The future is looking big for data marketplaces. The global data marketplace platform market size is anticipated to reach USD 5.09 billion by 2030 (Research and Markets). But as more marketplaces enter the space, the tougher the competition, and companies are finding it increasingly difficult to run a successful data marketplace which can withstand these overcrowded conditions. TL;DR: companies operating data marketplaces must be more opportunistic, scrappy, and, above all, solution-orientated to build a business that really works.
We’ll take a look into the state of the data marketplace today, the different kinds of data marketplaces you’ll encounter in the industry, and some possible trajectories for data marketplaces - and their various users - in the future.
Let’s begin with the most prevalent kind of data marketplace, and their core user group: public data marketplaces, and the commercial data providers which sell via them.
By now, the provider-facing benefits of public data marketplaces are common knowledge to most in the data industry. Firstly, they’re one of the best demand generation platforms for data providers. Public data marketplaces are mostly industry-agnostic, so the total addressable market (TAM) of buyers using a public data marketplace is huge. Companies large and small, from cybersecurity to consultancy firms, are using public data marketplaces for data sourcing. This gives data-as-a-service (DaaS) companies a huge pool of potential buyer leads spanning many verticals.
Secondly, data integration is not a pre-requisite for selling via a public data marketplace. Many, like Datarade Marketplace, enable providers to list data products which market their offering without having to sync their data to the marketplace. They need only upload a sample preview. This way, providers can collect inbound leads with no delay or painful integration. In this sense, public data marketplaces work just like B2C marketplaces. A retailer selling shoes can create listings for their various product to be published on the marketplace, but the stock itself resides with the provider. They retain full control over their product and there’s no complication associated with getting the product to the marketplace. When the listing is ‘purchased’, the provider is responsible for fulfilment. The same is true for data providers, who are able to deliver the data to the buyer without third-party interference, via the method which works best for them.
This brings use to a final important value proposition of the public data marketplace. It is a secure means of data exchange. Either public data marketplaces don’t host providers’ data, just their listings, so there’s no risk of a security breach. Or they’re integrated with blockchain technology to ensure that all data streamed over the platform is encrypted. Nokia Data Marketplace is a good example of a blockchain marketplace. These data marketplaces are referred to as ‘decentralized data marketplaces’. They’re powered by the blockchain so as to project the anonymity of users, making buying and selling data more safe for both parties.
Which brings us to an obvious but crucial consideration. Data marketplaces are two-sided markets, where commodities are exchanged. We’ve looked at the supply-side beneficiaries of data marketplaces; now let’s look at the demand-side.
A key factor driving the data marketplace’s ease of use is automation. Again, the B2B data marketplace is following the B2C marketplace’s lead and applying familiar ecommerce concepts to buying data. To make the data procurement as simple as possible, public data marketplaces are standardizing data products and moving even further away from the data brokerage model, instead favouring a buyer experience which is automated and fast. For example, buyers can purchase datasets instantly from public data marketplaces like Datarade Marketplace using a normal credit card. This is thanks to data marketplaces working with payment processors like Stripe.
It’s not just data purchasing which public data marketplaces are getting better at facilitating. Data sourcing has also become a more automated process on the public data marketplace. The likes of Nomad’s Connect allows buyers to describe their data use case, which the marketplace’s algorithms analyze in order to automatically send the request to providers which might fit the bill. This drastically reduces the time spent in data discovery and comparison stages, and the matches will only get more relevant as the algorithm improves.
And when it comes to use cases for data, none is receiving more attention at the moment than artificial intelligence. Countless articles have been written about the importance of external data for training generative AI models. In response to this, public data marketplaces are developing with this specific use case in mind. Databricks, for example, is directing its open data marketplace squarely towards buyers’ AI, ML and analytics use cases. Databricks allows buyers to access data, ML models, notebooks, applications and dashboards in clicks thanks to its Delta Sharing software. We’ll look into how AI is hastening the evolution of the data marketplace in more detail further on.
A final development we’re seeing occur with public data marketplaces is an increase in the free data products they offer. For example, Snowflake Marketplace offers free data products sourced from the IMF, Knoema and CARTO. A great consequence of data democratization, public data marketplaces are promoting data for good by welcoming free datasets. Open access to external data is fuelling research and innovation both within and outside of the private sector. For example, health authorities and health startups alike could use the dataset ‘COVID-19 Epidemiological Data’ for free from Snowflake to inform policy and improve medical treatment. Forward-thinking data marketplace companies have recognised the public sentiment shift towards data for good and attached their brand to it by supporting free datasets. In the long run, these marketplaces enjoy a positive public perception because they’re helping drive social and scientific development by making crucial data accessible.
Contemporaneously to this, however, we’re seeing more businesses build their own private data marketplace, thanks to the SaaS companies making this build possible.
A private data marketplace is owned and operated by the data provider, who controls access permissions and parties granted access to their data. It’s a model that’s expected to grow in popularity in the future, as new software is making it easier to build a private data marketplace. For example, Harbr has helped CoreLogic and Moody’s Analytics launch their own private data marketplace and populate them with products.
Data providers have many reasons to start their own private data markepltace. For one thing, the marketplace is proprietary, meaning the provider can obtain deeper analytics into user behavior, demand fluctuations, popular categories, and purchasing habits than from a public data marketplace.
Also, a private data marketplace is typically catered towards a specific industry or niche use case. Although this limits the audience so it’s far smaller than the TAM of a public data marketplace, this specificity works for some data providers. For example, certain financial data providers don’t offer data for which there’s mainstream demand. It makes more sense for such providers to run a private data marketplace, accessible only to investment and accountancy firms whose need for this data is sustained and dependable.
In future, as more companies establish themselves as successful data-as-a-service providers, we’ll see providers launch their own data marketplaces, stocking their own products and those of strategic partners. We saw this with CoreLogic, a real estate provider. More DaaS companies, providing data from geospatial to pharma, are likely to follow suit and build private data marketplaces for greater control over the end-to-end commercialization of their data.
Alongside an increase in the number of private data marketplaces, we’re also seeing more of a newer still kind of data marketplace: the white label data marketplace. A white label data marketplace is platform which a company builds using an existing marketplace architecture, owned by another entity. The company then pays to brand the marketplace as their own - it’s ‘white label’ in the sense that it’s a blank canvas for customization. For the company buying the rights to use the marketplace, the agreement makes sense because it enables them to fulfil their clients’ need for data. For one thing, this creates on-platform stickiness: these clients no longer need to use other sources to get their external data. It’s readily available in the software they’re already using. So there’s more incentive for clients to stick to one platform that does it all.
Secondly, a white label marketplace provides businesses with a fresh branding opportunity, without having to build the marketplace themselves. Usually, it’s large enterprises who start white label marketplaces. It allows them to develop the ‘tech’ arm of their business and brand with relatively little engineering effort. You can get a white label marketplace going in as little as weeks.
Lastly, the ROI of a white label data marketplace can be huge. Once the initial investment has been made and the marketplace is up and running, the company can begin charging their existing clients for using data from the marketplace. With this comes a net-new revenue stream with low customer acquisition costs (CAC), because the customers have already been acquired and are using the company.
Arguably, it’s a bit of a misnomer to call white label data marketplaces ‘new’; they’re the same marketplaces that have been around for years which have just undergone a rebrand after changing hands. There are other, totally newly-conceived data marketplaces appearing more regularly than we could’ve anticipated years ago.
One of these new data marketplaces is being built by data industry veteran, Snowflake, which is building a retail media data marketplace - in addition to its existing public data marketplace, which is data category generic. Another new markepltace, Mobito, specializes in mobility data. There are still more generic data marketplaces throwing their hat into the ring: New York-based Taktile recently launched its data library which is suitable for widespread use.
The proliferation of data marketplaces is just one indicator of an entire industry entering hyper-growth. There’s more in store for the future of the data industry than could be covered in this guide alone.
The data industry is experiencing rapid growth, which has been catalysed by new market challenges, software innovation and the advent of AI and analytics. The data marketplace provides one of the best case studies for examining the evolution of the industry, and how it’s been affected by wider technological progress.
The data use case of the age has prompted many smart data marketplace companies to expand their product to facilitate said use case. Yes, we’re again talking about artificial intelligence.
AI training and Machine Learning are fuelled by external datasets. Both require quality data, lots of it, in a format that teams can use collaboratively. For this reason, companies whose central product is a data marketplace are expanding the capabilities of the marketplace platform to include solutions for ensuring data quality, annotation, and sharing. These are some of the steps towards data governance which data experts are citing as essential for AI use cases (West Monroe).
Traditionally, data marketplaces focus solely on data monetization and acquisition. But AI has put greater emphasis on what’s happening with the data pre- and post-purchase. Namely, there’s now the question of whether the data is high quality enough to train an AI model. And after that, how easily data scientists, developers and product managers can collaborate to build an AI program using the data. For example, Narrative’s flagship product is its Data Streams Marketplace. However, they’re adding a new product, Rosetta, which standardizes data so that it can be put to cross-departmental use.
What’s certain is that AI will continue to direct product development at data marketplaces. Exactly what the future holds for new marketplace features, products and capabilities which arise as a result of AI remains to be seen.
The data marketplaces we’ve mentioned in this article barely scratches the surface when it comes to the total number of marketplaces out there. Shopify saw that the range of ecommerce sales channels available to online retailers was bewildering. And that to list products on each marketplace was painful and time-consuming. With this mass of marketplaces comes a need for new software which unifies the chaos, just as Shopify did for B2C commerce: the data commerce platform. Data Commerce Cloud™ (DCC) enables data providers to sync products to multiple marketplaces at once. In other words, data providers will be able to generate leads from each kind of data marketplace, via a platform which makes running an omni-channel business easy.
The future of many data marketplaces will include collaboration with data commerce platforms like DCC. Already, public data marketplaces including Google Cloud Analytics Hub, Alation Marketplaces, and SAP Datasphere are integrated with DCC to expand the supply of data products available on their marketplace and provide more value to their customers. As we’ve seen, the competition for data marketplace businesses is only increasing. Those which can stand out in terms of provider and product supply have a better chance of success.
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