The Evolution of the European Data Market and How UnionAll Facilitates Seamless Data Exchange

The European data market is undergoing a significant transformation, playing an increasingly pivotal role in shaping industries and economies. According to the European Data Market Study 2021-2023, the data economy in the EU27 surpassed €544 billion in 2023, reflecting a growth rate of 9.3% compared to the previous year​ (European Data Market Study). This rapid expansion highlights the growing importance of efficient data sharing and monetization across sectors.

UnionAll aims to support this shift by providing data sellers with the tools they need to manage, sell, and share data through their own customizable data shops.

Key Trends Shaping the Data Market

The European Data Market report shows a marked increase in both data providers and users. In 2023, there were over 238,000 data provider companies in EU27 and 604,000 data user companies, reflecting growth rates of 9.2% and 3.5% year-over-year, respectively​ (European Data Market Study). These numbers demonstrate how businesses across Europe are becoming more dependent on data for operations, innovation, and strategic decision-making.

However, this growth is accompanied by certain challenges. The study identifies a persistent data skills gap across Europe, with 363,000 unfilled data professional positions in EU27 in 2023. Although data professionals make up 4.3% of the EU workforce, demand continues to outpace supply​ (European Data Market Study). Addressing this skills gap is crucial for businesses looking to fully leverage their data capabilities.

UnionAll’s Role in the Data Economy

In response to these trends, UnionAll offers a solution that simplifies the data exchange process. For data sellers, the platform provides the necessary infrastructure to set up their own data shops, allowing them to sell data directly to interested buyers. This approach not only streamlines transactions but also supports the increasing need for transparency and regulation compliance in data sharing.

By offering a flexible platform, UnionAll ensures that data providers can showcase their datasets effectively, reach potential buyers, and manage transactions securely. The platform’s tools are designed to address the practical needs of companies navigating this growing market.

Looking Ahead: The Future of Data in Europe

The European Data Market report predicts continued growth in the data market, with projections suggesting that the EU27 data market will reach €118 billion by 2030 under a baseline scenario​(European Data Market Study). This growth will be driven by increasing digital transformation, improved data governance, and broader adoption of data-driven innovation across industries.

UnionAll is well-positioned to support this ongoing evolution by offering a platform that simplifies the complexities of data buying and selling. As businesses in Europe continue to recognize the value of data, platforms like UnionAll will play a key role in ensuring that the data economy remains accessible and efficient for all participants.

Conclusion

The data economy is becoming a vital part of Europe's economic landscape, with increasing numbers of companies relying on data to drive innovation and growth. The European Data Market Study 2021-2023 underscores this trend, offering insights into the market’s current state and future trajectory. By providing an accessible, user-friendly platform for data exchange, UnionAll is contributing to the growth and efficiency of this expanding market.

As the data market continues to evolve, having the right tools to navigate it will become even more essential. UnionAll offers a practical, straightforward solution for data providers looking to engage with this dynamic sector.

References:

European Data Market Study 2021-2023, Final Study Report, European Commission, 2024​

Navigating the Future of Alternative Data with UnionAll

The alternative data market is booming, and according to Neudata’s The Future of Alternative Data 2024 report, the industry is set for even more growth. With 95% of data buyers expecting to maintain or increase their budgets next year, it’s clear that the value of alternative data continues to rise. However, along with this expansion come challenges that buyers and vendors alike need to address.

According to Neudata, the primary hurdles in this space include high costs (62% of buyers cite price as a barrier), limited trial periods, and compliance concerns. Buyers also report difficulty in discovering suitable data during trials, which affects their purchasing decisions. The report indicates that most funds only subscribe to less than a quarter of the datasets they trial, largely due to these pain points​ (Neudata).

This is where UnionAll comes in, offering innovative solutions to solve the very problems highlighted by Neudata. By utilizing AI-driven technology, UnionAll simplifies the data buying and selling process.

Here’s how:

1. Price Flexibility: UnionAll’s platform allows vendors to upload and manage their datasets with ease, giving them the ability to offer smaller, customized slices of data that fit buyers’ budgets. This directly addresses the demand for more flexible pricing models​.

2. AI-Powered Discovery: One of the key insights from Neudata’s report is the difficulty buyers face in finding the right datasets. UnionAll tackles this head-on with an intuitive, AI-driven search tool. Using a chat-based interface, buyers can describe their specific business problems and receive tailored dataset recommendations, dramatically speeding up the discovery process.

3. Streamlined Trial and Purchase Process: UnionAll reduces friction in the trial phase by enabling instant insights through AI. Buyers can immediately ask questions, generate SQL queries, and analyze sample data, allowing for quick validation of data quality before purchase. If a dataset doesn’t meet the buyer’s needs, UnionAll’s AI assistant even recommends alternative datasets, helping vendors cross-sell​.

4. Compliance and Administration Simplified: For vendors, managing data compliance and administrative tasks can be time-consuming. UnionAll automates much of this process by using AI to handle everything from documentation to publishing across multiple marketplaces, including Snowflake, with Databricks and AWS integrations coming soon​.

As the alternative data market continues to evolve, UnionAll is positioned to bridge the gap between the needs of buyers and the offerings of data vendors. With the industry projected to see significant growth in 2025, UnionAll’s AI-powered platform is a timely solution for addressing the cost, discovery, and trial challenges identified by Neudata’s report.

By leveraging AI to simplify data commerce, UnionAll makes it easier for everyone to tap into the growing potential of alternative data.

For more insights from Neudata, check out their The Future of Alternative Data 2024 report​.

Turning Data into Revenue Streams: A Comprehensive Overview of Data Monetization

What is Data Monetization?

Modern organisations are increasingly recognizing the economic potential in their data - in the age of digital transformation, data is widely stated to be an organisation’s most valuable internal assets. Data monetization however, involves the strategic process of transforming internal data into new revenue generation, and most often not included in the general valuation of internal data.

By exploring how internal data can be packaged into sellable data products and making it available to external users, a whole new sphere of economic value and new insights can be generated.

This overview will dig deeper into the multifaceted area of data monetization, exploring its types, ethical considerations, steps, pricing strategies, and frameworks, with a focus on enabling organisations to turn their data into new revenue streams.

Data Driven Organizations Utilising Internal Data

Internal data can be strategically utilised to enhance internal operations, drive efficiencies, and contribute to revenue generation. Following are three types of internal data that organisations commonly possess and leverage to various benefits:

Operational Data: Data generated from day-to-day business activities. Examples include sales transactions, production metrics, and inventory levels. Analysing operational data can provide insights into process efficiency, identify bottlenecks, and streamline workflows.

Customer Data: Information about customer interactions, preferences, and behaviours is crucial for businesses. Internal customer data can be used to personalise offerings, improve customer experience, and implement targeted marketing strategies.

Supply Chain Data: For businesses involved in production and distribution, internal data on the supply chain is vital. This includes information on suppliers, logistics, and inventory management, which can be optimised for cost-efficiency.

Sensitivity in Data Monetization

Most types of internally gathered data can be used to generate operational efficiencies and benefits. However, it can also be of great value to external organisations aiming to gather insights into new business areas. Though, before delving into making internal data available to outside organisations, there are several factors to explore.

Individuals & PII classified data: User data can be of great value for external companies seeking insights into consumer behaviour. To balance business objectives with privacy, however, organisations can responsibly monetize data through anonymization and aggregation.

When monetizing user data, stringent privacy policies and user consent needs to be taken carefully into account, and expert advice is recommended before considering making user data available for monetization.

By removing personally identifiable information and consulting trusted third-party experts, businesses can extract valuable insights without compromising user privacy. This approach not only ensures compliance with regulations but also strengthens trust between companies and their user base, creating a win-win scenario for both parties.

Businesses: As written above, organisations generate substantial data during operations, encompassing transaction records, customer interactions, and various internal processes. This data is much less sensitive in terms of privacy issues, but can be risky to make available to the wrong buyer, should there for instance be a risk of making competitive insights available to direct competitors. It is therefore strongly recommended to carefully develop the data products and define who the data should be made available for.

Example of Industries Already Putting Data Monetization to Work

Financial Services Industry:

Financial services companies serve as typical examples of how to successfully generate revenue through data monetization. Credit card issuers and banks can strategically employ customer transaction data to refine cross-selling strategies. Partnerships with merchants can amplify revenue streams through data-driven reward programs.

Telecommunications Industry:

Telecommunications companies leverage data monetization by analysing customer usage patterns, preferences, and network performance. They can sell anonymized and aggregated data to advertisers, providing insights into consumer behaviour and enabling targeted advertising. Additionally, telecom companies can offer location-based services to third-party businesses, such as retail stores or advertisers, based on the geospatial data collected from mobile devices.

Healthcare Sector:

In the healthcare industry, organisations can monetize data through various means. Pharmaceutical companies can use patient data to identify trends, optimise clinical trials, and personalise drug development. Health insurance providers can utilise patient health records to create personalised wellness programs and offer insights to employers for employee well-being initiatives. Moreover, healthcare data analytics companies can aggregate and anonymize healthcare data to sell valuable insights to researchers, pharmaceutical companies, and other stakeholders.

How Data Monetization can be Streamlined with Automation

The ease of creating valuable data products and making these available to third parties through public data marketplaces has increased vastly over the last few years. Here, UnionAll has taken vast steps into automating the process, demanding less in terms of time and resources from the monetizing party when aiming to create and publish data products.

The market for external data is continuously growing, and we are seeing a strong increase in demand for new and still unexplored sectors for data monetization. The need to enrich internal company data with external insights, or utilise third party data for training AI-models is taking off with great velocity in the coming years. This opens up great opportunities for companies seeking to increase revenues and explore the realm of data monetization.

How to Monetize Your Data

Monetizing internal company data can be a long and resource intensive process, including the below described step:

Identify the Data

Find the data that can be monetized. This can be both internal data such as customer, network, or operations data.

Analyze the data

Assess the quality, which can be done automatically by applying AI, and determine the data asset’s sellability by analysing what insights that could be drawn from the data.

Define the Value Proposition

Identify potential customers or partners that could benefit from the data and decide on the business model and pricing strategy. Consider potential regulatory limitations as mentioned above.

Develop the Data Product

Source, clean, transform, encrypt, enrich, document, and publish the data asset on the desired platforms.

Market the Data Product

Market the data through relevant platforms and apply search engine optimization (SEO), or leverage existing business networks to reach potential buyers.

Making the Data Available on Data Marketplaces

Data marketplaces serve as dynamic platforms where organisations can publish, share, and extract value from their data. These marketplaces act as intermediaries, connecting data providers with potential buyers, fostering an ecosystem for data exchange. Understanding the dynamics of data marketplaces is important for organisations seeking to capitalise on data assets.

Data Product Go to Market

Pricing data for external buyers involves a thoughtful approach to ensure fairness, attractiveness, and alignment with the value provided. Here are some strategies and considerations for pricing data in the context of data monetization.

Analysing current supply of similar products and building an understanding of existing demand is important for establishing the initial price for the data products. Factors such as data freshness, granularity and size of the datasets compared to existing products on the market will also play a significant part in pricing the data.

How UnionAll is Automating the Process of Monetizing Data

UnionAll has taken vast steps into automating the above described process, demanding less in terms of time and resources from the monetizing party when aiming to create and publish data products. This process includes automated discovery, packaging and publishing. Based on vast insight into existing data on public marketplaces, as well as current demand for new data products, UnionAll can tailor products and price, as well as marketing campaigns and relevant content to get maximum throughput on value for published data products.