This dataset is taken from Google Trend. It shows the trend of "Data Science" search term on Google Search Engine and YouTube from 2004 to 2022 (April). There will be an update soon.

Data Catalog Market is Segmented by Component (Solutions and Services), Deployment Mode (Cloud and On-Premise), End-User Industry (BFSI, Retail and E-Commerce, Healthcare, and More), Organization Size (Large Enterprises and Small and Mid-Size Enterprises), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Data as a Service Market is expected to sustain a 30.00% CAGR, reaching USD $125.40 billion by the end of 2032, as per forecasts

The Data Protection Market size is expected to reach a valuation of USD 386.57 billion in 2033 growing at a CAGR of 16.20%. The Data Protection market research report classifies market by share, trend, demand, forecast and based on segmentation.

This repository contains the enrichments for the dataset The New York Times Annotated Corpus developed for the paper:

“Marco Ponza, Diego Ceccarelli, Paolo Ferragina, Edgar Meij, Sambhav Kothari. Contextualizing Trending Entities in News Stories. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021).”

It includes a total of 149 trends constituted by 120K entities. The goal is to retrieve a set of entities ranked with respect to their usefulness in explaining why a given trending entity is actually trending.

Format

The repository contains the enrichments in JSON format.

The news stories of the New York Times from which these enrichments have been developed are available from LDC.

Data Splits

We perform two kinds of evaluation.

Unsupervised evaluation, where we use the complete dataset of 149 trends as a benchmark.

Supervised evaluation, where we train/tune our models on a training/development set and we test them on a test set.

The training set contains 50 trends constituted by 36.3K entities from 1996 to 2000.

The development set contains 34 trends constituted by 26.7K entities from 2000 to 2002.

The test set contains 65 trends constituted by 57K entities from 2002 to 2007.

Use

Please cite the data set and the accompanying paper if you found the resources in this repository useful:

@inproceedings{ponza2021, Title = {Contextualizing Trending Entities in News Stories}, author = {Ponza, Marco and Ceccarelli, Diego and Ferragina, Paolo and Meij, Edgar and Kothari, Sambhav}, Booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining}, Year = {2021}, }

Exploring E-commerce Trends: A Guide to Leveraging Dummy Dataset

Introduction: In the world of e-commerce, data is a powerful asset that can be leveraged to understand customer behavior, improve sales strategies, and enhance overall business performance. This guide explores how to effectively utilize a dummy dataset generated to simulate various aspects of an e-commerce platform. By analyzing this dataset, businesses can gain valuable insights into product trends, customer preferences, and market dynamics.

1. Dataset Overview: The dummy dataset contains information on 1000 products across different categories such as electronics, clothing, home & kitchen, books, toys & games, and more. Each product is associated with attributes such as price, rating, number of reviews, stock quantity, discounts, sales, and date added to inventory. This comprehensive dataset provides a rich source of information for analysis and exploration.

2. Data Analysis: Using tools like Pandas, NumPy, and visualization libraries like Matplotlib or Seaborn, businesses can perform in-depth analysis of the dataset. Key insights such as top-selling products, popular product categories, pricing trends, and seasonal variations can be extracted through exploratory data analysis (EDA). Visualization techniques can be employed to create intuitive graphs and charts for better understanding and communication of findings.

3. Machine Learning Applications: The dataset can be used to train machine learning models for various e-commerce tasks such as product recommendation, sales prediction, customer segmentation, and sentiment analysis. By applying algorithms like linear regression, decision trees, or neural networks, businesses can develop predictive models to optimize inventory management, personalize customer experiences, and drive sales growth.

4. Testing and Prototyping: Businesses can utilize the dummy dataset to test new algorithms, prototype new features, or conduct A/B testing experiments without impacting real user data. This enables rapid iteration and experimentation to validate hypotheses and refine strategies before implementation in a live environment.

5. Educational Resources: The dummy dataset serves as an invaluable educational resource for students, researchers, and professionals interested in learning about e-commerce data analysis and machine learning. Tutorials, workshops, and online courses can be developed using the dataset to teach concepts such as data manipulation, statistical analysis, and model training in the context of e-commerce.

6. Decision Support and Strategy Development: Insights derived from the dataset can inform strategic decision-making processes and guide business strategy development. By understanding customer preferences, market trends, and competitor behavior, businesses can make informed decisions regarding product assortment, pricing strategies, marketing campaigns, and resource allocation.

Conclusion: In conclusion, the dummy dataset provides a versatile and valuable resource for exploring e-commerce trends, understanding customer behavior, and driving business growth. By leveraging this dataset effectively, businesses can unlock actionable insights, optimize operations, and stay ahead in today's competitive e-commerce landscape

The Data Entry Software market has evolved significantly, emerging as a critical component across various industries for enhancing efficiency, accuracy, and productivity in data management. This software serves as a vital solution, enabling businesses to automate and streamline the process of entering large volumes

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

The Data Catalog Market is projected to grow at a CAGR of around 23.3% during 2023-28. Leading Companies - lation Inc., Alteryx, Ataccama ONE, Cloudera, Inc., Collibra, Google, IBM, Informatica, Microsoft Corporation, and Oracle.

The data analytic market size is projected to grow from USD 69.40 billion in the current year to USD 877.12 billion by 2035, representing a CAGR of 25.93%, during the forecast period till 2035.

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Big Data As A Service Market Size 2025-2029

The big data as a service market size is forecast to increase by USD 75.71 billion, at a CAGR of 20.5% between 2024 and 2029.

The Big Data as a Service (BDaaS) market is experiencing significant growth, driven by the increasing volume of data being generated daily. This trend is further fueled by the rising popularity of big data in emerging technologies, such as blockchain, which requires massive amounts of data for optimal functionality. However, this market is not without challenges. Data privacy and security risks pose a significant obstacle, as the handling of large volumes of data increases the potential for breaches and cyberattacks. Edge computing solutions and on-premise data centers facilitate real-time data processing and analysis, while alerting systems and data validation rules maintain data quality.
Companies must navigate these challenges to effectively capitalize on the opportunities presented by the BDaaS market. By implementing robust data security measures and adhering to data privacy regulations, organizations can mitigate risks and build trust with their customers, ensuring long-term success in this dynamic market.

What will be the Size of the Big Data As A Service Market during the forecast period?

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The market continues to evolve, offering a range of solutions that address various data management needs across industries. Hadoop ecosystem services play a crucial role in handling large volumes of data, while ETL process optimization ensures data quality metrics are met. Data transformation services and data pipeline automation streamline data workflows, enabling businesses to derive valuable insights from their data. Nosql database solutions and custom data solutions cater to unique data requirements, with Spark cluster management optimizing performance. Data security protocols, metadata management tools, and data encryption methods protect sensitive information. Cloud data storage, predictive modeling APIs, and real-time data ingestion facilitate agile data processing.
Data anonymization techniques and data governance frameworks ensure compliance with regulations. Machine learning algorithms, access control mechanisms, and data processing pipelines drive automation and efficiency. API integration services, scalable data infrastructure, and distributed computing platforms enable seamless data integration and processing. Data lineage tracking, high-velocity data streams, data visualization dashboards, and data lake formation provide actionable insights for informed decision-making.
For instance, a leading retailer leveraged data warehousing services and predictive modeling APIs to analyze customer buying patterns, resulting in a 15% increase in sales. This success story highlights the potential of big data solutions to drive business growth and innovation.

How is this Big Data As A Service Industry segmented?

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

Type

  Data Analytics-as-a-service (DAaaS)
  Hadoop-as-a-service (HaaS)
  Data-as-a-service (DaaS)


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


End-user

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The Data analytics-as-a-service (DAaas) segment is estimated to witness significant growth during the forecast period. The data analytics-as-a-service (DAaaS) segment experiences significant growth within the market. Currently, over 30% of businesses adopt cloud-based data analytics solutions, reflecting the increasing demand for flexible, cost-effective alternatives to traditional on-premises infrastructure. Furthermore, industry experts anticipate that the DAaaS market will expand by approximately 25% in the upcoming years. This market segment offers organizations of all sizes the opportunity to access advanced analytical tools without the need for substantial capital investment and operational overhead. DAaaS solutions encompass the entire data analytics process, from data ingestion and preparation to advanced modeling and visualization, on a subscription or pay-per-use basis. Data integration tools, data cataloging systems, self-service data discovery, and data version control enhance data accessibility and usability.

The continuous evolution of this market is driven by the increasing volume, variety, and velocity of data, as well as the growing recognition of the business value that can be derived from data insights. Organizations across var

Google Search Trends: Economic Measures: Mortgage Loan data was reported at 0.000 Score in 29 Nov 2025. This stayed constant from the previous number of 0.000 Score for 28 Nov 2025. Google Search Trends: Economic Measures: Mortgage Loan data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 29 Nov 2025, with 1460 observations. The data reached an all-time high of 100.000 Score in 23 Jan 2022 and a record low of 0.000 Score in 29 Nov 2025. Google Search Trends: Economic Measures: Mortgage Loan data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Yemen – Table YE.Google.GT: Google Search Trends: by Categories.

The global data masking market size was USD 908.57 million in 2024 & is projected to grow from USD 1041.22 million in 2025 to USD 3097.55 million by 2033.
Report Scope:

The size of the Data Prep market was valued at USD 1978 million in 2024 and is projected to reach USD 4859.03 million by 2033, with an expected CAGR of 13.7 % during the forecast period.

Data Product Readiness Scoring Market Outlook

According to our latest research, the global data product readiness scoring market size reached USD 1.18 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.4% from 2025 to 2033. This dynamic growth is primarily driven by the accelerating demand for data-driven decision-making across industries, the increasing complexity of data ecosystems, and the critical need for organizations to assess the maturity and usability of their data products before deployment. By 2033, the market is forecasted to attain a value of USD 5.15 billion, reflecting the pivotal role of data product readiness scoring in the evolving digital landscape.

The surge in digital transformation initiatives across enterprises globally is a key growth factor for the data product readiness scoring market. Organizations are increasingly leveraging advanced analytics, artificial intelligence, and machine learning to gain actionable insights from their data assets. However, the success of these initiatives is heavily contingent upon the quality, governance, and integration of data products. As a result, businesses are adopting readiness scoring solutions to systematically evaluate whether their data products meet established standards for quality, compliance, and usability. This trend is further amplified by the growing recognition that data-driven innovation hinges on the reliability and maturity of underlying data assets, thus propelling the adoption of readiness scoring frameworks.

Another significant driver is the rising regulatory scrutiny and compliance requirements in sectors such as BFSI, healthcare, and government. Strict mandates around data privacy, integrity, and traceability have compelled organizations to implement rigorous data governance practices. Data product readiness scoring tools enable these organizations to ensure that their data products are compliant with industry regulations before deployment, thereby reducing the risk of non-compliance penalties and reputational damage. This compliance-centric approach is particularly pronounced in regions such as North America and Europe, where regulatory landscapes are highly mature and constantly evolving, making readiness scoring an indispensable part of the data lifecycle.

The proliferation of cloud computing and the increasing adoption of hybrid and multi-cloud environments have also played a crucial role in market expansion. As organizations migrate their data assets to cloud platforms, the complexity of managing and integrating disparate data sources has grown exponentially. Data product readiness scoring solutions help organizations navigate this complexity by providing a standardized framework to assess data readiness across diverse environments. This capability not only accelerates the time-to-insight but also ensures that data products are scalable, interoperable, and aligned with business objectives, further fueling market growth.

Regionally, North America continues to dominate the data product readiness scoring market, accounting for the largest share in 2024. This leadership is attributed to the strong presence of technology giants, early adoption of advanced data management practices, and a highly regulated business environment. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increasing investments in data infrastructure, and the rising awareness of data quality and governance in developing economies. Europe remains a key market, characterized by stringent data protection regulations and a mature enterprise landscape, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions accelerate their digital transformation journeys.

Component Analysis

The data product readiness scoring market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses a wide array of platforms and tools designed to automate the assessment of data product maturity, quality, and compliance. These solutions leverage advanced algorithms, machine learning, and artificial intelligence to provide real-time insights into the readiness of data products for deployment. The increasing sophistication of these tools, coupled with their ability to integrate seamlessly with existing data management systems, has made software the dominant component

The global data preparation software market is estimated at USD 579.3 million in 2025 and is expected to witness a compound annual growth rate (CAGR) of 8.1% from 2025 to 2033. Factors such as increasing data volumes, growing demand for data-driven insights, and the adoption of artificial intelligence (AI) and machine learning (ML) technologies are driving the growth of the market. Additionally, the rising need for data privacy and security regulations is also contributing to the demand for data preparation software. The market is segmented by application into large enterprises and SMEs, and by type into cloud-based and web-based. The cloud-based segment is expected to hold the largest market share during the forecast period due to its benefits such as ease of use, scalability, and cost-effectiveness. The market is also segmented by region into North America, South America, Europe, the Middle East and Africa, and Asia Pacific. North America is expected to account for the largest market share, followed by Europe. The Asia Pacific region is expected to witness the fastest growth during the forecast period. Key players in the market include Alteryx, Altair Monarch, Tableau Prep, Datameer, IBM, Oracle, Palantir Foundry, Podium, SAP, Talend, Trifacta, Unifi, and others. Data preparation software tools assist organizations in transforming raw data into a usable format for analysis, reporting, and storage. In 2023, the market size is expected to exceed $10 billion, driven by the growing adoption of AI, cloud computing, and machine learning technologies.

The Global Retail Sales Data provided here is a self-generated synthetic dataset created using Random Sampling techniques provided by the Numpy Package. The dataset emulates information regarding merchandise sales through a retail website set up by a popular fictional influencer based in the US between the '23-'24 period. The influencer would sell clothing, ornaments and other products at variable rates through the retail website to all of their followers across the world. Imagine that the influencer executes high levels of promotions for the materials they sell, prompting more ratings and reviews from their followers, pushing more user engagement.

This dataset is placed to help with practicing Sentiment Analysis or/and Time Series Analysis of sales, etc. as they are very important topics for Data Analyst prospects. The column description is given as follows:

Order ID: Serves as an identifier for each order made.

Order Date: The date when the order was made.

Product ID: Serves as an identifier for the product that was ordered.

Product Category: Category of Product sold(Clothing, Ornaments, Other).

Buyer Gender: Genders of people that have ordered from the website (Male, Female).

Buyer Age: Ages of the buyers.

Order Location: The city where the order was made from.

International Shipping: Whether the product was shipped internationally or not. (Yes/No)

Sales Price: Price tag for the product.

Shipping Charges: Extra charges for international shipments.

Sales per Unit: Sales cost while including international shipping charges.

Quantity: Quantity of the product bought.

Total Sales: Total sales made through the purchase.

Rating: User rating given for the order.

Review: User review given for the order.

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Subscriber Data Management Market Size 2024-2028

The subscriber data management market size is forecast to increase by USD 4.08 billion at a CAGR of 16.9% between 2023 and 2028.

The market is experiencing significant growth due to the increasing adoption of target advertisement-based streaming apps. This trend is driven by the rising demand for personalized content and services, which necessitates effective management of subscriber data. Furthermore, the proliferation of 5G technology is fueling the need for faster and more secure data processing and transmission. However, this market is not without challenges. Data privacy and security risks continue to pose a significant threat, with subscriber data being a valuable asset for cybercriminals. Companies must invest in security measures to protect sensitive information and maintain customer trust. Additionally, regulatory compliance and data interoperability across multiple platforms are other challenges that market participants must navigate to capitalize on the opportunities presented by this dynamic market. Overall, the market offers significant potential for growth, particularly for those players who can effectively address the evolving needs of subscribers and mitigate the risks associated with managing large volumes of sensitive data.

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

Request Free SampleThe market in the US is experiencing significant growth due to the increasing number of mobile subscriptions and the shift towards cloud-based solutions. Telecom operators are prioritizing network functions virtualization (NFV) and long-term evolution (LTE) technologies to enhance their mobile networks, leading to an escalating demand for user data repositories and policy management systems. Telecommunication network providers are also focusing on data security to mitigate cyberattacks and ensure data privacy policies are adhered to. Moreover, the proliferation of Voice over LTE (VoLTE) and Volte services, as well as the integration of subscriber data management systems in telecom service providers' customer relationship management (CRM) and identity management solutions, is driving market expansion. The market is expected to continue growing as 5G subscriptions increase, with hybrid solutions gaining popularity among network carriers to optimize their on-premise and cloud-based offerings. The market's size and direction reflect the industry's commitment to delivering secure, efficient, and innovative subscriber data management solutions to meet the evolving needs of mobile subscribers.

How is this Subscriber Data Management Industry segmented?

The subscriber data management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeMobile networksFixed networksGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanMiddle East and AfricaSouth America

By Type Insights

The mobile networks segment is estimated to witness significant growth during the forecast period.The Subscriber Data Management (SDM) market is driven by the mobile networks segment, which accounts for a substantial revenue share. This growth is attributed to the widespread use of mobile devices and the escalating demand for high-speed data services, particularly with the emergence of 5G technology. Mobile networks necessitate advanced SDM solutions to manage the voluminous subscriber data, ensuring uninterrupted service delivery and superior user experiences. The integration of 5G has intensified the need for sophisticated SDM systems due to the complexities introduced in data management, including real-time processing, authentication, and security. Cloud-based SDM solutions with cloud-native design are increasingly popular due to their flexibility, scalability, and ability to handle large volumes of data. Identity management, data integration, and policy management are crucial components of these solutions. The Internet of Things (IoT) and Voice Over IP (VoIP) are additional areas driving the market, as they generate substantial subscriber data that needs to be managed effectively. Data security and privacy are paramount concerns, necessitating the adoption of advanced security solutions and adherence to stringent data privacy policies. Network Carriers, Telecom Operators, and Communication Service Providers (CSPs) are key players in the market, leveraging SDM systems to manage their subscriber data and enhance network performance. Network Functions Virtualization (NFV) and Long-Term Evolution (LTE) are key technologies enabling the deployment of SDM solutions in a hybrid environment, ensuring seamless integration with fixed networks and mobile networks. The market is further fueled by the increas

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Sales Intelligence Market Size 2025-2029

The sales intelligence market size is forecast to increase by USD 4.86 billion at a CAGR of 17.6% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for custom-made solutions that cater to the unique needs of businesses. This trend is fueled by the rapid advancements in cloud technology, enabling real-time access to comprehensive and accurate sales data from anywhere. However, the high initial cost of implementing sales intelligence solutions can act as a barrier to entry for smaller organizations. Furthermore, regulatory hurdles impact adoption in certain industries, requiring strict compliance with data privacy regulations. With the advent of cloud computing and SaaS customer relationship management (CRM) systems, businesses are able to store and access customer information more efficiently. Moreover, the exponential growth of marketing intelligence, driven by big data and natural language processing (NLP) technologies, enables organizations to gain valuable insights from customer interactions.
Despite these challenges, the market's potential is vast, with opportunities for growth in sectors such as healthcare, finance, and retail. Companies seeking to capitalize on these opportunities must navigate these challenges effectively, investing in cost-effective solutions and ensuring regulatory compliance. By doing so, they can gain a competitive edge through improved lead generation, enhanced customer insights, and streamlined sales processes.

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

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In today's business landscape, sales intelligence has become a critical driver of revenue growth. The go-to-market strategy of companies relies heavily on predictive lead scoring and sales pipeline analysis to prioritize opportunities and optimize resource allocation. Sales operations teams leverage revenue intelligence to gain insights into sales performance and identify trends. Data quality is paramount in sales analytics dashboards, ensuring accurate sales negotiation and closing. Sales teams collaborate using sales enablement platforms, which integrate CRM systems and provide sales performance reporting. Sales process mapping and sales engagement tools enable effective communication and productivity. Conversational AI and sales automation software streamline sales outreach and prospecting efforts. Messaging and alerting features help sales teams engage with potential customers effectively, while chatbots facilitate efficient communication.
Sales forecasting models and intent data inform sales management decisions, while salesforce automation and data governance ensure data security and compliance. Sales effectiveness is enhanced through sales negotiation training and sales enablement training. The sales market is dynamic, with trends shifting towards advanced analytics and AI-driven solutions. Companies must adapt to stay competitive, focusing on data-driven strategies and continuous improvement.

How is this Sales Intelligence Industry segmented?

The sales intelligence 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


Component

  Software
  Services


Application

  Data management
  Lead management


End-user

  IT and Telecom
  Healthcare and life sciences
  BFSI
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. In today's business landscape, sales intelligence platforms have become indispensable tools for organizations seeking to optimize their sales processes and gain a competitive edge. These solutions offer various features, including deal tracking, win-loss analysis, data mining, sales efficiency, customer journey mapping, sales process optimization, pipeline management, sales cycle analysis, revenue optimization, market research, data integration, customer segmentation, sales engagement, sales coaching, sales playbook, sales process automation, business intelligence (BI), predictive analytics, target account identification, lead generation, account-based marketing (ABM), sales strategy, sales velocity, real-time data, artificial intelligence (AI), sales insights, sales enablement content, sales enablement, sales funnel optimization, sales performance metrics, competitive intelligence, sales methodology, customer churn, and machine learning (ML) for sales forecasting and buyer persona deve

The size of the Data Center Cooling Pumps market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.