Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Context Google Analytics 4 is Google analytics latest service that enables you to measure traffic and engagement across your website as well as app.

Content This is a sample dataset of GA4 for Google Merchendise store for the month of Jan 2021.

Acknowledgements This information is exported from the google bigquery public data set.

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:ga4_obfuscated_sample_ecommerce.events_*

Inspiration To study google analytics it is really very difficult to get sample data so here's making it easy for some in need of one.

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.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

Dataset

This dataset was created by Tignangshu Chatterjee

Contents

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

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We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
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This is a simple sample dataset for individuals to work around with in the process of learning data visualizations.

Comparison of user, site, and network-centric approaches to web analytics data collection showing advantages, disadvantages, and examples of each approach at the time of the study.

Envestnet®| Yodlee®'s Retail Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Web Analytics Market Outlook

According to our latest research, the global web analytics market size was valued at USD 8.4 billion in 2024, reflecting robust growth driven by the increasing adoption of digital platforms across industries. The market is projected to expand at a compound annual growth rate (CAGR) of 17.2% from 2025 to 2033, reaching an estimated USD 36.8 billion by 2033. This significant upsurge is primarily attributed to the escalating demand for actionable insights, data-driven decision-making, and the proliferation of online consumer activity. As per the latest research, enterprises worldwide are leveraging advanced web analytics tools to enhance customer engagement, improve marketing strategies, and drive business outcomes.

One of the principal growth factors fueling the web analytics market is the exponential increase in digitalization and internet penetration. Organizations across various sectors are rapidly transitioning their operations online, resulting in a surge of data generation through multiple digital touchpoints. This digital transformation has heightened the need for sophisticated web analytics solutions that can process vast volumes of data, extract meaningful patterns, and provide actionable insights. Moreover, the rise in e-commerce activities, coupled with the growing popularity of social media platforms, has created a fertile environment for the adoption of web analytics, enabling businesses to track consumer behavior, measure campaign effectiveness, and optimize user experiences.

Another critical driver for the web analytics market is the integration of artificial intelligence (AI) and machine learning (ML) technologies. These advanced technologies are revolutionizing the way organizations analyze web data by enabling predictive analytics, real-time reporting, and personalized recommendations. AI-powered web analytics tools can automatically identify trends, anomalies, and customer preferences, empowering businesses to make data-driven decisions faster and more accurately. Furthermore, the increasing focus on omnichannel marketing strategies and the need to unify customer data across different platforms have further accelerated the demand for comprehensive web analytics solutions.

The regulatory landscape and growing emphasis on data privacy and compliance are also shaping the web analytics market. With the implementation of stringent data protection regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are compelled to adopt web analytics tools that ensure data security and privacy. This has led to the development of privacy-centric analytics platforms that offer enhanced data governance features, enabling businesses to comply with global regulatory requirements while still deriving valuable insights from web data. The ability to balance data-driven innovation with privacy considerations is becoming a key differentiator for vendors in this dynamic market.

From a regional perspective, North America continues to dominate the web analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to the presence of major technology providers, a mature digital ecosystem, and high levels of investment in analytics infrastructure. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by the rapid adoption of digital technologies, expanding internet user base, and increasing investments in e-commerce and digital marketing. The growing awareness among businesses in emerging economies about the benefits of web analytics is further propelling market growth in this region.

Component Analysis

The web analytics market by component is bifurcated into software and services, with each segment playing a pivotal role in market expansion. The software segment holds the lion’s share of the market, driven by the continuous evolution of analytics plat

Analysis of ‘Sample Sales Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kyanyoga/sample-sales-data on 28 January 2022.

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

Sample Sales Data, Order Info, Sales, Customer, Shipping, etc., Used for Segmentation, Customer Analytics, Clustering and More. Inspired for retail analytics. This was originally used for Pentaho DI Kettle, But I found the set could be useful for Sales Simulation training.

Originally Written by María Carina Roldán, Pentaho Community Member, BI consultant (Assert Solutions), Argentina. This work is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License. Modified by Gus Segura June 2014.

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

Xverum’s AI & ML Training Data provides one of the most extensive datasets available for AI and machine learning applications, featuring 800M B2B profiles with 100+ attributes. This dataset is designed to enable AI developers, data scientists, and businesses to train robust and accurate ML models. From natural language processing (NLP) to predictive analytics, our data empowers a wide range of industries and use cases with unparalleled scale, depth, and quality.

What Makes Our Data Unique?

Scale and Coverage: - A global dataset encompassing 800M B2B profiles from a wide array of industries and geographies. - Includes coverage across the Americas, Europe, Asia, and other key markets, ensuring worldwide representation.

Rich Attributes for Training Models: - Over 100 fields of detailed information, including company details, job roles, geographic data, industry categories, past experiences, and behavioral insights. - Tailored for training models in NLP, recommendation systems, and predictive algorithms.

Compliance and Quality: - Fully GDPR and CCPA compliant, providing secure and ethically sourced data. - Extensive data cleaning and validation processes ensure reliability and accuracy.

Annotation-Ready: - Pre-structured and formatted datasets that are easily ingestible into AI workflows. - Ideal for supervised learning with tagging options such as entities, sentiment, or categories.

How Is the Data Sourced? - Publicly available information gathered through advanced, GDPR-compliant web aggregation techniques. - Proprietary enrichment pipelines that validate, clean, and structure raw data into high-quality datasets. This approach ensures we deliver comprehensive, up-to-date, and actionable data for machine learning training.

Primary Use Cases and Verticals

Natural Language Processing (NLP): Train models for named entity recognition (NER), text classification, sentiment analysis, and conversational AI. Ideal for chatbots, language models, and content categorization.

Predictive Analytics and Recommendation Systems: Enable personalized marketing campaigns by predicting buyer behavior. Build smarter recommendation engines for ecommerce and content platforms.

B2B Lead Generation and Market Insights: Create models that identify high-value leads using enriched company and contact information. Develop AI systems that track trends and provide strategic insights for businesses.

HR and Talent Acquisition AI: Optimize talent-matching algorithms using structured job descriptions and candidate profiles. Build AI-powered platforms for recruitment analytics.

How This Product Fits Into Xverum’s Broader Data Offering Xverum is a leading provider of structured, high-quality web datasets. While we specialize in B2B profiles and company data, we also offer complementary datasets tailored for specific verticals, including ecommerce product data, job listings, and customer reviews. The AI Training Data is a natural extension of our core capabilities, bridging the gap between structured data and machine learning workflows. By providing annotation-ready datasets, real-time API access, and customization options, we ensure our clients can seamlessly integrate our data into their AI development processes.

Why Choose Xverum? - Experience and Expertise: A trusted name in structured web data with a proven track record. - Flexibility: Datasets can be tailored for any AI/ML application. - Scalability: With 800M profiles and more being added, you’ll always have access to fresh, up-to-date data. - Compliance: We prioritize data ethics and security, ensuring all data adheres to GDPR and other legal frameworks.

Ready to supercharge your AI and ML projects? Explore Xverum’s AI Training Data to unlock the potential of 800M global B2B profiles. Whether you’re building a chatbot, predictive algorithm, or next-gen AI application, our data is here to help.

Contact us for sample datasets or to discuss your specific needs.

This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal information that captures the timing of student interactions with the Virtual Learning Environment (VLE). By integrating these time-based interactions, the dataset enhances the capabilities of stream algorithms, which are particularly well-suited for real-time monitoring and analysis of student learning behaviors.

The dataset consists of 34 features and 1,718,983 samples, encompassing students' demographic information, assessment scores, and interactions with the VLE for a specific time ( T ), corresponding to each student ( S ) within a given course ( C ) and module ( M ). The target classes—'Withdrawn', 'Fail', 'Pass', and 'Distinction'—were encoded as 0, 1, 2, and 3, respectively. Notably, the data exhibits a significant imbalance, with a substantial prevalence of records associated with students who passed the final examination. The class distribution is as follows: 'Pass' (1,022,760 samples), 'Distinction' (308,642 samples), 'Fail' (227,550$ samples), and 'Withdrawn' (160,031 samples).

For further details on the data, please refer to the manuscript: Gabriella Casalino, Giovanna Castellano, Gianluca Zaza, "Does Time Matter in Analyzing Educational Data? - A New Dataset for Streaming Learning Analytics.", CEUR Proceedings

Dataset

This dataset was created by Navneet Kumar

Contents

Snapshot img

Web Analytics Market Size 2025-2029

The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.

What will be the Size of the Web Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

How is this Web Analytics Industry segmented?

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

Deployment

  Cloud-based
  On-premises


Application

  Social media management
  Targeting and behavioral analysis
  Display advertising optimization
  Multichannel campaign analysis
  Online marketing


Component

  Solutions
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting,

Snapshot img

Crowd Analytics Market Size 2024-2028

The crowd analytics market size is forecast to increase by USD 4.70 billion at a CAGR of 32.14% between 2023 and 2028. The market is experiencing significant growth due to the increasing need for proactive risk management in various sectors, including transportation and public safety. With the rise in domestic crime and potential risks from terrorist acts, there is a growing demand for advanced crowd analytics solutions. Companies are deploying incident analytics software to help identify suspects and prevent incidents before they occur. Data integration and data quality tools are crucial for effective crowd analytics. Hyper-personalization, AI, and automation are key trends in the market, enabling real-time threat detection and response. The manufacturing and retail sectors are major adopters of crowd analytics for client retention and risk management.

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The market is gaining significance in various sectors, including transportation hubs, retail establishments, and large public venues. This technology leverages sensors, cameras, and telecom operators' data to analyze crowd behavior and customer preference patterns in real-time. Airports and train stations are major adopters of crowd analytics. By monitoring foot traffic, queue management, and passenger flow, these organizations can enhance operational efficiency and provide better services. Sensors and cameras installed at these facilities help in tracking crowd behavior and identifying potential bottlenecks, ensuring a smooth passenger experience. City malls and retail stores also benefit from crowd analytics.

Additionally, understanding customer footfall, preferences, and shopping trends can lead to targeted marketing campaigns and improved store layouts. Modern restaurant management is another sector that can benefit from this technology. The National Restaurant Association reports that efficiency gains are a top priority for the restaurant industry. Enhanced video analytics can help in optimizing kitchen workflows, reducing wait times, and improving overall customer experience. Conference centers and stadiums are other significant users of crowd analytics. Public safety and defense organizations also leverage this technology for surveillance purposes. Filmed visuals can be analyzed to identify suspects and maintain security. City planning and public transport are other areas where crowd analytics plays a crucial role.

Further, real-time data on crowd behavior can help in optimizing public transport routes, reducing congestion, and improving overall infrastructure management. Retail malls and commercial buildings also benefit from this technology by ensuring safety and security while providing a seamless customer experience. In conclusion, the market offers numerous benefits to various industries, from transportation and retail to public safety and city planning. By analyzing crowd behavior and customer preferences, organizations can optimize their operations, enhance safety, and improve overall customer experience. Sensors, cameras, and telecom operators' data are the key enablers of this technology, providing valuable insights into crowd dynamics and trends.

Market Segmentation

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

Deployment

  Cloud
  On-premises


End-user

  Transportation
  Retail
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period. The market encompasses the use of business intelligence solutions to analyze and gain insights from the behavior of large groups of people in various public spaces, including airports, train stations, city malls, retail stores, conference centers, and stadiums. The Internet of Things (IoT) adoption and advanced IT infrastructure are key drivers propelling the growth of this market. In terms of deployment models, the cloud holds the largest market share due to its flexibility and accessibility. Cloud-based crowd analytics enables data collection, analysis, storage, and sharing through cloud-based services and applications, making it easier for researchers to collaborate and enhance models and predictions.

The scalability of cloud computing, combined with advanced crowd analytics, is fueling the demand for cloud-based solutions. Crowd flow management and mobility tracking are critical applications of crowd analytics, particularly in transportation and transportation hubs. By analyzing customer preference patterns and trends, businesses can optimize operations, imp

This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1].

The logs are provided in JSON format with the following attributes in the first level:

• id: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1.
• time: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z.
• uid: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer.
• uidType: Specifier for uid, which is either the user name or IP address for logged out users.
• type: The action carried out by the user, e.g., file_accessed.
• params: Additional event parameters (e.g., paths, groups) stored in a nested dictionary.
• isLocalIP: Optional flag for event origin, which is either internal (true) or external (false).
• role: Optional user role: consulting, administration, management, sales, technical, or external.
• location: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc.

In the following data sample, the first object depicts a successful user login (see type: login_successful) and the second object depicts a file access (see type: file_accessed) from a remote location:

{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}

{"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"}

The data set was generated at the premises of Huemer Group, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called hBOX enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project Nextcloud with interfaces and functionalities tailored to the requirements of customers.

The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our github repo.

Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set.

If you use the dataset, please cite the following publication:

[1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. "A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing". 2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022), December 17-20, 2022, Osaka, Japan. IEEE. [PDF]

The googleanalyticsbasic extension for CKAN provides a simple way to integrate Google Analytics tracking into your CKAN-based data catalog. It injects the Google Analytics asynchronous tracking code into the page headers of your CKAN site, enabling basic page view tracking. This allows you to monitor site traffic and user behavior via the Google Analytics dashboard and therefore gain insights into how users interact with your data portal. This extension is compatible with CKAN versions 2.9 and 2.10. Key Features: Easy Google Analytics Integration: Simplifies the process of adding Google Analytics tracking to a CKAN site. You do not need to edit templates or write complex code, as the extension handles the injection of the tracking code. Asynchronous Tracking: Uses the asynchronous Google Analytics tracking code, which is designed to minimize any potential impact on page load times. Configuration via INI File: Enables the setting of Google Analytics tracking IDs via the CKAN configuration file (development.ini or similar). The extension uses a space-separated list of these google ids. Basic Page Tracking: Provides standard page view tracking functionality within Google Analytics. This is suited to monitoring how many hits each CKAN page receives and gives you an overview of site engagement. Compatibility: Supports CKAN versions 2.9 and 2.10. Use Cases: Usage Monitoring: Administrators can track essential metrics such as page views and user visits. Effectiveness Assessment: Evaluating the performance of data portals over time is improved with analytical insights. Informed Decisions: Providing data-driven basis for decisions, such as the prioritization of new features. Technical Integration: The googleanalyticsbasic extension integrates with CKAN by adding the required HTML to CKAN's pages using CKAN's plugin system. You need to activate the plugin in your CKAN configuration file and specify the Google Analytics tracking IDs you want to use. The extension then automatically inserts the tracking code into the appropriate sections of your CKAN pages. This is a simple process that requires no modification of the CKAN core code or installed template files. Benefits & Impact: By implementing the googleanalyticsbasic extension, CKAN site administrators can effortlessly monitor website traffic and user behaviour. This understanding can refine data portal content, improve site usability, and ultimately drive greater data accessibility and user engagement. This monitoring leads to better content development and resource prioritisation across the CKAN catalog.

The global big data and business analytics (BDA) market was valued at ***** billion U.S. dollars in 2018 and is forecast to grow to ***** billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around ** billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. 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. For example, connected IoT devices are projected to generate **** ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around **** billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

Late Payment Index (LPI) helps organisations evaluate prospects and current business partners and their potential risk of future non-payment. Helping businesses to make strategic decisions ahead of time.​

The Late Payment Index is a comprehensive metric developed by Coface to evaluate payment behaviour of companies globally. It reflects the age, frequency, severity of claims, and other vital factors to assess payment difficulties accurately.

Dataset Structure and Components: Sample Size: 15 company records with unique identifiers Assessment Date: All entries dated Product Type: All categorized as "LPI" (Late Payment Index) Evaluation Format: Numeric rating system (0-4) with corresponding descriptive explanations

Rating Classification System: The dataset employs a standardized 0-4 scale to categorize payment behavior:

0: Not available - Assessment data unavailable or insufficient 1: In progress - Assessment currently being conducted 2: Considerable negative experience - Significant payment issues detected 3: Some negative experience - Minor or occasional payment issues detected 4: No negative experience - Clean payment history with no detected issues

Application Context: This sample illustrates how payment behavior can be systematically tracked and categorized to support credit decision-making and business relationship management. The LPI provides an objective metric for evaluating the payment reliability of potential business partners or customers.

This structured assessment system allows organizations to: Identify potential payment risks before entering business relationships Support credit limit decisions with objective payment history data Monitor changing payment behaviors across their business portfolio Create consistent payment evaluation standards across departments

Note: This is sample data intended to demonstrate the structure and capabilities of a payment index system.

Learn More For a complete demonstration of our Late Payment Index capabilities or to discuss how our system can be integrated with your existing processes, please visit https://business-information.coface.com/what-is-urba360 to request additional information.

Content

Improve Time , Cost and Quality of Hire in a random recruitment data. Objective is to Minimize the Time and Cost of Hire and maximize the Quality of Hire metrics.Sample People Analytics project to Mainly used ANOVA, Correlation and Multiple Linear Regression in order to perform the Predictive and Prescriptive Analytics in this Dataset. Dashboards are made in Excel.

Acknowledgements

Kaggle for the sample Dataset (I made modifications to the original Dataset) XLRI for giving me the opportunity to create this project

Inspiration

Inspired by the desire to step into the venture of learning People Analytics.