Data Marketplaces Market size was valued at USD 1.09 Billion in 2023 and is projected to reach USD 1.29 Billion by 2031, growing at a CAGR of 4.56% during the forecast period 2024-2031.

Global Data Marketplaces Market Drivers

The market drivers for the Data Marketplaces Market can be influenced by various factors. These may include:

Increasing Big Data Adoption: The proliferation of big data across industries has led to a significant rise in the need for data marketplaces. Organizations are increasingly aware of the value of data-driven decision-making, which has spurred demand for diverse data sources. Companies seek to harness large volumes of structured and unstructured data, driving them to marketplaces for innovative data solutions. The challenge of managing data internally encourages businesses to leverage external data assets, enhancing analytics capabilities and fostering better customer insights. Consequently, the growth of big data adoption directly influences the expansion and diversification of data marketplaces.

Enhanced Data Privacy Regulations: The introduction of stringent data privacy regulations, such as GDPR and CCPA, has transformed how businesses manage and exchange data. These regulations compel organizations to be more transparent in their data handling processes, fostering a need for compliant data sources. As companies prioritize adherence to legal standards, they are increasingly turning to data marketplaces with vetted datasets that ensure compliance. This shift is enhancing trust and encouraging more organizations to participate in data trading. Therefore, the evolution of privacy laws is a significant driver for the growth of the data marketplace ecosystem.

Global Data Marketplaces Market Restraints

Several factors can act as restraints or challenges for the Data Marketplaces Market. These may include:

Regulatory Compliance: Data marketplaces face significant challenges due to stringent regulations concerning data privacy and security. Regulatory frameworks such as GDPR and CCPA impose strict guidelines on how data can be collected, stored, and shared. Organizations must ensure that their data practices align with these regulations, which can lead to increased costs and complexity in operations. Non-compliance can result in severe penalties, reputational damage, and loss of consumer trust. This regulatory burden may deter businesses from participating in data marketplaces, as they grapple with evolving compliance standards and the potential for legal ramifications associated with mishandled data.

Data Quality and Integrity: The success of data marketplaces hinges on the quality and integrity of the data being offered. Poorly curated or inaccurate data can undermine the credibility of the marketplace and diminish user trust. Buyers seeking high-quality datasets may be deterred by the fear of investing in unreliable information. Additionally, maintaining data quality requires constant monitoring, validation, and updating, which can strain resources for marketplace operators. This challenge is further exacerbated by the proliferation of data sources, making it difficult to ensure consistency and accuracy across the offerings, ultimately affecting buyer satisfaction and marketplace growth.

This dataset contains information about all publications collected from the Scopus database using the keywords of (“data market*”) and (“data marketplace*”). The dataset was extracted on 6 July 2020. The dataset is a supplementary document of the article entitled “Business Data Sharing through Data Marketplaces: A Systematic Literature Review”. The dataset contains nine sheets.

This dataset is a supplementary document of the article entitled “Creating a Taxonomy of Business Models for Data Marketplace.” In general, the dataset contains a list of data marketplaces (n=178) identified from the desk research process. It also covers information about the final sample of 40 data marketplaces to develop the taxonomy.

The Data Marketplace Platform market is experiencing robust growth, driven by the increasing demand for data-driven decision-making across various industries. The market's expansion is fueled by several key factors, including the proliferation of big data, the rising adoption of cloud-based solutions, and the growing need for data monetization strategies. Businesses are increasingly seeking efficient and secure ways to access, share, and analyze diverse datasets, leading to a surge in demand for platforms that facilitate these processes. Furthermore, the development of advanced analytics and AI/ML capabilities further enhances the value proposition of these platforms, attracting both data buyers and sellers. Competition is fierce, with established tech giants like Microsoft, Oracle, and AWS alongside specialized data marketplace providers vying for market share. The market is segmented by data type (structured, unstructured), deployment model (cloud, on-premise), and industry vertical (finance, healthcare, retail, etc.), each exhibiting unique growth trajectories. A conservative estimate suggests a market size of approximately $5 billion in 2025, growing at a CAGR of 25% over the forecast period (2025-2033). This growth is expected to be driven by increasing cloud adoption, improved data security measures, and the emergence of innovative business models within the data marketplace ecosystem. The competitive landscape is characterized by both large established players and nimble startups. Successful players are those that offer comprehensive solutions encompassing data discovery, secure access control, data governance, and advanced analytics capabilities. Geographic expansion and strategic partnerships are crucial for achieving sustainable growth. While the market enjoys significant growth potential, challenges remain including data privacy concerns, data quality issues, and the need to establish trust and transparency within the marketplace ecosystem. Addressing these challenges effectively will be critical for the continued success and expansion of the Data Marketplace Platform market. The robust growth forecast suggests significant opportunities for both established players and new entrants to capitalize on this expanding market.

The Data Marketplaces market has rapidly evolved into a pivotal sector within the broader data economy, serving as a dynamic platform for buying, selling, and exchanging data. In simple terms, data marketplaces act as digital storefronts where data providers can monetize their datasets, while data-driven businesses

Data Marketplace Platform Market size was valued at USD 1.4 Billion in 2024 and is projected to reach USD 7.5 Billion by 2032, growing at a CAGR of 22.5% from 2026 to 2032.The growth of the Data Marketplace Platform Market is primarily driven by the increasing demand for secure and transparent data exchange across industries, the rise in adoption of big data and analytics, and the need for effective data monetization strategies. Additionally, growing investments in data infrastructure and increasing use of AI and ML technologies to manage and extract value from data are fueling market expansion.

This dataset is a supplementary document to the article entitled "Business model implications of privacy-preserving technologies in data marketplaces: The case of multi-party computation." It is also a supplementary document for Chapter 3 of the dissertation entitled "The impact of Multi-Party Computation on data sharing decisions in data marketplaces: insights from businesses and consumers". The data was collected through semi-structured interviews conducted in March-June 2020. Further details are provided in the article and in the methodology section in Chapter 3 of the dissertation.

The Medical Data Marketplace Services market has emerged as a pivotal segment in the healthcare ecosystem, providing a seamless avenue for the exchange of medical data between various stakeholders including healthcare providers, pharmaceutical companies, researchers, and technology firms. This marketplace facilitate

The Enterprise Data Monetization Platform market is experiencing robust growth, driven by the increasing need for organizations to leverage their data assets for revenue generation and competitive advantage. The market is projected to be valued at $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This significant expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the growing demand for advanced analytics and AI-driven insights, and the increasing regulatory focus on data privacy and security. Businesses are increasingly recognizing the potential of their data to create new revenue streams through personalized services, targeted advertising, and data-driven product development. Furthermore, the emergence of innovative data monetization strategies, such as data marketplaces and data-as-a-service models, is further accelerating market growth. However, challenges remain. Data security and privacy concerns continue to be significant hurdles, requiring robust security measures and compliance with regulations like GDPR and CCPA. The complexity of data integration and management, along with the need for skilled professionals to effectively monetize data, also pose barriers to entry for some organizations. Despite these challenges, the long-term outlook for the Enterprise Data Monetization Platform market remains positive, with continued technological advancements and evolving business models expected to drive further expansion in the coming years. Major players like Microsoft, Google, and Salesforce are heavily investing in this space, indicating its strategic importance within the broader technology landscape.

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

Comprehensive dataset containing 45 verified Marketplace locations in United States with complete contact information, ratings, reviews, and location data.

The Office of Enterprise Data and Analytics, within the Centers for Medicare aqnd Medicaid Services (CMS), has developed a set of information products and analytics examining enrollment activity in the Health Insurance Marketplaces (the Marketplaces). The Marketplaces were established in 2014 and allow individuals to shop for health insurance and dental plans. While some of the data products available here include data for all 50 states and the District of Columbia, other products focus only on states that utilize the Healthcare.gov platform (38 states in 2016). Each product clearly defines the population. Caution is recommended when comparing annual data as the definitions for some variables have changed from one reporting period to another. These changes are noted in the affected products.

The data monetization market is experiencing significant growth, projected to reach $4.17 billion in 2025 and exhibiting a robust Compound Annual Growth Rate (CAGR) of 19.94% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and variety of data generated across industries, coupled with advancements in data analytics and AI, are creating lucrative opportunities for businesses to extract value from their data assets. Furthermore, the rising adoption of cloud computing and improved data security measures are fostering trust and enabling wider data sharing and monetization initiatives. Growing regulatory pressure around data privacy, while presenting challenges, also drives innovation in secure and compliant data monetization strategies. Key players like SAS Institute, Infosys, and Accenture are capitalizing on these trends, developing sophisticated solutions for data management, analysis, and secure exchange, thus facilitating the market's growth. The market segmentation, while not explicitly detailed, likely includes various data types (structured, unstructured), monetization models (data licensing, data-as-a-service, data marketplaces), and industry verticals (finance, healthcare, retail). The competitive landscape features a mix of established technology giants and specialized data monetization firms. While restraints exist, such as data quality issues, lack of standardized protocols, and potential ethical concerns regarding data usage, ongoing technological advancements and increasing awareness of data's economic value are expected to mitigate these challenges and sustain the market's high growth trajectory throughout the forecast period. The market's projected expansion underscores the transformative potential of data monetization, enabling businesses to generate new revenue streams and unlock significant value from their data assets. Key drivers for this market are: Rapid Adoption of Advanced Analytics and Visualization, Increasing Volume and Variety of Business Data. Potential restraints include: Interoperability With Existing Systems, Varying Structure of Regulatory Policies. Notable trends are: Large Enterprises to Hold Major Market Share.

Office of Agriculture's listing of farmers markets in the County. Includes market managers' name and contact information, seasons of operation, operation times and accepted programs. This data will update annually.

"Anonymized database pertaining to the AlphaBay marketplace. This data was used in the papers ""Plug and Prey? Measuring the Commoditization of Cybercrime via Online Anonymous Markets"" (Van Wegberg et al., 2018), ""An Empirical Analysis of Traceability in the Monero Blockchain"" (Moeser et al., 2018) and in the joint EMCDDA/EUROPOL report ""Drugs and thedarknet: Perspectives for enforcement, researchand policy"" (EMCDDA, 2017). In this dataset, we chose not to make available any textual information (item name, description, or feedback text). We also anonymized all handles (user id, item id). This represents more than two and a half years of parsed data from what was arguably the largest online anonymous marketplace ever.

EMCDDA (2017) Drugs and thedarknet: Perspectives for enforcement, researchand policy. November 2017.
Van Wegberg et al.. Plug and Prey? Measuring the Commoditization of Cybercrime via Online Anonymous Markets. To appear in Proceedings of the 27th USENIX Security Symposium (USENIX Security'18). Baltimore, MD. August 2018.
Moeser et al. An Empirical Analysis of Traceability in the Monero Blockchain. To appear in Proceedings of the Privacy Enhancing Technology Symposium (PETS 2018), volume 3. Barcelona, Spain. July 2018."

This dataset is a supplementary document to Chapter 5 of the dissertation entitled "The impact of Multi-Party Computation on data sharing decisions in data marketplaces: insights from businesses and consumers." Data was collected through an online experiment conducted on 15 November 2021. Further details are provided in Chapter 5 of the dissertation.

Longitude and latitude, state, address, name, and zip code of Farmers Markets in the United States

This is raw (quantitative) research data based collected for TRUSTS Project deliverable: D7.2 Sustainable business model for TRUSTS data marketplace II. Specifically, this data relates to section “3. Evaluating Value Creation of a Federated Data Marketplace.” This section is divided into two steps: Steps 1 and 2.

The research data follow these steps. The spreadsheets of 1_Raw data, 1_Survey item, 1_Descriptive statistic, 1_Demographic_portrait, and 1_Reliability and validity belong to Step 1. Meanwhile, 2_Raw data, 2_Codebook, 2_Descriptive statistic, 2_Model, 2_Reliability and validity, and 2_Structural model belong to Step 2.

This research data is used for Partial Least Squares Structural Equation Modelling (PLS-SEM) analysis.

Amazon Business is the most popular business-to-business (B2B) generalist marketplace, a worldwide survey from 2022 revealed. ** percent of B2B buyers stated to have used it, while eBay followed with usage rate of nearly ** percent. ** percent of respondents used Chinese marketplace Alibaba.

Brazilian E-Commerce Public Dataset by Olist

Welcome! This is a Brazilian ecommerce public dataset of orders made at Olist Store. The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allows viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers. We also released a geolocation dataset that relates Brazilian zip codes to lat/lng coordinates.

This is real commercial data, it has been anonymised, and references to the companies and partners in the review text have been replaced with the names of Game of Thrones great houses.

Join it With the Marketing Funnel by Olist

We have also released a Marketing Funnel Dataset. You may join both datasets and see an order from Marketing perspective now!

Instructions on joining are available on this Kernel.

Context

This dataset was generously provided by Olist, the largest department store in Brazilian marketplaces. Olist connects small businesses from all over Brazil to channels without hassle and with a single contract. Those merchants are able to sell their products through the Olist Store and ship them directly to the customers using Olist logistics partners. See more on our website: www.olist.com

After a customer purchases the product from Olist Store a seller gets notified to fulfill that order. Once the customer receives the product, or the estimated delivery date is due, the customer gets a satisfaction survey by email where he can give a note for the purchase experience and write down some comments.

Attention

1. An order might have multiple items.
2. Each item might be fulfilled by a distinct seller.
3. All text identifying stores and partners where replaced by the names of Game of Thrones great houses.

Example of a product listing on a marketplace

https://i.imgur.com/JuJMns1.png" alt="Example of a product listing on a marketplace">

Data Schema

The data is divided in multiple datasets for better understanding and organization. Please refer to the following data schema when working with it: https://i.imgur.com/HRhd2Y0.png" alt="Data Schema">

Classified Dataset

We had previously released a classified dataset, but we removed it at Version 6. We intend to release it again as a new dataset with a new data schema. While we don't finish it, you may use the classified dataset available at the Version 5 or previous.

Inspiration

Here are some inspiration for possible outcomes from this dataset.

NLP:
This dataset offers a supreme environment to parse out the reviews text through its multiple dimensions.

Clustering:
Some customers didn't write a review. But why are they happy or mad?

Sales Prediction:
With purchase date information you'll be able to predict future sales.

Delivery Performance:
You will also be able to work through delivery performance and find ways to optimize delivery times.

Product Quality:
Enjoy yourself discovering the products categories that are more prone to customer insatisfaction.

Feature Engineering:
Create features from this rich dataset or attach some external public information to it.

Acknowledgements

Thanks to Olist for releasing this dataset.