This dataset contains fictional reviews from a hypothetical mobile application, generated for demo purposes in various projects. The reviews include detailed feedback from users across different countries and platforms, with additional attributes such as star ratings, like/dislike counts, and issue flags. The data was later used as an input for a large language model (LLM) to generate labeled outputs, which are included in a separate dataset named labeled_app_store_reviews. This labeled dataset can be used for machine learning tasks such as sentiment analysis, text classification, or even A/B testing simulations.

Unlock valuable insights with the Google Play Store Android Apps Dataset in CSV format, featuring detailed information on over thousands of Android apps available on the Google Play Store. This comprehensive dataset includes key attributes such as App Name, App Logo, Category, Description, Average Rating, Ratings Count, In-app Purchases, Operating System, Company, Content Rating, Images, Email, Additional Information, and more.

Perfect for market researchers, data scientists, app developers, and analysts, this dataset allows for deep analysis of app performance, user preferences, and industry trends. With data on app descriptions, content ratings, in-app purchases, and company information, you can track trends in the mobile app market, evaluate user satisfaction, and conduct competitive analysis.

The dataset is ideal for businesses looking to optimize app strategies, enhance user experience, and improve app performance based on real user feedback. Easily import the data into your favorite analysis tools to gain actionable insights for your app development or research.

With regularly updated data scraped directly from the Google Play Store, the Google Play Store Android Apps Dataset is an invaluable resource for anyone looking to explore trends, track performance, or enhance their app strategies.

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

Unlock the power of user feedback with our iOS App Store Reviews Dataset, a comprehensive collection of reviews from thousands of apps across various categories. This robust App Store dataset includes essential details such as app names, ratings, user comments, timestamps, and more, offering valuable insights into user experiences and preferences.

Perfect for app developers, marketers, and data analysts, this dataset allows you to conduct sentiment analysis, monitor app performance, and identify trends in user behavior. By leveraging the iOS App Store Reviews Dataset, you can refine app features, optimize marketing strategies, and elevate user satisfaction.

Whether you’re tracking mobile app trends, analyzing specific app categories, or developing data-driven strategies, this App Store dataset is an indispensable tool. Download the iOS App Store Reviews Dataset today or contact us for custom datasets tailored to your unique project requirements.

Ready to take your app insights to the next level? Get the iOS App Store Reviews Dataset now or explore our custom data solutions to meet your needs.

This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

Dataset Specifications:

• Investment: $45.0
• Status: Published and immediately available.
• Category: Ratings and Reviews Data
• Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
• Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
• Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

Richness of Detail (11 Comprehensive Fields):

Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

1. Review Content:

   • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
   • title: The title given to the review by the user, often summarizing their main point.
   • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.

2. Reviewer & Rating Information:

   • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
   • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.

3. App & Origin Context:

   • app_name: The name of the application being reviewed.
   • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
   • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.

4. Metadata & Timestamps:

   • _id: A unique identifier for the specific review record in the dataset.
   • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
   • date: The original date the review was posted by the user on the App Store.

Expanded Use Cases & Analytical Applications:

This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

• Product Development & Improvement:

  • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
  • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
  • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
  • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• Market Research & Competitive Intelligence:

  • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
  • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
  • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.

• Marketing & App Store Optimization (ASO):

  • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
  • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
  • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
  • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
  • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
  • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

This is a fictional dataset created for the purpose of learning and practicing data analysis, visualization, and machine learning.

It includes details of 300 fictional mobile apps with fields like:

App Name

Category

Rating

Reviews Count

App Size (MB)

Installs

Price (USD)

This dataset is ideal for beginners who want to:

Practice exploratory data analysis (EDA)

Build dashboards and visualizations

Train simple ML models on app performance

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.

This comprehensive iOS application reviews dataset contains thousands of authentic user reviews from the Apple App Store in English. The dataset provides valuable insights for app developers, marketers, and researchers studying mobile application performance and user sentiment.

Key Features:

• Real user reviews from popular iOS apps
• Star ratings from 1 to 5 stars
• Review dates and timestamps
• App store URLs and metadata
• User demographics and location data
• App version information
• Review titles and detailed feedback

Applications: Perfect for sentiment analysis, app store optimization, mobile app development research, user experience studies, and competitive analysis. This dataset enables businesses to understand user preferences, identify app improvement opportunities, and develop better mobile applications.

Data Quality: All reviews are genuine user feedback collected from the official Apple App Store, ensuring authenticity and reliability for research and business intelligence purposes. The dataset covers various app categories including fitness, shopping, education, entertainment, and productivity applications.

Description:

• The dataset comprises anonymized data on apps available on the Google Play Store, capturing various aspects such as ratings, downloads, and categorization.
• The dataset has 10,841 entries, with some columns containing missing values, particularly in "Rating," "Type," "Content Rating," "Current Ver," and "Android Ver".
• This dataset can be utilized for analyzing trends in mobile app usage, user preferences, and app performance metrics across different categories.
• It can aid in understanding the impact of factors like app size, rating, and category on user downloads and popularity.
• This dataset can be utilized for analyzing app popularity, user preferences, and the relationship between app features (e.g., size, price) and downloads.
• It can help in identifying trends in app categories, assessing user satisfaction through ratings and reviews, and providing insights for app developers and marketers on user engagement and app performance.

Features:

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, few counterpart datasets are available for Google Play Store apps anywhere on the web. On digging deeper, I discovered that the iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

• There are 13 features in the dataset, and each feature indicates some details of Google application name, category, rating, reviews, size, installs, type, price, content rating genres, last updated, current version and Android version.
• App: The application name.
• Category: The category the app belongs to.
• Rating: Overall user rating of the app.
• Reviews: Number of user reviews for the app.
• Size: The size of the app.
• Installs: Number of user installs for the app.
• Type: Either "Paid" or "Free".
• Price: The price of the app.
• Content Rating: The age group the app is targeted at - "Children" / "Mature 21+" / "Adult".
• Genres: Possibly multiple genres the app belongs to.
• Last Updated: The date the app was last updated.
• Current Ver: The current version of the app.
• Android Ver: The Android version is needed for this app.

Video Dataset Market Outlook

Based on our latest research, the global video dataset market size reached USD 2.1 billion in 2024 and is projected to grow at a robust CAGR of 19.7% during the forecast period, reaching a value of USD 10.3 billion by 2033. This remarkable growth trajectory is driven by the increasing adoption of artificial intelligence and machine learning technologies, which heavily rely on high-quality video datasets for training and validation purposes. As organizations across industries seek to leverage advanced analytics and automation, the demand for comprehensive, well-annotated video datasets is accelerating rapidly, establishing the video dataset market as a critical enabler for next-generation digital solutions.

One of the primary growth factors propelling the video dataset market is the exponential rise in the deployment of computer vision applications across diverse sectors. Industries such as automotive, healthcare, retail, and security are increasingly integrating AI-powered vision systems for tasks ranging from autonomous navigation and medical diagnostics to customer behavior analysis and surveillance. The effectiveness of these systems hinges on the availability of large, diverse, and accurately labeled video datasets that can be used to train robust machine learning models. With the proliferation of video-enabled devices and sensors, the volume of raw video data has surged, further fueling the need for curated datasets that can be harnessed to unlock actionable insights and drive automation.

Another significant driver for the video dataset market is the growing emphasis on data-driven research and innovation within academic, commercial, and governmental institutions. Universities and research organizations are leveraging video datasets to advance studies in areas such as robotics, behavioral science, and smart city development. Similarly, commercial entities are utilizing these datasets to enhance product offerings, improve customer experiences, and gain a competitive edge through AI-driven solutions. Government and defense agencies are also investing in video datasets to bolster national security, surveillance, and public safety initiatives. This broad-based adoption across end-users is catalyzing the expansion of the video dataset market, as stakeholders recognize the strategic value of high-quality video data in driving technological progress and operational efficiency.

The emergence of synthetic and augmented video datasets represents a transformative trend within the market, addressing challenges related to data scarcity, privacy, and bias. Synthetic datasets, generated using advanced simulation and generative AI techniques, enable organizations to create vast amounts of labeled video data tailored to specific scenarios without the need for extensive real-world data collection. This approach not only accelerates model development but also enhances data diversity and mitigates ethical concerns associated with using sensitive or personally identifiable information. As the technology for generating and validating synthetic video data matures, its adoption is expected to further accelerate, opening new avenues for innovation and market growth.

Regionally, North America continues to dominate the video dataset market, accounting for the largest share in 2024 due to its advanced technological ecosystem, strong presence of leading AI companies, and substantial investments in research and development. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid digital transformation, increasing adoption of AI in sectors like manufacturing and healthcare, and supportive government policies. Europe also represents a significant market, characterized by its focus on data privacy and regulatory compliance, which is shaping the development and utilization of video datasets across industries. These regional dynamics underscore the global nature of the video dataset market and highlight the diverse opportunities for stakeholders worldwide.

Dataset Type Analysis

The video dataset market is segmented by dataset type into labeled, unlabeled, and synthetic datasets, each serving distinct purposes and addressing unique industry requirements. Labeled video datasets are foundational for supervised learning applications, where annotated frames and sequences enable machine learning models to learn complex patterns and behaviors. The demand for labeled datasets is particularly high in sectors

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

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AI Training Dataset Market Size 2025-2029

The ai training dataset market size is valued to increase by USD 7.33 billion, at a CAGR of 29% from 2024 to 2029. Proliferation and increasing complexity of foundational AI models will drive the ai training dataset market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - Text segment was valued at USD 742.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 479.81 million 
Market Future Opportunities 2024: USD 7334.90 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on artificial intelligence (AI) to optimize operations, enhance customer experiences, and drive innovation. The proliferation and increasing complexity of foundational AI models necessitate large, high-quality datasets for effective training and improvement. This shift from data quantity to data quality and curation is a key trend in the market. Navigating data privacy, security, and copyright complexities, however, poses a significant challenge. Businesses must ensure that their datasets are ethically sourced, anonymized, and securely stored to mitigate risks and maintain compliance. For instance, in the supply chain optimization sector, companies use AI models to predict demand, optimize inventory levels, and improve logistics. Access to accurate and up-to-date training datasets is essential for these applications to function efficiently and effectively. Despite these challenges, the benefits of AI and the need for high-quality training datasets continue to drive market growth. The potential applications of AI are vast and varied, from healthcare and finance to manufacturing and transportation. As businesses continue to explore the possibilities of AI, the demand for curated, reliable, and secure training datasets will only increase.

What will be the size of the AI Training Dataset Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with businesses increasingly recognizing the importance of high-quality datasets for developing and refining artificial intelligence models. According to recent studies, the use of AI in various industries is projected to grow by over 40% in the next five years, creating a significant demand for training datasets. This trend is particularly relevant for boardrooms, as companies grapple with compliance requirements, budgeting decisions, and product strategy. Moreover, the importance of data labeling, feature selection, and imbalanced data handling in model performance cannot be overstated. For instance, a mislabeled dataset can lead to biased and inaccurate models, potentially resulting in costly errors. Similarly, effective feature selection algorithms can significantly improve model accuracy and reduce computational resources. Despite these challenges, advances in model compression methods, dataset scalability, and data lineage tracking are helping to address some of the most pressing issues in the market. For example, model compression techniques can reduce the size of models, making them more efficient and easier to deploy. Similarly, data lineage tracking can help ensure data consistency and improve model interpretability. In conclusion, the market is a critical component of the broader AI ecosystem, with significant implications for businesses across industries. By focusing on data quality, effective labeling, and advanced techniques for handling imbalanced data and improving model performance, organizations can stay ahead of the curve and unlock the full potential of AI.

Unpacking the AI Training Dataset Market Landscape

In the realm of artificial intelligence (AI), the significance of high-quality training datasets is indisputable. Businesses harnessing AI technologies invest substantially in acquiring and managing these datasets to ensure model robustness and accuracy. According to recent studies, up to 80% of machine learning projects fail due to insufficient or poor-quality data. Conversely, organizations that effectively manage their training data experience an average ROI improvement of 15% through cost reduction and enhanced model performance.

Distributed computing systems and high-performance computing facilitate the processing of vast datasets, enabling businesses to train models at scale. Data security protocols and privacy preservation techniques are crucial to protect sensitive information within these datasets. Reinforcement learning models and supervised learning models each have their unique applications, with the former demonstrating a 30% faster convergence rate in certain use cases.

Data annot

Install App dataset provides comprehensive, first-party app install intelligence across the APAC region, sourced from AI-driven OS-level keyboard and utility applications. It captures highly granular insights into mobile app installations, updates, and user behavior, enabling precise market analytics, attribution tracking, and growth optimization.

Each record includes hashed device and advertising identifiers, application metadata (package name, app version, category), and timestamped install/update events. The field is_new_install indicates whether the app installation is first-time or an existing reinstall/update, helping distinguish between new user acquisition and returning user activity — a critical signal for campaign performance and user lifecycle analytics.

Alongside app-level insights, the dataset provides detailed device intelligence — including manufacturer, model, OS type/version, language, and user agent — combined with IP-based location data (country, region, city) and daily server timestamps for freshness tracking.

All data is hashed, privacy-compliant, and refreshed daily, making it ideal for organizations seeking high-quality, real-world app install signals across Android and iOS ecosystems.

📊 Key Features • First-party, consented data from OS-level applications • Hashed identifiers (device_id, advertising_id) for privacy-safe integration • Install and update timestamps for temporal and behavioral analysis • is_new_install flag to separate new installs from reinstalls or app updates • Comprehensive app, device, and location attributes • Daily refreshed dataset ensuring data accuracy and timeliness

⚙️ Primary Use Cases • Mobile Attribution & User Acquisition Tracking – Identify new users vs. re-engaged ones via the is_new_install flag • Market Intelligence & Competitive Benchmarking – Analyze install trends across app categories and geographies • Audience Segmentation – Classify users by device type, OS version, and app install behavior • Ad Targeting Optimization – Refine lookalike and re-engagement audiences with verified install data • Product & Growth Analytics – Study retention, uninstall rates, and user churn patterns • App Store Strategy – Evaluate app update frequency and version distribution

📍 Industries Benefiting • Ad-Tech & Mar-Tech Platforms • Mobile App Publishers & Developers • Telecom Operators & Device OEMs • Market Research & Analytics Firms • E-commerce, Fintech & Gaming Companies • Media, Entertainment & OTT Platforms

With millions of verified app installs tracked across Android and iOS, this AI-powered, consent-based dataset delivers actionable insights into app discovery, engagement, and retention, driving smarter decisions in mobile marketing, audience intelligence, and growth analytics.

This dataset comprises 10,000 user reviews of the BCA Mobile app collected from the Google Play Store between December 24, 2023, and June 12, 2024. Each review includes the user's name, the rating they provided (ranging from 1 to 5 stars), the timestamp of when the review was created, and the text content of the review. The dataset is in Indonesian and focuses on feedback from users in Indonesia. This data can be used to perform sentiment analysis, understand user experiences, identify common issues, and assess the overall performance of the BCA Mobile app during the specified timeframe. The reviews are sorted based on the newest first, providing the latest feedback at the top.

Context

Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. We'll look for insights in the data to devise strategies to drive growth and retention.

AI Dataset Management Market Outlook

According to our latest research, the AI Dataset Management market size reached USD 1.82 billion in 2024, reflecting robust momentum driven by the increasing adoption of artificial intelligence across diverse industries. The market is projected to grow at a CAGR of 27.6% from 2025 to 2033, reaching a forecasted value of USD 14.35 billion by 2033. This rapid expansion is propelled by the surging need for high-quality, well-managed datasets to fuel AI and machine learning models, coupled with the proliferation of data-intensive applications in sectors such as healthcare, finance, and retail. As per our latest research, the market’s upward trajectory is further supported by advancements in data labeling, annotation tools, and stringent regulatory requirements for data governance.

One of the primary growth factors for the AI Dataset Management market is the exponential increase in data generation from connected devices, social media platforms, IoT sensors, and enterprise applications. Organizations are increasingly recognizing that the quality and integrity of their AI models are directly tied to the quality of the underlying datasets. As a result, there is a growing demand for sophisticated dataset management solutions that can automate data collection, cleansing, labeling, and augmentation. These solutions not only streamline the AI development lifecycle but also ensure compliance with evolving data privacy regulations such as GDPR and CCPA. Furthermore, the integration of advanced technologies like natural language processing and computer vision into dataset management platforms is enhancing their ability to handle complex, unstructured data, further stimulating market growth.

Another significant driver is the expanding application of AI across verticals such as healthcare, BFSI, retail, automotive, and government. In healthcare, for instance, the need for annotated medical images and patient records is spurring investment in specialized dataset management tools. Similarly, financial institutions are leveraging AI dataset management to detect fraud, manage risk, and personalize customer experiences. The retail and e-commerce sector is utilizing these solutions for customer segmentation, demand forecasting, and inventory optimization. This cross-industry adoption is creating a fertile environment for both established players and innovative startups to introduce tailored offerings that address the unique data challenges of each sector. As a result, the market is witnessing a wave of product innovation, strategic partnerships, and mergers and acquisitions aimed at expanding capabilities and geographic reach.

Additionally, the shift towards cloud-based deployment models is accelerating the adoption of AI dataset management solutions, especially among small and medium enterprises (SMEs) that require scalable, cost-effective tools. Cloud platforms offer the flexibility to store, process, and manage large volumes of data without significant upfront investment in IT infrastructure. This democratization of AI dataset management is leveling the playing field, enabling organizations of all sizes to harness the power of AI for competitive advantage. Moreover, the emergence of open-source dataset management frameworks and APIs is lowering barriers to entry, fostering a vibrant ecosystem of developers, researchers, and data scientists. These trends are expected to sustain the market’s double-digit growth over the forecast period.

Regionally, North America continues to dominate the AI Dataset Management market, accounting for the largest revenue share in 2024, thanks to its advanced digital infrastructure, high AI adoption rates, and concentration of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, government initiatives supporting AI research, and a burgeoning base of tech-savvy enterprises. Europe is also making significant strides, particularly in sectors such as automotive and healthcare, where stringent data protection regulations are fueling demand for robust dataset management solutions. Latin America and the Middle East & Africa are gradually catching up, with increasing investments in AI and digitalization initiatives. Overall, the regional outlook remains highly optimistic, with each geography presenting unique growth opportunities and challenges for market participants.

Component Analysis

The AI Dataset

NoSQL Database Market size was valued at USD 6.47 Billion in 2024 and is expected to reach USD 44.66 Billion by 2032, growing at a CAGR of 30.14% from 2026 to 2032.Global NoSQL Database Market DriversExponential Growth of Big Data and IoT: The explosion of Big Data and Internet of Things (IoT) applications is a primary catalyst for NoSQL adoption, requiring database solutions that can ingest and process colossal volumes of unstructured and semi-structured data from diverse sources like sensors, social media, and web logs. Unlike rigid relational systems, Increasing Demand for Real-Time Web and Mobile Applications: The surging demand for real-time web and mobile applications is significantly fueling the NoSQL market, as these modern applications require sub-millisecond latency and exceptionally high throughput to deliver a seamless user experience. NoSQL database types, particularly key-value stores and document databases, are architecturally optimized for rapid read/write operations and horizontal scaling,.