistent firmographic fields • Build TAM/SAM datasets using industry, employee size, revenue tier, and location granularity

B2B Lead Generation & RevOps Segmentation • Score and segment inbound leads using enriched firmographic attributes such as company size, region, industry, and revenue • Eliminate low-value or unqualified leads from prospecting databases by applying firmographic filters • Route leads to the right sales reps or vertical pods based on company headcount, location, and category • Enrich lead records automatically with up-to-date firmographic data pulled from verified external sources • Build ABM lists using revenue-based tiers, industry verticals, and mapped branch data via Google Maps enrichment

What Makes This Firmographic Data Unique Deep Enrichment with Verified Firmographic Attributes • Our firmographic data includes revenue range, employee size bracket, industry classification, company type, and regional identifiers — all normalized to enable aggregation, filtering, and modeling.

Matchable with Google Maps for Accuracy and Context • Match your firmographic records with Google Maps to verify physical branch presence, exact addresses, latitude/longitude, phone numbers, and ratings. This adds a real-world signal layer to abstract company data and supports KYB, lead scoring, and risk assessment.

Continuously Updated and Scalable • Weekly refreshes ensure your firmographi...

This data package includes the underlying data to replicate the charts, tables, and calculations presented in The US Revenue Implications of President Trump’s 2025 Tariffs, PIIE Briefing 25-2.

If you use the data, please cite as:

McKibbin, Warwick, and Geoffrey Shuetrim. 2025. The US Revenue Implications of President Trump’s 2025 Tariffs. PIIE Briefing 25-2. Washington: Peterson Institute for International Economics.

Dataset of historical annual gold prices from 1970 to 2024, including significant events and acts that impacted gold prices.

On December 16, 2021 the IRS announced that it would discontinue updates to the IRS 990 Filings dataset on AWS, starting December 31, 2021.The IRS has requested public inquiries be directed to +1-800-829-1040.Machine-readable data from certain electronic 990 forms filed with the IRS from 2013 to present.

The US data processing and hosting services industry is navigating a dynamic environment marked by rising demands and revolutionary trends. As digitalization accelerates, data centers have evolved from simple infrastructure to essential strategic assets. These hubs now power services ranging from cloud computing to advanced data analytics. In 2025, the data processing and hosting service market includes giants like Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). Industry revenue currently sits at $383.8 billion, growing robustly at a CAGR of 9.2% over the past five years, including a 6.2% surge in 2025 alone. Alongside leading tech firms, smaller specialized providers cater to sectors like healthcare, financial services and government agencies with precision-placed data storage solutions. Emerging trends significantly influence the evolution of the US data processing and hosting services industry. Prominent among these is edge computing, a decentralized approach that locates data centers closer to end-user devices. Along with AI and modern data centers, these innovations aim to reduce latency and enhance application performance by minimizing resource usage in data transmission, thereby promoting broader adoption of cloud computing. Despite this transformative growth, the US data processing and hosting services industry faces significant hurdles, including a skill gap, escalating energy costs and escalating cybersecurity threats. This scarcity has heightened the focus on software automation, leading many facilities to implement AI solutions. Though offshoring trends lead to lost business for many participants, this activity is limited and the industry still benefits from strong demand, leading to rising profit. The industry is projected to grow at a CAGR of 2.4% to $431.4 billion by 2030. The future holds a mix of challenges and opportunities for the industry. Strategic investments in human capital and advanced technologies will distinguish industry leaders from laggards. Compliance with evolving data sovereignty and privacy regulations will determine local market competitiveness. Continuous innovation is expected to drive this progress, solidifying data centers' roles as pivotal components shaping the digital landscape ahead.

The app analytics market, valued at $7.29 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 21.09% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of mobile applications across diverse industries, coupled with the rising need for businesses to understand user behavior and optimize app performance, fuels the demand for sophisticated analytics solutions. Furthermore, advancements in data analytics technologies, including artificial intelligence (AI) and machine learning (ML), are enabling more insightful and actionable data analysis, further propelling market expansion. The diverse application of app analytics across marketing/advertising, revenue generation, and in-app performance monitoring across various sectors like BFSI, e-commerce, media, travel and tourism, and IT and telecom significantly contributes to this growth. The market is segmented by deployment (mobile apps and website/desktop apps) and end-user industry, with mobile app analytics currently dominating due to the widespread adoption of smartphones. The competitive landscape is characterized by a mix of established technology giants like Google and Amazon alongside specialized app analytics providers like AppsFlyer and Mixpanel. These companies are continuously innovating, integrating new technologies, and expanding their product offerings to cater to the evolving needs of businesses. While the North American market currently holds a significant share, the Asia-Pacific region is expected to witness substantial growth in the coming years driven by increasing smartphone penetration and digitalization initiatives. However, factors like data privacy concerns and the rising complexity of integrating various analytics tools could pose challenges to market growth. Nonetheless, the overall outlook for the app analytics market remains positive, indicating substantial opportunities for players across the value chain. Recent developments include: June 2024 - Comscore and Kochava unveiled an innovative performance media measurement solution, providing marketers with enhanced insights. This cutting-edge cross-screen solution empowers marketers to understand better how linear TV ad campaigns impact both online and offline actions. By integrating Comscore’s Exact Commercial Ratings (ECR) data with Kochava’s sophisticated marketing mix modeling, the solution facilitates the measurement of crucial metrics, including mobile app activities (such as installs and in-app purchases) and website interactions., June 2024 - AppsFlyer announced its integration of the Data Collaboration Platform with Start.io, an omnichannel advertising platform that focuses on real-time mobile audiences for publishers. Through this collaboration, businesses leveraging the AppsFlyer Data Collaboration Platform can merge their Start.io data with campaign metrics and audience insights, creating a more comprehensive dataset for precise audience targeting.. Key drivers for this market are: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Potential restraints include: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Notable trends are: Media and Entertainment Industry Expected to Capture Significant Share.

Cloud Computing Market Growth | Industry Analysis, Size & Forecast Report

Dataset updated: Jun 27, 2024

Dataset authored and provided by: Mordor Intelligence

License: https://www.mordorintelligence.com/privacy-policy

Time period covered: 2019 - 2029

Area covered: Global

Variables measured: CAGR, Market size, Market share analysis, Global trends, Industry forecast

Description: The Cloud Computing Market size is estimated at USD 0.68 trillion in 2024, and is expected to reach USD 1.44 trillion by 2029, growing at a CAGR of 16.40% during the forecast period (2024-2029).

Report Attribute

Quantitative Units: Revenue in USD Billion, Volumes in Units, Pricing in USD

Regions and Countries Covered:

Industry Segmentation Covered:

By Cloud Computing: IaaS, SaaS, PaaS

By End-User: IT and Telecom, BFSI, Retail and Consumer Goods, Manufacturing, Healthcare, Media and Entertainment

Market Players Covered: Amazon Web Services, Google LLC, Microsoft Corporation, Alibaba Cloud, and Salesforce