Ultimate Guide to The Data Marketplace in 2024
Data marketplaces are soaring in popularity. They're a one-stop-shop for buying and selling external data. For data buyers, they make data sourcing effortless. For data providers, they maximize data monetization.
There are constantly new data exchange platforms being created: for different data types, different users, and different industries.
Our Ultimate Guide to the Data Marketplace tells you everything you need to know about the best data marketplaces available today. You'll learn about the difference between data exchanges, who's using them, and why data marketplaces are the most powerful method of data exchange in 2024.
A data marketplace is a platform where users can buy and sell data. A data marketplace is an easy way for data providers to market, manage and sell their data. In turn, data marketplaces allow data buyers to browse, compare and purchase data from multiple sources collected in one, easy-to-navigate marketplace.
A data marketplace works in the same way as any other online market which facilitates the exchange of commodities. For example, Alibaba is a marketplace for wholesale goods, and Airbnb is a marketplace for short-term real estate, primarily vacation rentals. What’s important here is that Alibaba and Airbnb don’t own any of the capital being traded on their marketplaces. Alibaba doesn't have any food or retail goods in their inventory, and Airbnb operates without owning a single property.
They’re both examples of ‘two-sided markets’. Two-sided markets are places for exchanging commodities. The markets themselves don’t own the commodities which are being traded. Rather, they’re a space for connecting the two ‘sides’: buyers to sellers, customers to vendors, demand to supply...you get the picture.
Data marketplaces are two-sided markets. There’s the data provider, who is looking to commercialize their data assets, and there’s the data buyer, who wants to find a data source which meets their requirements. Data marketplaces work to the benefit of both parties, which is why more companies are turning to them to unlock successful data strategies.
Let’s have a more detailed look at the factors which have catapulted data marketplaces to popularity as a data exchange solution.
Multiple factors come into play when we’re charting the rise of the data marketplace. In short, they’re an amazing solution to the problems often associated with accessing, managing and monetizing external data. And they provide a solution which is much needed: the external data industry reached $26 billion in value in 2019, and its value is only rising. Here’s why businesses are turning to data marketplaces to win their fair share from the this rapidly-expanding industry:
The Big Data market just keeps on growing. It reached $274 billion in value in 2022 (International Data Cooperation). This market growth has been caused by the exponential increase in data generation itself, attributed mainly to IoT and web scraping tools.
IoT, or the ‘Internet of Things’, refers to the growing network of physical assets which have digital twins or counterparts. Vehicles, buildings, headphones, TVs, wearable medical devices, power plants, weather sensors, AR glasses - these are all examples of the endless number of physical objects which make up one side of the Internet of Things. The other side to IoT is the information produced and captured in the software which makes these devices work. This information is being produced in a constant stream, making the IoT a source of vast, real-time data about different locations and the people in them. The IoT is such a significant source of data that there are IoT data marketplaces, exchanging intelligence sourced exclusively from IoT signals (we’ll look at IoT data marketplaces in more detail later).
Aside from IoT, the increase in web scraping technology has also contributed to the Big Data revolution. Web scraping tools are a way of crawling the internet for content which has been posted there on news outlets, search engine queries, social media platforms, video sharing sites, blogs, and ecommerce pages. This content is called ‘user-generated content’, and it can be separated into textual, visual, or audio content. Web scraping tools enable users to harness this data at scale, on demand. It’s estimated that, per minute, 500,000 Tweets are shared, 3.6 million Google searches processed, and 42 million Whatsapp messages shared (Domo, Statista). That’s a lot of user-generated content. Given that web scraping tools are capable of crawling vast amounts of it in seconds, it’s easy to see how web scraping has contributed to the Big Data boom.
So IoT and web scraping are part of what caused over 2.5 quintillion bytes of data to be created globally every day in 2017. This is only going to increase, with the total volume of data created worldwide expected to reach 149 zettabytes by 2045 (Domo).
Against the backdrop of this massive surge in data generation, data marketplaces bring three core benefits. Firstly, they empower individuals and organizations to monetize the proprietary data which they’re automatically producing. The IoT and user-generated content mean we’re leaving behind digital footprints rich in data. Data marketplaces allow individuals and businesses to profit from this internal data by making it externally available to purchase.
Secondly, data marketplaces give ordinary users the ability to navigate this complex data world. Data marketplaces are designed to be used by anyone, not just data scientists or analysts. The platforms themselves are often modelled on familiar ecommerce platforms. This is to give users a data shopping experience which is less daunting, and instead which is simple and similar to browsing and purchasing any other product online. And many of these ‘products’ - or datasets - exchanged on data marketplaces are analytics-ready, meaning they can be used instantly by any buyer.
And thirdly: what’s in it for the data marketplace? Well, the marketplace business model has the potential to thrive as a result of Big Data. By building a two-sided market, data marketplace owners can bring satisfaction to data vendors and buyers simultaneously and win the custom of both. Data marketplaces also have the potential to grow at scale, because, as we’ve explained, they don’t own data assets themselves. So a data marketplace business can profit from facilitating the exchange of Big Data without the costs of managing and storing it.
The value of external data for both businesses looking to buy data and businesses looking to sell it is becoming more and more apparent. At Datarade, we use the term ‘Data Capital’.
Just like how human and financial capital enable organizations to grow, Data Capital refers to external data, a resource which has the power to transform strategy and performance at countless organizations and businesses. Demand for external Data Capital is rising: 92% of firms agree that they need to increase their use of outside data sourcing. (Pitney Bowes). Why? Because enriching proprietary data with outside insights allows businesses to make more confident decisions, optimize their predictive models, boost productivity, and increase ROI. We’ll go into more specific examples of how exactly Data Capital is being put to work across organizations later. In short, more businesses and organizations need external data, and they’re turning to data marketplaces to supply much-needed data sourcing solutions.
What about businesses with Data Capital to sell and re-sell? For forward-looking businesses and enterprises, data monetization is becoming a secure and scalable source of revenue. And just like that, data-as-a-service - or DaaS - was born. Well, not ‘just like that’; data-as-a-service is a concept which developed over time since the beginning of the Big Data revolution. And it’s still a concept restricted mainly to tech spaces. But just as software-as-a-service (SaaS) started off as a techy niche, then exploded into verticals from retail to healthcare, so too will DaaS. What exactly does this mean? It means that, in coming years, any company could, and will, become a DaaS company by selling their internal assets. Let’s say you operate an online travel agency. Primarily, your business will be providing travel, accommodation and tourist attractions to consumers which book via your platform. However, you could add net-new revenue to your company by starting a DaaS company. There’s value in the masses of data generated as a by-product of your ordinary operational processes and internal systems. For example, travel sentiment data, Airbnb occupancy rate data, seasonality data, air traffic data and web activity data are all in-demand categories which an online travel agency captures by default.
Why let this data lie dormant in internal databases when there is hot customer demand for this intelligence? From travel companies to SATNAV manufacturers, organizations of all industries are realizing the value of their Data Capital. Data monetization is easier than ever thanks to data marketplaces. Data marketplaces allow these organizations to offer their internal data to external users via a privacy-assured, transaction-secure platform.
This brings us to a final, crucial reason for the popularity of data marketplaces. They make data sourcing simple and speedy, but without compromising on security. Verified data marketplaces require all users - that’s data providers and data buyers - to meet relevant KYC and security requirements.
This means that, for users to join a data marketplace, they have to comply with relevant legal and ethical regulations when it comes to data collection. For example, data providers have to prove that they source data in line with GDPR and CCPA regulations, and that they aggregate any PII (personally identifiable information) to project customer privacy. Only when a provider is vetted and approved can they begin selling data via a data marketplace. Some data marketplaces also have Blockchain integrations to ensure that all data streams exchanged over the platform are encrypted. These data marketplaces are referred to as ‘decentralized data marketplaces’.
Decentralized data marketplaces are powered by the blockchain so as to project the anonymity of users so that buying and selling data is safe for both parties. Likewise, data marketplaces often have ethical guidelines in place to block certain data purchases when the buyer intends to use the data for ‘blacklisted’ use cases.
Another security aspect which data marketplaces support is financial. Data marketplaces facilitate high-ticket data exchanges over a secure platform. Commercial datasets can cost $100,000s, so it’s easy to understand why businesses are cautious when it comes to paying for data from a source they haven’t used before. Data marketplaces bring trust to these transactions. They allow authentic buyers to connect with authentic providers and sign data sourcing agreements in confidence.
Accessing commercial datasets has remained notoriously difficult, with 50% of firms saying they encounter challenges when it comes to sourcing reliable and accurate data which fits their use case (Pitney Bowes). Data marketplaces remove the effort of finding the right data provider and bring transparency to the external data industry. They power trustworthy and efficient data commerce.
How exactly do they do this? What does ‘shopping for data’ on a data commerce platform look like?
On a basic level, data marketplaces work just like any other online market or ecommerce platform! Except on data marketplaces, instead of the product being a coat or a subscription to a movie streaming service, the product the buyer is buying on data marketplaces is data. Otherwise, most data marketplaces deliberately replicate the same shopping experience which you’d encounter on a retail website or movie streaming platform. They facilitate what’s known as data commerce, which follows broadly the same process as ecommerce, or retail commerce, or B2B commerce. This process can be broken into 5 basic commercial stages: Browse, Compare, Sample, Purchase, Review.
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The benefit of data marketplaces is that they host multiple data vendors and providers on one platform. With over 2000 data providers, Datarade’s data marketplace is the largest in the world. This variety gives buyers a better chance of finding the right data source. Data marketplaces can be used by vendors from across the world. Buyers can tap into this global network of data sources with ease, and get connected to providers they would otherwise probably never have discovered.
To mimic the commercial experience found on other marketplaces, data marketplaces have applied familiar concepts from B2C platforms to their platforms. For example, Datarade enables buyers to review data sellers based on their experience buying from them.
To optimize the browsing process, data marketplaces allow users to refine their search according to their particular data parameters. Just like how you’re able to put a price range for products on Amazon, or filter according to location on Airbnb, users can use data marketplaces to search for their specific data type, taking budget and geography into consideration. This way, data buyers can discover datasets with the right country coverage, at the right cost.
Data marketplaces allow data buyers to perform fast data provider comparison. All data marketplaces include an overview of a provider’s data offering, although the level of detail varies between platforms. Some data marketplaces require users to get in contact with data providers to find out more about their data solutions, whereas others make it easier for buyers to get an insight into a provider’s data capabilities.
Datarade empowers data providers to build their data storefront. This means that data vendors can showcase their data offering by listing Data Products, explain their competitive advantages, and put a spotlight on client success stories, reviews, and key certifications. For data providers, a great data storefront is key to data monetization. For data buyers, data storefronts allow them to easily benchmark providers and weigh up the pros and cons of different providers’ datasets.
Unlike other data sourcing methods, data marketplaces put a unique focus on the ‘compare’ stage of the buyer journey. This is because data marketplaces are independent platforms. By making it easy for data buyers to make objective comparisons between data vendors, there’s a higher chance that the buyer will find the data they need. Selling data isn’t a new phenomenon: data vendors have been trading for decades. What is new, however, is the unbiased platform which data marketplaces offer. In this sense, data marketplaces encourage a productive and transparent means of data exchange. The buyer is free to compare data providers. More importantly, the marketplace is there to give independent data sourcing advice if the buyer needs it.
Data marketplaces often build this consultancy service into their value proposition. This way, data vendors only receive lead referrals from qualified business opportunities. And buyers only receive suggestions for data sources and services if they actually match with their requirements.
The natural next step after comparison: try before you buy. Once a buyer has refined their search to one or two data provider candidates, in the majority of cases, they request a data sample from these candidates.
Before committing to a data purchase, it’s vital that buyers make sure that the external data they’re interested in works for their use case. This means that it must contain exactly the insights which will enable them to make data-driven decisions. Also, the external data must integrate with their organization's internal systems and software. With a data sample, analysts and data scientists can run tests to ensure that the dataset, data service, or API is suitable.
Data marketplaces make it easy to arrange sample sharing. Different features are available to make it as easy as possible to get a data sample and streamline the data sourcing process. Some data marketplaces offer instant sample downloads in CSV format. Others support on-platform communication between buyer and provider, so they can arrange the logistics of supplying data samples to the buyer quickly and securely.
Once a data buyer is happy that the data sample fits with their organization's software systems and that it will work for their use case, they can purchase the data. Commercial data is available in multiple different formats depending on the individual provider - some offer APIs, others offer databases in bulk via S3 drops, and others still provide constant data streams and feeds. We’ll look closer at the different formats and delivery options for external data later on.
What matters here is that the data is purchased from a vendor via the data marketplace. Typically, the data marketplace charges a commission fee for the service of connecting the provider to the buyer. There are also several possible payment options when buying external data:
Obviously, the legal nitty-gritty at the ‘purchase’ stage of the data shopping journey is important, especially for big deals and high-ticket data exchanges. Again, data marketplaces can help here. To ensure the data deal closes smoothly, data marketplaces offer support to both parties to finalize the transaction and sign data contracts. This is especially helpful for first-time data buyers, and for companies who haven’t monetized their data before. Other times there can be logistical issues, which data marketplaces, as global platforms with lots of experience, are able to resolve. For example, if the two companies use different currencies. Data marketplaces continue to offer independent advice throughout the data commerce experience to ensure both data buyer and seller are satisfied.
The final stage is where the data buyer has the opportunity to express their satisfaction! Buyer reviews influence any purchase decision, whether it’s for a hotel room or a pair of shoes. In this way, data marketplaces fulfil a key role in the external data industry: they invite buyers to review the data products they buy.
Part of the reason that the data industry is so notoriously intransparent is a lack of openly-available post-purchase feedback from authentic buyers. By inviting users to leave reviews, data marketplaces present a solution to that.
As more Data Capital emerges, buyers need to be sure that they’re choosing the right data sources. Data marketplaces enable them to see clearly which data providers rank highly for their data offering, quality and customer service. This makes for a simpler, more productive data shopping experience. And in a wider context, reviews left on data marketplaces are helping shape a positive future for the external data community. Reviews bring radical transparency. This encourages data providers to deliver the best data solutions and services possible so that they can win strong reviews and stand out from the competition.
So that’s the 5 basic stages of the data commerce experience. Obviously, when it comes to how each of these stages works in practice, there are differences between data platforms and the kind of data shopping experience they offer. To understand these, let’s have a look at some examples of data marketplaces, as well as alternative models for data exchange.
Data marketplaces can be split into three broad types, based on who they're designed for and where the data is sourced from. In this section, we'll go into the different kinds of data marketplaces. For specific examples of the best data marketplaces on the market in 2024, check out this comprehensive guide which lists 50+ data marketplaces.
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Personal data marketplaces are a recent phenomenon. Personal data marketplaces, like decentralized data marketplaces, were created in response to consumer dissatisfaction with their personal and sensitive data being used by tech firms, whilst the consumer received no commercial benefit. Personal data marketplaces allow consumers to get paid for sharing their data on a consent-managed basis.
This means that the individual agrees to share information about their online behavior and location with the personal data marketplace. The individual is paid for all of the information they share, and the data is collected on a consent-managed basis. Personal data marketplaces are another innovative way of shaping the external data industry for the better. Firstly, they’re making sure that individuals receive their fair share from the behavioral intelligence companies are collecting about them.
Secondly, they’re a driving force behind Data for Good. For certain use cases, like location mapping in emergencies and pandemics, personal data marketplaces supply vital insights about human movement and behavior. These insights help governments and aid organizations respond to the situation effectively, and at the same time, the individuals who agree to share their data are compensated by getting paid for it. They’re usually paid in the form of vouchers or gift cards for security purposes.
Examples of personal data marketplaces include Datum, SynapseAI, and Datawallet.
B2B (or ‘business-to-business’) data marketplaces are for data which is traded between businesses and organizations. Individuals sign up to the platform on behalf of the company they own or work for. B2B data marketplaces make up the majority of data marketplaces. Which makes sense: typically, it’s not individuals who want to buy data, but businesses. And Big Data collection is rarely done on an individual level, but by companies with lots of storage space in their technology stack. It’s these companies who want to monetize their data at scale because they have the supply to make it commercially-viable.
Here’s where B2B data marketplaces come in. They enable companies to meet their business and operational targets by making it easy to buy and sell data. The range of B2B marketplaces available all offer different specific advantages and services to businesses, which we’ll look at in more detail soon. Generally speaking, B2B data marketplaces are a demand generation platform for commercial data providers and SasS vendors. Data providers choose data marketplaces where it’s easy for them to integrate their data, or which require no integration at all. B2B data marketplaces operate according to various business models: sometimes providers have to subscribe and pay to list their data, others offer a free route for data monetization, where the vendor only pays when they sell their data successfully.
From the buy-side, businesses opt to source their data from B2B platforms over open-source alternatives because B2B marketplaces primarily offer analytics-grade data. These powerful datasets and APIs give companies the access to insights which their competitors likely won’t have, because they’re only available to purchase. For this reason, commercial datasets and APIs listed on a B2B data marketplace can be worth a lot of money. B2B buyers rely on business data marketplaces to help them source the right data for the right price.
Examples of B2B data marketplaces include, Datarade, Snowflake, AWS, Axon, Eagle Alpha, and Oracle.
Internal Data MarketplacesIn light of the need for enhanced analytics, many enterprises have developed internal data marketplaces and catalogs. These are accessible to the enterprise's employees and customers. Users simply navigate the marketplace, select the right data for their project, and the information and attached metadata are ready to use. Enterprises with internal data marketplaces include Alation, which runs Alation Marketplaces, and SAP, which runs SAP Datasphere Marketplace. Data providers can list their data in both of these internal data marketplaces to connect with in-market buyers from Alation and SAP.
The easiest way to do so is by joining DCC, which enables you to join both marketplaces with one account - without the hassle of joining each one separately. Although internal data marketplaces aren't seen by the masses, they boast a high conversion rate because anyone using them already has a clear use case in mind and is relatively far down the sales funnel, ready to use external data for their analytics.
We’ve already looked at what the IoT is - a network of devices which constantly emit sensors and generate data. An IoT data marketplace is a platform for buying and selling the intelligence produced by the IoT. The data available to buy from an IoT marketplace is sourced exclusively from this web of interconnected devices, giving buyers real-time signals from millions of digital touchpoints.
IoT data marketplaces are created with the aim of unifying the streams of information being produced on a global scale all the time. When harnessed and structured, these data streams can yield significant insights into consumer behavior, online trends, and technological developments. IoT marketplaces are typically very accessible, with flexible pricing options like ‘pay-per-hour’, in line with the open network which this kind of data marketplace supports.
Monetizing data via an IoT data marketplace is an attractive option for many businesses. As companies increase the volume and quality of hardware in their technology stack, more and more data is being produced as a by-product. These by-products, the ‘digital twins’ we discussed earlier, are a valuable data asset. By listing IoT-sourced data, companies can tap into a simple stream of revenue by utilizing the products they already use.
Examples of IoT data marketplaces include IOTA Data Market and Streamr.
As we’ve seen, there are four core types of data marketplace, with dozens of companies operating marketplaces within each category. That’s a lot of data marketplaces. The dilemma for data providers is syncing to all of them at once. Or at least prioritising which to sync to in order to capture the most demand. The overhead that comes with integrating products to multiple data markepltaces, then managing each of these separate sales channels, is huge. Often, it’s unviable for young DaaS companies to build such an omni-channel business.
To eliminate this problem, data providers can use solutions like Data Commerce Cloud™ (DCC). DCC enables providers to synchronize with multiple data marketplaces - and other kinds of data exchange platforms, which we’ll look into below - with one single account. So providers only need to set up their storefront and list data products once, as opposed to starting the listing process from scratch for each data marketplace. It’s a SaaS solution for data commerce that solves many of the problems which Shopify solves for retail and ecommerce. Namely, managing all of your stock, leads, transactions, and orders across every different sales channel.
Let’s say you’re an online seller making homemade candles. Your business is a one-woman show. To reach the most amount of buyers globally, you’d want to be present on Amazon, Ebay, Etsy, ASOS, Zalando, vinted, and perhaps other retail outlets. It would soon get very difficult for you to process every order you receive from each of these channels, especially because the commission fees and even currency you owe will probably vary between each platform. Shopify alleviates this logistical and financial stress. You can maximize your business’ visibility with an effective omni-channel strategy, whilst managing everything centrally in your Shopify seller account.
DCC works in the same way, for data providers. With DCC, all activity from disparate marketplaces is centralized in your DCC account. This saves an incredible amount of time and resources, especially for younger DaaS players! Data commerce solutions like DCC will become more important as the number of data marketplaces increases, alongside a whole host of other data sourcing and fulfillment platforms.
What about these other platforms designed for data exchange? Let’s see how they weigh up to the data marketplace model as a solution for providers and buyers.
Welcome to Datarade’s 2024 edition of ‘what’s the difference between’. There are lots of different sites and services which aim to make buying and selling Data Capital more rewarding. Each is slightly different to a data marketplace, and each has its pros and cons:
Data streaming platforms are great for data buyers who need access to on-demand, real-time data feeds. Like data marketplaces, they work with third-party vendors to make external data commercially-available to users across the world. So they’re an amazing fresh data source, especially for businesses who need instant, live data via a flexible, easy-to-use platform.
The issue with data streaming platforms lies on the provider-side. Data streaming requires integration with the platform as a host site, which can be a lengthy and complex process for an organization whose primary business is not selling data. This means data streaming platforms popular amongst companies with the primary business of selling data, but for organizations who just want to monetize their proprietary data, the time and money cost of integrating with a data streaming site sometimes just isn’t worth it. Integration is not a prerequisite for data marketplaces: some offer it, some don’t. This gives providers the freedom to sell their data their way, using the software they already have, with minimal added time or financial cost.
Examples of data streaming platforms include Narrative.io.
The big pro of data warehousing is that it produces analytics-ready insights. In other words, the mature is mature and primed-for business usage, in comparison to raw or open-source insights which could be found in datasets elsewhere. They’re also more established than data marketplaces, meaning it’s possible that users trust data warehouses more. However, the data held in data warehouses can only answer business-specific questions, and even then, the BI they provide does not include key technical information or contextual metadata. This means that data warehouses aren’t always a great source of external data, because their Big Data capabilities are so limited.
For data marketplaces, this isn’t an issue. The range of data available from vendors on data marketplaces spans all categories of Big Data - there are over 300 different data categories on Datarade. Likewise, with blockchain advancements, data marketplaces operate just as securely as a data warehouse.
Data lakes are huge repositories of data gathered using IoT and machine signals, proprietary company data, records, and CRM databases. The trouble with data lakes is that, although the volume of data they offer is vast, it can often take so much time and effort to differentiate the valuable data from the irrelevant that it ends up costing companies time and money instead of saving. Identifying and analysing data from data lakes is also a complex task which requires experienced data analytics and science.
In comparison, data marketplaces are designed for the everyman. Businesses of all sizes and with any level of experience can use data marketplaces for rewarding and efficient data shopping experiences. And increasingly, data marketplaces are offering datasets and APIs just as granular and powerful as the information kept in a data lake, but that are actionable without having to undergo complex structuring and cleansing before they can be analyzed.
A data silo is similar to a data lake, and presents many of the same challenges. Essentially, data silos store masses of internal data for companies and organizations globally. There is potential to derive rich insights by data mining these silos. However, they’re poor data management systems because, like data lakes, it’s difficult to identify and analyze data points from a data silo. Data silos don’t provide actionable data-driven solutions, and often the data they contain is no longer fresh.
Data marketplaces offer an alternative to data silos which ensures the user gets fresh data without the need for extensive data mining. SaaS vendors may use data silos as a source for some of their insights, but before they list these data products on a data marketplace, they will carry out validation and data enrichment processes. So the APIs and datasets available on data marketplaces still offer rich insights, like you’d get from a data lake. But on top of that, data vendors strip away the irrelevant data points to offer up-to-date, analytics-ready datasets, so that the buyer gets exactly the data they need. Again, data marketplaces provide a fast and accessible solution for buyers to tap into external data and start putting it to work.
Data clouds are another solution aimed at harnessing the data stored in lakes and silos. A data cloud enables users to exchange, unify and monetize data from lakes and silos. Data clouds form a global network of SaaS providers with data to share: the result is democratized access to external data for organizations across the world. Data cloud exchanges are a much more powerful data storage solution than data warehouses, silos, or lakes, because they’re updated in real-time thanks to data feeds and streams from every data provider which is integrated. As a result, users can access valuable, fresh data on-demand via a single platform.
Like with data streaming platforms, however, data clouds can present integration challenges. As the data exchange industry develops, data clouds and streaming sites are working hard to rectify this and make it as painless as possible to add Big Data to their platforms to start monetizing it. At the moment, however, SaaS vendors and data providers are still reporting integration issues with these sites. Data marketplaces don’t require any integration. So companies of any size and SaaS experience can set up their data storefront on data marketplaces like Datarade and begin selling their data instantly and securely.
Examples of data clouds include Snowflake and AWS Data Exchange.
Let’s now look at the two sides of the data marketplace in more detail. First: sell-side. Why do data vendors sell their data via data marketplaces? To understand this, we first need to understand data monetization.
Data monetization is a way of companies increasing their revenue by selling their intangible data assets. The fastest-growing companies have adopted a data monetization strategy and are reaping the commercial benefits (McKinsey & Co.).
For the most successful data monetization initiatives, organizations first have to define the value of their in-house data. What kind of data is it? What insights does the database yield? Who would buy the data? How much would they pay for it? Once these questions have been answered by conducting competitive market research, the next step in data monetization is for companies to select the right data monetization platform.
Studies show that companies which take advantage of next-generation data marketplaces will gain a competitive digital edge (Pitney Bowes), because data marketplaces are increasingly being considered the best demand generation platform and the easiest route into data commercialization and monetization.
For companies looking to launch their data monetization strategy, data markeptlaces offer a number of unique advantages:
Data marketplaces offer unrivalled demand generation potential. They attract data buyers from all over the world, working for the most valuable global companies. For SaaS data vendors, this means the opportunity to tap into a new lead pool which includes some of the world’s largest corporates, brands, and financial institutions.
When they join a data marketplace, data providers can continue to sell their data in the way that suits them. Data marketplaces don’t require complex integration or third-party brokers, meaning data vendors can keep their data monetization strategy in place, but with the added visibility and lead generation potential which a data marketplace provides.
Data marketplaces welcome all kinds of data providers, from established SaaS companies, to startups looking to use their data as a new revenue source. As independent platforms, data marketplaces are ‘vendor agnostic’. As long as providers meet the data marketplace’s relevant security measures, can prove they have valid licenses to provide data, and that they abide by data privacy legislation including GDPR and CCPA, they can list their data.
Each marketplace has its own listing procedure. However, we can sketch out a general process which all potential data providers go through to list their data on marketplaces and start selling.
Step 1) KYC
Every data marketplace will run KYC checks on new companies requesting to be listed. This is for due diligence purposes: they need to check that the provider company is legitimate and isn't involved in any criminal activity. Especially when it comes to the external data industry, it's crucial that marketplaces vet their providers to ensure that the data is sourced in a legal, privacy-compliant manner. KYC could also include researching media coverage of the provider to identify any negative press, so as to avoid a PR mishap further down the line.
Other times, KYC is simply to ascertain whether the prospect is suitable for the data marketplace in question. For example, you could run an established data business selling datasets to university students. After performing KYC checks on your company, a B2B data marketplace wouldn't allow you to be listed. This is because you're B2C, not B2B, so your business isn't in line with the marketplace's business model and policy. For the most part, data marketplaces are fast when it comes to conducting KYC checks. The step after you're approved is signing the marketplace's commercial agreement.
Step 2) Signing the marketplace’s commercial agreement
Data marketplaces can run using several different business models. Some operate a revenue share, whereby they take a commission whenever providers close a deal successfully via the marketplace. Others offer subscriptions plans, whereby providers pay (usually monthly or annually) to appear on the marketplace. However the marketplace’s commercial model works, the main thing is that providers sign a contract agreeing to its usage and payment terms. Once this crucial step is complete, providers can start listing their data products on the marketplace.
Step 3) Listing data products
Again, the process for listing data products varies from marketplace to marketplace. What's notable about Datarade Marketplace is that there's no data integration required for providers to create listings. The listing process requires you to give each product a title, description, coverage, and attach a sample. Otherwise, there's no painful upload or syncing needed. This is more efficient and secure than having to upload your data offering to a data marketplace where it's hosted externally and needs to be replaced whenever there's a change to your dataset. The listing process for AWS, for example, is a lot lengthier because each listing requires that you sync your data to the platform anew each time.
Another solution provided by Data Commerce Cloud™ is one-time product listing. Once you've created listings once, you're able to sync them to marketplaces of your choosing instantly. This saves providers the hassle of navigating new listing processes for every new channel they join.
At a general level, that’s how to list products on a data marketplace. But who exactly is doing so? In other words, what kind of businesses and organizations will you find listed on a data marketplace? Let’s have a look at some examples of the data providers which are realizing the potential of marketplaces for data monetization in 2024.
Companies selling data are most commonly known as data providers or vendors. Companies which have made data their primary revenue stream are often called data-as-a-service (or DaaS) companies. DaaS companies have created a business from selling data “as-a-service” - that is, on-demand and tailored to clients and organizations looking to buy data.
There are thousands of data providers in the external data market. Some data providers, like Alesco Data, are businesses where the primary product and service is to provide a data sourcing solution. Others have other primary business focusses, like Nikkei, a news outlet for Japanese financial markets. Both companies use data marketplaces like Datarade to monetize their data, showcase their datasets, and reach clients around the world.
As the name suggests, big data is vast in terms of the categories comprising it. There are over 600 data categories listed on Datarade Marketplace, with more emerging constantly as technology advances and new kinds of data is generated and collected.
All the same, within data commerce, demand for data is mostly centered around seven core categories: geospatial data, commerce data, financial market data, company data, real estate data, web data, and AI & ML training data. It’s companies providing these categories of data which you’re likely to recognise as established industry players. So that’s what data marketplaces bring to the sell-side. Now let’s see what they offer data buyers.
The first question to answer here: why buy external data in the first place?
Businesses buy external data to improve their decisions. With external data enrichment, businesses can make confident decisions about marketing, sales and BI. Buying external datasets and APIs empowers these companies to build predictive models which give them the edge over competitors and increase ROI. For more detailed information about how data can streamline all kinds of decision making, discover the different use cases for external data.
Now back to our original question. How do data marketplaces facilitate data sourcing?
Data sourcing is the process of researching, sampling, testing, and finally buying data. It usually refers to external data acquisition, where companies (and sometimes individuals) source data from outside of their proprietary databases and records. Data sourcing can require a lot of time and research, because it's crucial that data buyers invest in exactly the right data for their use case, and that they acquire this data at the right price. Otherwise, from an ROI perspective, they could end up buying data which doesn't contribute to their strategies and success. Either because the insights in the dataset aren't suited to the buyer, or because the data sourcing cost too much time (and money!).
Part of the role of the data marketplace is to make data sourcing more efficient and less complicated. Traditionally, data hunting has been made difficult because it's hard for inexperienced buyers to know which providers can supply the right datasets, and because there's a often lack of transparency in the external data industry. Data marketplaces aim to rectify these pain points associated with data sourcing. They carry out vendor KYC so that buyers don't have to conduct such lengthy verification procedures for each data source. Data marketplaces also ensure that buyers can connect with a range of trusted providers at once, meaning buyers can have multiple active avenues for data procurement. This way, the chances of them finding the best data are higher.
Many data marketplaces also provide data partnerships as a data sourcing solution. These data partnerships are a kind of on-going consultancy service offered by vendors via the marketplace. The buyer begins by briefing the provider on their data acquisition requirements: what data they need, when they need it by, and how much they can pay for the data. The providers then source the data, and then structure their datasets to allow the buyer to get actionable insights without the lengthy analytics. These data sourcing solutions are part of how data marketplaces democratize access to external data: buyers don’t have to be data scientists to use data marketplaces. They’re able to invest in data sourcing and consultation service to help them find the right data according to their use case, budget, and time demands. The aim of data sourcing is that the user is able to buy a data product in the right, readable format for them and their business’ internal software systems, without the hassle of independent data hunting.
Data sourcing is on the rise. One study found that the majority of companies surveyed already source data externally, and that over half of these plan to increase how much they spend on data marketplaces over the next three years. 2 of 5 of these reported that they'll spend over $10,000 on external data marketplaces (Pitney Bowes).
Data marketplaces remove other buyer pain points associated with data sourcing. Privacy regulations and blockchain technology mean that buyers can access encrypted datasets and APIs securely. They can connect with multiple global providers at once via one platform. It’s fast, and overcomes logistical pain points such as communication with providers who are in different time zones or who use different currencies. Arranging data samples is also easy with data marketplaces thanks to features like in-platform messaging. Most importantly, the data buyer can put the data products they’ve sourced to work instantly.
What do these data products actually look like? And how does the buyer access them?
Data from a data marketplace is available to buy in different formats. Depending on when the buyer needs the data and the data management system they use, vendors can tailor the delivery format and availability to the buyer. Data exchange via a B2B data marketplace usually occurs via two delivery methods:
SaaS vendors can deliver data from endpoint to endpoint to buyers via API. APIs give users access to real-time data feeds and streams which are received by their company’s servers. Buyers who need fresh data can purchase an API subscription to ensure they receive constant communication and information from their data vendor. Most data products available from a data marketplace are available for delivery in API format.
For buyers who want to make a one-off data purchase, S3 buckets are ideal. Whereas APIs relay real-time data, S3 buckets tend to contain historical data points. Data vendors can share complete and custom datasets and databases for a fixed price via an S3 bucket. The buyer then has unlimited access and ownership of an analysis-ready dataset. These transactions are carried out efficiently thanks to data marketplaces and exchange platforms.
Of course, there are alternative options for data exchange. The beauty of data marketplaces is that vendors and buyers are free to arrange a data sourcing solution which works for both parties. Although API and S3 are amongst the most common methods, data marketplaces don’t restrict users. Data pricing, delivery and availability can be tailored to the use case and the customer’s requirements.
We’ve looked at how data is sent from the data vendor to the buyer via a B2B marketplace.
This brings us to the final part of the Ultimate Guide to Data Marketplaces 2024: the kinds of data you can buy from them.
The Big Data revolution has meant that we can now access intelligence spanning hundreds of different data categories. Start browsing Datarade’s data marketplace to discover data from over 600 categories, sourced from a network of over 2000 providers.
Wondering whether your company is a fit to sell via data marketplaces? Whether your business is specifically a SaaS company, or you have in-house data to commercialize, discover how you can list your data on Datarade’s data marketplace, as well as Databricks Marketplace, SAP Datasphere Marketplace, and Google Cloud Analytics Hub, with one DCC account. Learn more →
Data marketplaces are soaring in popularity. They're a one-stop-shop for buying and selling external data. For data buyers, they make data sourcing effortless. For data providers, they maximize data monetization.
There are constantly new data exchange platforms being created: for different data types, different users, and different industries.
Our Ultimate Guide to the Data Marketplace tells you everything you need to know about the best data marketplaces available today. You'll learn about the difference between data exchanges, who's using them, and why data marketplaces are the most powerful method of data exchange in 2024.
A data marketplace is a platform where users can buy and sell data. A data marketplace is an easy way for data providers to market, manage and sell their data. In turn, data marketplaces allow data buyers to browse, compare and purchase data from multiple sources collected in one, easy-to-navigate marketplace.
A data marketplace works in the same way as any other online market which facilitates the exchange of commodities. For example, Alibaba is a marketplace for wholesale goods, and Airbnb is a marketplace for short-term real estate, primarily vacation rentals. What’s important here is that Alibaba and Airbnb don’t own any of the capital being traded on their marketplaces. Alibaba doesn't have any food or retail goods in their inventory, and Airbnb operates without owning a single property.
They’re both examples of ‘two-sided markets’. Two-sided markets are places for exchanging commodities. The markets themselves don’t own the commodities which are being traded. Rather, they’re a space for connecting the two ‘sides’: buyers to sellers, customers to vendors, demand to supply...you get the picture.
Data marketplaces are two-sided markets. There’s the data provider, who is looking to commercialize their data assets, and there’s the data buyer, who wants to find a data source which meets their requirements. Data marketplaces work to the benefit of both parties, which is why more companies are turning to them to unlock successful data strategies.
Let’s have a more detailed look at the factors which have catapulted data marketplaces to popularity as a data exchange solution.
Multiple factors come into play when we’re charting the rise of the data marketplace. In short, they’re an amazing solution to the problems often associated with accessing, managing and monetizing external data. And they provide a solution which is much needed: the external data industry reached $26 billion in value in 2019, and its value is only rising. Here’s why businesses are turning to data marketplaces to win their fair share from the this rapidly-expanding industry:
The Big Data market just keeps on growing. It reached $274 billion in value in 2022 (International Data Cooperation). This market growth has been caused by the exponential increase in data generation itself, attributed mainly to IoT and web scraping tools.
IoT, or the ‘Internet of Things’, refers to the growing network of physical assets which have digital twins or counterparts. Vehicles, buildings, headphones, TVs, wearable medical devices, power plants, weather sensors, AR glasses - these are all examples of the endless number of physical objects which make up one side of the Internet of Things. The other side to IoT is the information produced and captured in the software which makes these devices work. This information is being produced in a constant stream, making the IoT a source of vast, real-time data about different locations and the people in them. The IoT is such a significant source of data that there are IoT data marketplaces, exchanging intelligence sourced exclusively from IoT signals (we’ll look at IoT data marketplaces in more detail later).
Aside from IoT, the increase in web scraping technology has also contributed to the Big Data revolution. Web scraping tools are a way of crawling the internet for content which has been posted there on news outlets, search engine queries, social media platforms, video sharing sites, blogs, and ecommerce pages. This content is called ‘user-generated content’, and it can be separated into textual, visual, or audio content. Web scraping tools enable users to harness this data at scale, on demand. It’s estimated that, per minute, 500,000 Tweets are shared, 3.6 million Google searches processed, and 42 million Whatsapp messages shared (Domo, Statista). That’s a lot of user-generated content. Given that web scraping tools are capable of crawling vast amounts of it in seconds, it’s easy to see how web scraping has contributed to the Big Data boom.
So IoT and web scraping are part of what caused over 2.5 quintillion bytes of data to be created globally every day in 2017. This is only going to increase, with the total volume of data created worldwide expected to reach 149 zettabytes by 2045 (Domo).
Against the backdrop of this massive surge in data generation, data marketplaces bring three core benefits. Firstly, they empower individuals and organizations to monetize the proprietary data which they’re automatically producing. The IoT and user-generated content mean we’re leaving behind digital footprints rich in data. Data marketplaces allow individuals and businesses to profit from this internal data by making it externally available to purchase.
Secondly, data marketplaces give ordinary users the ability to navigate this complex data world. Data marketplaces are designed to be used by anyone, not just data scientists or analysts. The platforms themselves are often modelled on familiar ecommerce platforms. This is to give users a data shopping experience which is less daunting, and instead which is simple and similar to browsing and purchasing any other product online. And many of these ‘products’ - or datasets - exchanged on data marketplaces are analytics-ready, meaning they can be used instantly by any buyer.
And thirdly: what’s in it for the data marketplace? Well, the marketplace business model has the potential to thrive as a result of Big Data. By building a two-sided market, data marketplace owners can bring satisfaction to data vendors and buyers simultaneously and win the custom of both. Data marketplaces also have the potential to grow at scale, because, as we’ve explained, they don’t own data assets themselves. So a data marketplace business can profit from facilitating the exchange of Big Data without the costs of managing and storing it.
The value of external data for both businesses looking to buy data and businesses looking to sell it is becoming more and more apparent. At Datarade, we use the term ‘Data Capital’.
Just like how human and financial capital enable organizations to grow, Data Capital refers to external data, a resource which has the power to transform strategy and performance at countless organizations and businesses. Demand for external Data Capital is rising: 92% of firms agree that they need to increase their use of outside data sourcing. (Pitney Bowes). Why? Because enriching proprietary data with outside insights allows businesses to make more confident decisions, optimize their predictive models, boost productivity, and increase ROI. We’ll go into more specific examples of how exactly Data Capital is being put to work across organizations later. In short, more businesses and organizations need external data, and they’re turning to data marketplaces to supply much-needed data sourcing solutions.
What about businesses with Data Capital to sell and re-sell? For forward-looking businesses and enterprises, data monetization is becoming a secure and scalable source of revenue. And just like that, data-as-a-service - or DaaS - was born. Well, not ‘just like that’; data-as-a-service is a concept which developed over time since the beginning of the Big Data revolution. And it’s still a concept restricted mainly to tech spaces. But just as software-as-a-service (SaaS) started off as a techy niche, then exploded into verticals from retail to healthcare, so too will DaaS. What exactly does this mean? It means that, in coming years, any company could, and will, become a DaaS company by selling their internal assets. Let’s say you operate an online travel agency. Primarily, your business will be providing travel, accommodation and tourist attractions to consumers which book via your platform. However, you could add net-new revenue to your company by starting a DaaS company. There’s value in the masses of data generated as a by-product of your ordinary operational processes and internal systems. For example, travel sentiment data, Airbnb occupancy rate data, seasonality data, air traffic data and web activity data are all in-demand categories which an online travel agency captures by default.
Why let this data lie dormant in internal databases when there is hot customer demand for this intelligence? From travel companies to SATNAV manufacturers, organizations of all industries are realizing the value of their Data Capital. Data monetization is easier than ever thanks to data marketplaces. Data marketplaces allow these organizations to offer their internal data to external users via a privacy-assured, transaction-secure platform.
This brings us to a final, crucial reason for the popularity of data marketplaces. They make data sourcing simple and speedy, but without compromising on security. Verified data marketplaces require all users - that’s data providers and data buyers - to meet relevant KYC and security requirements.
This means that, for users to join a data marketplace, they have to comply with relevant legal and ethical regulations when it comes to data collection. For example, data providers have to prove that they source data in line with GDPR and CCPA regulations, and that they aggregate any PII (personally identifiable information) to project customer privacy. Only when a provider is vetted and approved can they begin selling data via a data marketplace. Some data marketplaces also have Blockchain integrations to ensure that all data streams exchanged over the platform are encrypted. These data marketplaces are referred to as ‘decentralized data marketplaces’.
Decentralized data marketplaces are powered by the blockchain so as to project the anonymity of users so that buying and selling data is safe for both parties. Likewise, data marketplaces often have ethical guidelines in place to block certain data purchases when the buyer intends to use the data for ‘blacklisted’ use cases.
Another security aspect which data marketplaces support is financial. Data marketplaces facilitate high-ticket data exchanges over a secure platform. Commercial datasets can cost $100,000s, so it’s easy to understand why businesses are cautious when it comes to paying for data from a source they haven’t used before. Data marketplaces bring trust to these transactions. They allow authentic buyers to connect with authentic providers and sign data sourcing agreements in confidence.
Accessing commercial datasets has remained notoriously difficult, with 50% of firms saying they encounter challenges when it comes to sourcing reliable and accurate data which fits their use case (Pitney Bowes). Data marketplaces remove the effort of finding the right data provider and bring transparency to the external data industry. They power trustworthy and efficient data commerce.
How exactly do they do this? What does ‘shopping for data’ on a data commerce platform look like?
On a basic level, data marketplaces work just like any other online market or ecommerce platform! Except on data marketplaces, instead of the product being a coat or a subscription to a movie streaming service, the product the buyer is buying on data marketplaces is data. Otherwise, most data marketplaces deliberately replicate the same shopping experience which you’d encounter on a retail website or movie streaming platform. They facilitate what’s known as data commerce, which follows broadly the same process as ecommerce, or retail commerce, or B2B commerce. This process can be broken into 5 basic commercial stages: Browse, Compare, Sample, Purchase, Review.
The benefit of data marketplaces is that they host multiple data vendors and providers on one platform. With over 2000 data providers, Datarade’s data marketplace is the largest in the world. This variety gives buyers a better chance of finding the right data source. Data marketplaces can be used by vendors from across the world. Buyers can tap into this global network of data sources with ease, and get connected to providers they would otherwise probably never have discovered.
To mimic the commercial experience found on other marketplaces, data marketplaces have applied familiar concepts from B2C platforms to their platforms. For example, Datarade enables buyers to review data sellers based on their experience buying from them.
To optimize the browsing process, data marketplaces allow users to refine their search according to their particular data parameters. Just like how you’re able to put a price range for products on Amazon, or filter according to location on Airbnb, users can use data marketplaces to search for their specific data type, taking budget and geography into consideration. This way, data buyers can discover datasets with the right country coverage, at the right cost.
Data marketplaces allow data buyers to perform fast data provider comparison. All data marketplaces include an overview of a provider’s data offering, although the level of detail varies between platforms. Some data marketplaces require users to get in contact with data providers to find out more about their data solutions, whereas others make it easier for buyers to get an insight into a provider’s data capabilities.
Datarade empowers data providers to build their data storefront. This means that data vendors can showcase their data offering by listing Data Products, explain their competitive advantages, and put a spotlight on client success stories, reviews, and key certifications. For data providers, a great data storefront is key to data monetization. For data buyers, data storefronts allow them to easily benchmark providers and weigh up the pros and cons of different providers’ datasets.
Unlike other data sourcing methods, data marketplaces put a unique focus on the ‘compare’ stage of the buyer journey. This is because data marketplaces are independent platforms. By making it easy for data buyers to make objective comparisons between data vendors, there’s a higher chance that the buyer will find the data they need. Selling data isn’t a new phenomenon: data vendors have been trading for decades. What is new, however, is the unbiased platform which data marketplaces offer. In this sense, data marketplaces encourage a productive and transparent means of data exchange. The buyer is free to compare data providers. More importantly, the marketplace is there to give independent data sourcing advice if the buyer needs it.
Data marketplaces often build this consultancy service into their value proposition. This way, data vendors only receive lead referrals from qualified business opportunities. And buyers only receive suggestions for data sources and services if they actually match with their requirements.
The natural next step after comparison: try before you buy. Once a buyer has refined their search to one or two data provider candidates, in the majority of cases, they request a data sample from these candidates.
Before committing to a data purchase, it’s vital that buyers make sure that the external data they’re interested in works for their use case. This means that it must contain exactly the insights which will enable them to make data-driven decisions. Also, the external data must integrate with their organization's internal systems and software. With a data sample, analysts and data scientists can run tests to ensure that the dataset, data service, or API is suitable.
Data marketplaces make it easy to arrange sample sharing. Different features are available to make it as easy as possible to get a data sample and streamline the data sourcing process. Some data marketplaces offer instant sample downloads in CSV format. Others support on-platform communication between buyer and provider, so they can arrange the logistics of supplying data samples to the buyer quickly and securely.
Once a data buyer is happy that the data sample fits with their organization's software systems and that it will work for their use case, they can purchase the data. Commercial data is available in multiple different formats depending on the individual provider - some offer APIs, others offer databases in bulk via S3 drops, and others still provide constant data streams and feeds. We’ll look closer at the different formats and delivery options for external data later on.
What matters here is that the data is purchased from a vendor via the data marketplace. Typically, the data marketplace charges a commission fee for the service of connecting the provider to the buyer. There are also several possible payment options when buying external data:
Obviously, the legal nitty-gritty at the ‘purchase’ stage of the data shopping journey is important, especially for big deals and high-ticket data exchanges. Again, data marketplaces can help here. To ensure the data deal closes smoothly, data marketplaces offer support to both parties to finalize the transaction and sign data contracts. This is especially helpful for first-time data buyers, and for companies who haven’t monetized their data before. Other times there can be logistical issues, which data marketplaces, as global platforms with lots of experience, are able to resolve. For example, if the two companies use different currencies. Data marketplaces continue to offer independent advice throughout the data commerce experience to ensure both data buyer and seller are satisfied.
The final stage is where the data buyer has the opportunity to express their satisfaction! Buyer reviews influence any purchase decision, whether it’s for a hotel room or a pair of shoes. In this way, data marketplaces fulfil a key role in the external data industry: they invite buyers to review the data products they buy.
Part of the reason that the data industry is so notoriously intransparent is a lack of openly-available post-purchase feedback from authentic buyers. By inviting users to leave reviews, data marketplaces present a solution to that.
As more Data Capital emerges, buyers need to be sure that they’re choosing the right data sources. Data marketplaces enable them to see clearly which data providers rank highly for their data offering, quality and customer service. This makes for a simpler, more productive data shopping experience. And in a wider context, reviews left on data marketplaces are helping shape a positive future for the external data community. Reviews bring radical transparency. This encourages data providers to deliver the best data solutions and services possible so that they can win strong reviews and stand out from the competition.
So that’s the 5 basic stages of the data commerce experience. Obviously, when it comes to how each of these stages works in practice, there are differences between data platforms and the kind of data shopping experience they offer. To understand these, let’s have a look at some examples of data marketplaces, as well as alternative models for data exchange.
Data marketplaces can be split into three broad types, based on who they're designed for and where the data is sourced from. In this section, we'll go into the different kinds of data marketplaces. For specific examples of the best data marketplaces on the market in 2024, check out this comprehensive guide which lists 50+ data marketplaces.
Personal data marketplaces are a recent phenomenon. Personal data marketplaces, like decentralized data marketplaces, were created in response to consumer dissatisfaction with their personal and sensitive data being used by tech firms, whilst the consumer received no commercial benefit. Personal data marketplaces allow consumers to get paid for sharing their data on a consent-managed basis.
This means that the individual agrees to share information about their online behavior and location with the personal data marketplace. The individual is paid for all of the information they share, and the data is collected on a consent-managed basis. Personal data marketplaces are another innovative way of shaping the external data industry for the better. Firstly, they’re making sure that individuals receive their fair share from the behavioral intelligence companies are collecting about them.
Secondly, they’re a driving force behind Data for Good. For certain use cases, like location mapping in emergencies and pandemics, personal data marketplaces supply vital insights about human movement and behavior. These insights help governments and aid organizations respond to the situation effectively, and at the same time, the individuals who agree to share their data are compensated by getting paid for it. They’re usually paid in the form of vouchers or gift cards for security purposes.
Examples of personal data marketplaces include Datum, SynapseAI, and Datawallet.
B2B (or ‘business-to-business’) data marketplaces are for data which is traded between businesses and organizations. Individuals sign up to the platform on behalf of the company they own or work for. B2B data marketplaces make up the majority of data marketplaces. Which makes sense: typically, it’s not individuals who want to buy data, but businesses. And Big Data collection is rarely done on an individual level, but by companies with lots of storage space in their technology stack. It’s these companies who want to monetize their data at scale because they have the supply to make it commercially-viable.
Here’s where B2B data marketplaces come in. They enable companies to meet their business and operational targets by making it easy to buy and sell data. The range of B2B marketplaces available all offer different specific advantages and services to businesses, which we’ll look at in more detail soon. Generally speaking, B2B data marketplaces are a demand generation platform for commercial data providers and SasS vendors. Data providers choose data marketplaces where it’s easy for them to integrate their data, or which require no integration at all. B2B data marketplaces operate according to various business models: sometimes providers have to subscribe and pay to list their data, others offer a free route for data monetization, where the vendor only pays when they sell their data successfully.
From the buy-side, businesses opt to source their data from B2B platforms over open-source alternatives because B2B marketplaces primarily offer analytics-grade data. These powerful datasets and APIs give companies the access to insights which their competitors likely won’t have, because they’re only available to purchase. For this reason, commercial datasets and APIs listed on a B2B data marketplace can be worth a lot of money. B2B buyers rely on business data marketplaces to help them source the right data for the right price.
Examples of B2B data marketplaces include, Datarade, Snowflake, AWS, Axon, Eagle Alpha, and Oracle.
Internal Data MarketplacesIn light of the need for enhanced analytics, many enterprises have developed internal data marketplaces and catalogs. These are accessible to the enterprise's employees and customers. Users simply navigate the marketplace, select the right data for their project, and the information and attached metadata are ready to use. Enterprises with internal data marketplaces include Alation, which runs Alation Marketplaces, and SAP, which runs SAP Datasphere Marketplace. Data providers can list their data in both of these internal data marketplaces to connect with in-market buyers from Alation and SAP.
The easiest way to do so is by joining DCC, which enables you to join both marketplaces with one account - without the hassle of joining each one separately. Although internal data marketplaces aren't seen by the masses, they boast a high conversion rate because anyone using them already has a clear use case in mind and is relatively far down the sales funnel, ready to use external data for their analytics.
We’ve already looked at what the IoT is - a network of devices which constantly emit sensors and generate data. An IoT data marketplace is a platform for buying and selling the intelligence produced by the IoT. The data available to buy from an IoT marketplace is sourced exclusively from this web of interconnected devices, giving buyers real-time signals from millions of digital touchpoints.
IoT data marketplaces are created with the aim of unifying the streams of information being produced on a global scale all the time. When harnessed and structured, these data streams can yield significant insights into consumer behavior, online trends, and technological developments. IoT marketplaces are typically very accessible, with flexible pricing options like ‘pay-per-hour’, in line with the open network which this kind of data marketplace supports.
Monetizing data via an IoT data marketplace is an attractive option for many businesses. As companies increase the volume and quality of hardware in their technology stack, more and more data is being produced as a by-product. These by-products, the ‘digital twins’ we discussed earlier, are a valuable data asset. By listing IoT-sourced data, companies can tap into a simple stream of revenue by utilizing the products they already use.
Examples of IoT data marketplaces include IOTA Data Market and Streamr.
As we’ve seen, there are four core types of data marketplace, with dozens of companies operating marketplaces within each category. That’s a lot of data marketplaces. The dilemma for data providers is syncing to all of them at once. Or at least prioritising which to sync to in order to capture the most demand. The overhead that comes with integrating products to multiple data markepltaces, then managing each of these separate sales channels, is huge. Often, it’s unviable for young DaaS companies to build such an omni-channel business.
To eliminate this problem, data providers can use solutions like Data Commerce Cloud™ (DCC). DCC enables providers to synchronize with multiple data marketplaces - and other kinds of data exchange platforms, which we’ll look into below - with one single account. So providers only need to set up their storefront and list data products once, as opposed to starting the listing process from scratch for each data marketplace. It’s a SaaS solution for data commerce that solves many of the problems which Shopify solves for retail and ecommerce. Namely, managing all of your stock, leads, transactions, and orders across every different sales channel.
Let’s say you’re an online seller making homemade candles. Your business is a one-woman show. To reach the most amount of buyers globally, you’d want to be present on Amazon, Ebay, Etsy, ASOS, Zalando, vinted, and perhaps other retail outlets. It would soon get very difficult for you to process every order you receive from each of these channels, especially because the commission fees and even currency you owe will probably vary between each platform. Shopify alleviates this logistical and financial stress. You can maximize your business’ visibility with an effective omni-channel strategy, whilst managing everything centrally in your Shopify seller account.
DCC works in the same way, for data providers. With DCC, all activity from disparate marketplaces is centralized in your DCC account. This saves an incredible amount of time and resources, especially for younger DaaS players! Data commerce solutions like DCC will become more important as the number of data marketplaces increases, alongside a whole host of other data sourcing and fulfillment platforms.
What about these other platforms designed for data exchange? Let’s see how they weigh up to the data marketplace model as a solution for providers and buyers.
Welcome to Datarade’s 2024 edition of ‘what’s the difference between’. There are lots of different sites and services which aim to make buying and selling Data Capital more rewarding. Each is slightly different to a data marketplace, and each has its pros and cons:
Data streaming platforms are great for data buyers who need access to on-demand, real-time data feeds. Like data marketplaces, they work with third-party vendors to make external data commercially-available to users across the world. So they’re an amazing fresh data source, especially for businesses who need instant, live data via a flexible, easy-to-use platform.
The issue with data streaming platforms lies on the provider-side. Data streaming requires integration with the platform as a host site, which can be a lengthy and complex process for an organization whose primary business is not selling data. This means data streaming platforms popular amongst companies with the primary business of selling data, but for organizations who just want to monetize their proprietary data, the time and money cost of integrating with a data streaming site sometimes just isn’t worth it. Integration is not a prerequisite for data marketplaces: some offer it, some don’t. This gives providers the freedom to sell their data their way, using the software they already have, with minimal added time or financial cost.
Examples of data streaming platforms include Narrative.io.
The big pro of data warehousing is that it produces analytics-ready insights. In other words, the mature is mature and primed-for business usage, in comparison to raw or open-source insights which could be found in datasets elsewhere. They’re also more established than data marketplaces, meaning it’s possible that users trust data warehouses more. However, the data held in data warehouses can only answer business-specific questions, and even then, the BI they provide does not include key technical information or contextual metadata. This means that data warehouses aren’t always a great source of external data, because their Big Data capabilities are so limited.
For data marketplaces, this isn’t an issue. The range of data available from vendors on data marketplaces spans all categories of Big Data - there are over 300 different data categories on Datarade. Likewise, with blockchain advancements, data marketplaces operate just as securely as a data warehouse.
Data lakes are huge repositories of data gathered using IoT and machine signals, proprietary company data, records, and CRM databases. The trouble with data lakes is that, although the volume of data they offer is vast, it can often take so much time and effort to differentiate the valuable data from the irrelevant that it ends up costing companies time and money instead of saving. Identifying and analysing data from data lakes is also a complex task which requires experienced data analytics and science.
In comparison, data marketplaces are designed for the everyman. Businesses of all sizes and with any level of experience can use data marketplaces for rewarding and efficient data shopping experiences. And increasingly, data marketplaces are offering datasets and APIs just as granular and powerful as the information kept in a data lake, but that are actionable without having to undergo complex structuring and cleansing before they can be analyzed.
A data silo is similar to a data lake, and presents many of the same challenges. Essentially, data silos store masses of internal data for companies and organizations globally. There is potential to derive rich insights by data mining these silos. However, they’re poor data management systems because, like data lakes, it’s difficult to identify and analyze data points from a data silo. Data silos don’t provide actionable data-driven solutions, and often the data they contain is no longer fresh.
Data marketplaces offer an alternative to data silos which ensures the user gets fresh data without the need for extensive data mining. SaaS vendors may use data silos as a source for some of their insights, but before they list these data products on a data marketplace, they will carry out validation and data enrichment processes. So the APIs and datasets available on data marketplaces still offer rich insights, like you’d get from a data lake. But on top of that, data vendors strip away the irrelevant data points to offer up-to-date, analytics-ready datasets, so that the buyer gets exactly the data they need. Again, data marketplaces provide a fast and accessible solution for buyers to tap into external data and start putting it to work.
Data clouds are another solution aimed at harnessing the data stored in lakes and silos. A data cloud enables users to exchange, unify and monetize data from lakes and silos. Data clouds form a global network of SaaS providers with data to share: the result is democratized access to external data for organizations across the world. Data cloud exchanges are a much more powerful data storage solution than data warehouses, silos, or lakes, because they’re updated in real-time thanks to data feeds and streams from every data provider which is integrated. As a result, users can access valuable, fresh data on-demand via a single platform.
Like with data streaming platforms, however, data clouds can present integration challenges. As the data exchange industry develops, data clouds and streaming sites are working hard to rectify this and make it as painless as possible to add Big Data to their platforms to start monetizing it. At the moment, however, SaaS vendors and data providers are still reporting integration issues with these sites. Data marketplaces don’t require any integration. So companies of any size and SaaS experience can set up their data storefront on data marketplaces like Datarade and begin selling their data instantly and securely.
Examples of data clouds include Snowflake and AWS Data Exchange.
Let’s now look at the two sides of the data marketplace in more detail. First: sell-side. Why do data vendors sell their data via data marketplaces? To understand this, we first need to understand data monetization.
Data monetization is a way of companies increasing their revenue by selling their intangible data assets. The fastest-growing companies have adopted a data monetization strategy and are reaping the commercial benefits (McKinsey & Co.).
For the most successful data monetization initiatives, organizations first have to define the value of their in-house data. What kind of data is it? What insights does the database yield? Who would buy the data? How much would they pay for it? Once these questions have been answered by conducting competitive market research, the next step in data monetization is for companies to select the right data monetization platform.
Studies show that companies which take advantage of next-generation data marketplaces will gain a competitive digital edge (Pitney Bowes), because data marketplaces are increasingly being considered the best demand generation platform and the easiest route into data commercialization and monetization.
For companies looking to launch their data monetization strategy, data markeptlaces offer a number of unique advantages:
Data marketplaces offer unrivalled demand generation potential. They attract data buyers from all over the world, working for the most valuable global companies. For SaaS data vendors, this means the opportunity to tap into a new lead pool which includes some of the world’s largest corporates, brands, and financial institutions.
When they join a data marketplace, data providers can continue to sell their data in the way that suits them. Data marketplaces don’t require complex integration or third-party brokers, meaning data vendors can keep their data monetization strategy in place, but with the added visibility and lead generation potential which a data marketplace provides.
Data marketplaces welcome all kinds of data providers, from established SaaS companies, to startups looking to use their data as a new revenue source. As independent platforms, data marketplaces are ‘vendor agnostic’. As long as providers meet the data marketplace’s relevant security measures, can prove they have valid licenses to provide data, and that they abide by data privacy legislation including GDPR and CCPA, they can list their data.
Each marketplace has its own listing procedure. However, we can sketch out a general process which all potential data providers go through to list their data on marketplaces and start selling.
Step 1) KYC
Every data marketplace will run KYC checks on new companies requesting to be listed. This is for due diligence purposes: they need to check that the provider company is legitimate and isn't involved in any criminal activity. Especially when it comes to the external data industry, it's crucial that marketplaces vet their providers to ensure that the data is sourced in a legal, privacy-compliant manner. KYC could also include researching media coverage of the provider to identify any negative press, so as to avoid a PR mishap further down the line.
Other times, KYC is simply to ascertain whether the prospect is suitable for the data marketplace in question. For example, you could run an established data business selling datasets to university students. After performing KYC checks on your company, a B2B data marketplace wouldn't allow you to be listed. This is because you're B2C, not B2B, so your business isn't in line with the marketplace's business model and policy. For the most part, data marketplaces are fast when it comes to conducting KYC checks. The step after you're approved is signing the marketplace's commercial agreement.
Step 2) Signing the marketplace’s commercial agreement
Data marketplaces can run using several different business models. Some operate a revenue share, whereby they take a commission whenever providers close a deal successfully via the marketplace. Others offer subscriptions plans, whereby providers pay (usually monthly or annually) to appear on the marketplace. However the marketplace’s commercial model works, the main thing is that providers sign a contract agreeing to its usage and payment terms. Once this crucial step is complete, providers can start listing their data products on the marketplace.
Step 3) Listing data products
Again, the process for listing data products varies from marketplace to marketplace. What's notable about Datarade Marketplace is that there's no data integration required for providers to create listings. The listing process requires you to give each product a title, description, coverage, and attach a sample. Otherwise, there's no painful upload or syncing needed. This is more efficient and secure than having to upload your data offering to a data marketplace where it's hosted externally and needs to be replaced whenever there's a change to your dataset. The listing process for AWS, for example, is a lot lengthier because each listing requires that you sync your data to the platform anew each time.
Another solution provided by Data Commerce Cloud™ is one-time product listing. Once you've created listings once, you're able to sync them to marketplaces of your choosing instantly. This saves providers the hassle of navigating new listing processes for every new channel they join.
At a general level, that’s how to list products on a data marketplace. But who exactly is doing so? In other words, what kind of businesses and organizations will you find listed on a data marketplace? Let’s have a look at some examples of the data providers which are realizing the potential of marketplaces for data monetization in 2024.
Companies selling data are most commonly known as data providers or vendors. Companies which have made data their primary revenue stream are often called data-as-a-service (or DaaS) companies. DaaS companies have created a business from selling data “as-a-service” - that is, on-demand and tailored to clients and organizations looking to buy data.
There are thousands of data providers in the external data market. Some data providers, like Alesco Data, are businesses where the primary product and service is to provide a data sourcing solution. Others have other primary business focusses, like Nikkei, a news outlet for Japanese financial markets. Both companies use data marketplaces like Datarade to monetize their data, showcase their datasets, and reach clients around the world.
As the name suggests, big data is vast in terms of the categories comprising it. There are over 600 data categories listed on Datarade Marketplace, with more emerging constantly as technology advances and new kinds of data is generated and collected.
All the same, within data commerce, demand for data is mostly centered around seven core categories: geospatial data, commerce data, financial market data, company data, real estate data, web data, and AI & ML training data. It’s companies providing these categories of data which you’re likely to recognise as established industry players. So that’s what data marketplaces bring to the sell-side. Now let’s see what they offer data buyers.
The first question to answer here: why buy external data in the first place?
Businesses buy external data to improve their decisions. With external data enrichment, businesses can make confident decisions about marketing, sales and BI. Buying external datasets and APIs empowers these companies to build predictive models which give them the edge over competitors and increase ROI. For more detailed information about how data can streamline all kinds of decision making, discover the different use cases for external data.
Now back to our original question. How do data marketplaces facilitate data sourcing?
Data sourcing is the process of researching, sampling, testing, and finally buying data. It usually refers to external data acquisition, where companies (and sometimes individuals) source data from outside of their proprietary databases and records. Data sourcing can require a lot of time and research, because it's crucial that data buyers invest in exactly the right data for their use case, and that they acquire this data at the right price. Otherwise, from an ROI perspective, they could end up buying data which doesn't contribute to their strategies and success. Either because the insights in the dataset aren't suited to the buyer, or because the data sourcing cost too much time (and money!).
Part of the role of the data marketplace is to make data sourcing more efficient and less complicated. Traditionally, data hunting has been made difficult because it's hard for inexperienced buyers to know which providers can supply the right datasets, and because there's a often lack of transparency in the external data industry. Data marketplaces aim to rectify these pain points associated with data sourcing. They carry out vendor KYC so that buyers don't have to conduct such lengthy verification procedures for each data source. Data marketplaces also ensure that buyers can connect with a range of trusted providers at once, meaning buyers can have multiple active avenues for data procurement. This way, the chances of them finding the best data are higher.
Many data marketplaces also provide data partnerships as a data sourcing solution. These data partnerships are a kind of on-going consultancy service offered by vendors via the marketplace. The buyer begins by briefing the provider on their data acquisition requirements: what data they need, when they need it by, and how much they can pay for the data. The providers then source the data, and then structure their datasets to allow the buyer to get actionable insights without the lengthy analytics. These data sourcing solutions are part of how data marketplaces democratize access to external data: buyers don’t have to be data scientists to use data marketplaces. They’re able to invest in data sourcing and consultation service to help them find the right data according to their use case, budget, and time demands. The aim of data sourcing is that the user is able to buy a data product in the right, readable format for them and their business’ internal software systems, without the hassle of independent data hunting.
Data sourcing is on the rise. One study found that the majority of companies surveyed already source data externally, and that over half of these plan to increase how much they spend on data marketplaces over the next three years. 2 of 5 of these reported that they'll spend over $10,000 on external data marketplaces (Pitney Bowes).
Data marketplaces remove other buyer pain points associated with data sourcing. Privacy regulations and blockchain technology mean that buyers can access encrypted datasets and APIs securely. They can connect with multiple global providers at once via one platform. It’s fast, and overcomes logistical pain points such as communication with providers who are in different time zones or who use different currencies. Arranging data samples is also easy with data marketplaces thanks to features like in-platform messaging. Most importantly, the data buyer can put the data products they’ve sourced to work instantly.
What do these data products actually look like? And how does the buyer access them?
Data from a data marketplace is available to buy in different formats. Depending on when the buyer needs the data and the data management system they use, vendors can tailor the delivery format and availability to the buyer. Data exchange via a B2B data marketplace usually occurs via two delivery methods:
SaaS vendors can deliver data from endpoint to endpoint to buyers via API. APIs give users access to real-time data feeds and streams which are received by their company’s servers. Buyers who need fresh data can purchase an API subscription to ensure they receive constant communication and information from their data vendor. Most data products available from a data marketplace are available for delivery in API format.
For buyers who want to make a one-off data purchase, S3 buckets are ideal. Whereas APIs relay real-time data, S3 buckets tend to contain historical data points. Data vendors can share complete and custom datasets and databases for a fixed price via an S3 bucket. The buyer then has unlimited access and ownership of an analysis-ready dataset. These transactions are carried out efficiently thanks to data marketplaces and exchange platforms.
Of course, there are alternative options for data exchange. The beauty of data marketplaces is that vendors and buyers are free to arrange a data sourcing solution which works for both parties. Although API and S3 are amongst the most common methods, data marketplaces don’t restrict users. Data pricing, delivery and availability can be tailored to the use case and the customer’s requirements.
We’ve looked at how data is sent from the data vendor to the buyer via a B2B marketplace.
This brings us to the final part of the Ultimate Guide to Data Marketplaces 2024: the kinds of data you can buy from them.
The Big Data revolution has meant that we can now access intelligence spanning hundreds of different data categories. Start browsing Datarade’s data marketplace to discover data from over 600 categories, sourced from a network of over 2000 providers.
Wondering whether your company is a fit to sell via data marketplaces? Whether your business is specifically a SaaS company, or you have in-house data to commercialize, discover how you can list your data on Datarade’s data marketplace, as well as Databricks Marketplace, SAP Datasphere Marketplace, and Google Cloud Analytics Hub, with one DCC account. Learn more →
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